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We introduce Vibe Code Bench, a benchmark of 100 web application specifications (50 public validation, 50 held-out test) with 964 browser-based workflows comprising 10,131 substeps, evaluated against deployed applications by an autonomous browser agent. Across 16 frontier models, the best achieves 61.8% accuracy on the test split, revealing that reliable end-to-end application development remains a frontier challenge. We identify self-testing during generation as a strong performance predictor (Pearson r=0.72), and show through a completed human alignment study that evaluator selection materially affects outcomes (31.8-93.6% pairwise step-level agreement). 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We release VERO to support research on agent optimization as a core capability for coding agents.\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-05T04:00:00.000Z\",\n      \"fetched_at\": \"2026-05-05T16:07:19.561Z\"\n    },\n    {\n      \"id\": \"eca28fb9520b7bc5219c79a2b1c41f91aa70215f176618b04ace17002b905807\",\n      \"url\": \"https://github.com/Hmbown/DeepSeek-TUI\",\n      \"source_url\": \"https://github.com/trending?since=daily\",\n      \"title\": \"Hmbown/DeepSeek-TUI\",\n      \"author\": [\n        \"Hmbown\"\n      ],\n      \"summary\": \"Coding agent for DeepSeek models that runs in your terminal\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-03T16:02:07.815Z\",\n      \"fetched_at\": \"2026-05-03T16:02:12.686Z\"\n    },\n    {\n      \"id\": \"abd69fd3ad92b4e9eda139be70d4ccbd50841d534ac144cd8f105b75c5e319b2\",\n      \"url\": \"https://simonwillison.net/2026/May/6/vibe-coding-and-agentic-engineering/\",\n      \"source_url\": \"https://news.ycombinator.com/rss\",\n      \"title\": \"Vibe coding and agentic engineering are getting closer than I'd like\",\n      \"author\": null,\n      \"summary\": \"Comments\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-06T15:06:37.000Z\",\n      \"fetched_at\": \"2026-05-06T16:00:17.344Z\"\n    },\n    {\n      \"id\": \"cfe6b1639c3c3094f26f16ce33a12201e1de8303ca0f8e068c4b18b2c6ec2483\",\n      \"url\": \"https://github.com/mksglu/context-mode\",\n      \"source_url\": \"https://github.com/trending?since=daily\",\n      \"title\": \"context-mode\",\n      \"author\": [\n        \"mksglu\"\n      ],\n      \"summary\": \"Context window optimization for AI coding agents. 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Unlike existing approaches that either build intricate workflows around a single large model or only provide workflow modularity, our agent integrates tool-based and pure vision agents within a highly modular architecture, enabling different models to collaboratively solve decoupled tasks in a step-by-step manner. Our generality is demonstrated by our ability to evaluate not only pure vision-based real-world benchmarks (i.e., OSWorld), but also more general or tool-intensive benchmarks (e.g., GAIA and SWE-Bench). Specifically, we achieve $\\\\mathbf{7.27\\\\%}$ accuracy on OSWorld, higher than Claude-Computer-Use. Codes and evaluation scripts are open-sourced at https://github.com/bin123apple/InfantAgent.\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-04T04:00:00.000Z\",\n      \"fetched_at\": \"2026-05-04T16:30:11.773Z\"\n    },\n    {\n      \"id\": \"27935e41cff7ee0a1918ba7203e315460bb645e9cc9fc1b83d12bee13ba5d6b9\",\n      \"url\": \"https://trendshift.io/repositories/14244\",\n      \"source_url\": \"https://trendshift.io/github-trending-repositories?trending-range=1\",\n      \"title\": \"D4Vinci/Scrapling\",\n      \"author\": null,\n      \"summary\": \"🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-06T10:01:51.022Z\",\n      \"fetched_at\": \"2026-05-06T10:01:53.787Z\"\n    },\n    {\n      \"id\": \"85b011b86cb83f163738ec478e5454dfff5d77fc293b4e0924ef2998b6a0c91d\",\n      \"url\": \"https://news.ycombinator.com/item?id=48023496\",\n      \"source_url\": \"https://news.ycombinator.com/rss\",\n      \"title\": \"Show HN: Airbyte Agents – context for agents across multiple data sources\",\n      \"author\": null,\n      \"summary\": \"Comments\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-05T15:03:18.000Z\",\n      \"fetched_at\": \"2026-05-05T22:00:21.144Z\"\n    },\n    {\n      \"id\": \"9fb6d64d7116a6134eeccd988f62f9fe60959d5cefbd14f10eb9ea468560c703\",\n      \"url\": \"https://addyosmani.com/blog/agent-skills/\",\n      \"source_url\": \"https://news.ycombinator.com/rss\",\n      \"title\": \"Agent Skills\",\n      \"author\": null,\n      \"summary\": \"Comments\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-04T21:40:42.000Z\",\n      \"fetched_at\": \"2026-05-05T10:00:19.258Z\"\n    },\n    {\n      \"id\": \"0b6244279772ea4cdafe81f7fa434f97b05b8e2f23f0fbf53239a9068505d90e\",\n      \"url\": \"https://github.com/msitarzewski/agency-agents\",\n      \"source_url\": \"https://github.com/trending?since=daily\",\n      \"title\": \"agency-agents\",\n      \"author\": [\n        \"msitarzewski\"\n      ],\n      \"summary\": \"A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-04T16:03:21.905Z\",\n      \"fetched_at\": \"2026-05-04T16:03:26.549Z\"\n    },\n    {\n      \"id\": \"oai:arXiv.org:2605.02592v1\",\n      \"url\": \"https://arxiv.org/abs/2605.02592\",\n      \"source_url\": \"https://rss.arxiv.org/rss/cs.ai\",\n      \"title\": \"Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges\",\n      \"author\": [\n        \"Vincent Henkel\",\n        \"Felix Gehlhoff\",\n        \"David Kube\",\n        \"Asaad Almutareb\",\n        \"Luis Cruz\",\n        \"Bernd Hellingrath\",\n        \"Philip Koch\",\n        \"Christoph Legat\",\n        \"Florian Mohr\",\n        \"Michael Oberle\",\n        \"Felix Ocker\",\n        \"Thorsten Schoeler\",\n        \"Mario Thron\",\n        \"Nico Andre T\\\\\\\"opfer\",\n        \"Lucas Vogt\",\n        \"Yuchen Xia\"\n      ],\n      \"summary\": \"arXiv:2605.02592v1 Announce Type: new Abstract: Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and engineering automation. Yet evidence on their purposes, capabilities, and limitations remains fragmented across domains. This work examines how mature foundation-model-based agent systems are in industrial contexts, how their functional profile differs from conventional agent systems, and which limitations persist. A systematic literature survey following the PRISMA 2020 guideline is presented, screening 2,341 publications and synthesising a corpus of 88 publications through a structured coding scheme. The results show that reported systems are predominantly at prototype and early validation stages (75.0% at TRL 4-6), with deployment-oriented evidence remaining rare (9.1%). Operational goals are most frequently positioned in user assistance, monitoring, and process optimisation, while conventional production-control purposes such as planning and scheduling are less prominent. Compared with an established baseline for industrial agent systems, the capability profile reveals substantial gains in human interaction (+37%) and dealing with uncertainty (+35%), but a pronounced deficit in negotiation (-39%). The most widely reported limitations concern lack of generalization, hallucination and output instability, data scarcity, and inference latency. A working definition of foundation-model-based industrial agents is also proposed, bridging conventional agent theory, automation-engineering standards, and the foundation-model paradigm.\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-05T04:00:00.000Z\",\n      \"fetched_at\": \"2026-05-05T16:07:19.561Z\"\n    },\n    {\n      \"id\": \"oai:arXiv.org:2605.02964v1\",\n      \"url\": \"https://arxiv.org/abs/2605.02964\",\n      \"source_url\": \"https://rss.arxiv.org/rss/cs.lg\",\n      \"title\": \"Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use\",\n      \"author\": [\n        \"Kunvar Thaman\"\n      ],\n      \"summary\": \"arXiv:2605.02964v1 Announce Type: new Abstract: Reinforcement learning (RL) trained language model agents with tool access are increasingly deployed in coding assistants, research tools, and autonomous systems. We introduce the Reward Hacking Benchmark (RHB), a suite of multi-step tasks requiring sequential tool operations with naturalistic shortcut opportunities such as skipping verification steps, inferring answers from task-adjacent metadata, or tampering with evaluation-relevant functions. RHB supports independent and chained task regimes, where chain length acts as a proxy for longer-horizon agent behavior. We evaluate 13 frontier models from OpenAI, Anthropic, Google, and DeepSeek. Exploit rates range from 0% (Claude Sonnet 4.5) to 13.9% (DeepSeek-R1-Zero), varying sharply by post-training style. A controlled sibling comparison (DeepSeek-V3 vs. DeepSeek-R1-Zero) shows RL post-training is associated with substantially higher reward hacking (0.6% vs. 13.9%), with consistent gaps across all four task families. We identify six exploit categories and find that 72% of reward hacking episodes include explicit chain-of-thought rationale, suggesting models often frame exploits as legitimate problem-solving. Simple environmental hardening reduces exploit rates by 5.7 percentage points (87.7% relative) without degrading task success. Models with near-zero exploit rates on standard tasks show elevated rates on harder variants, suggesting that production-aligned post-training appears to suppress reward hacking only below a complexity threshold where honest solutions remain tractable.\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-06T04:00:00.000Z\",\n      \"fetched_at\": \"2026-05-06T16:00:29.193Z\"\n    },\n    {\n      \"id\": \"f3588de3466656d5caf1e72a8b7be1cfe61169172ef4ec3740101869cb09f91b\",\n      \"url\": \"https://letsdatascience.com/news/telus-uses-ai-to-alter-call-agent-accents-a3868f63\",\n      \"source_url\": \"https://news.ycombinator.com/rss\",\n      \"title\": \"Telus Uses AI to Alter Call-Agent Accents\",\n      \"author\": null,\n      \"summary\": \"Comments\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-06T01:38:44.000Z\",\n      \"fetched_at\": \"2026-05-06T04:00:24.872Z\"\n    },\n    {\n      \"id\": \"oai:arXiv.org:2605.01102v1\",\n      \"url\": \"https://arxiv.org/abs/2605.01102\",\n      \"source_url\": \"https://rss.arxiv.org/rss/cs.ai\",\n      \"title\": \"Towards Multi-Agent Autonomous Reasoning in Hydrodynamics\",\n      \"author\": [\n        \"Jinpai Zhao\",\n        \"Albert Cerrone\",\n        \"Joannes Westerink\",\n        \"Clint Dawson\"\n      ],\n      \"summary\": \"arXiv:2605.01102v1 Announce Type: new Abstract: Single-agent systems (SAS) have become the default pattern for LLM-driven scientific workflows, but routing planning, tool use, and synthesis through a single context window comes with a well-known cost: as tool specifications and observational traces accumulate, the effective context available for each decision shrinks, and end-to-end reliability suffers. We present a multi-agent system (MAS) prototype for hydrodynamics in which specialized agents are coordinated through a Layer Execution Graph (LEG). A planner agent constructs query-specific execution topologies from natural-language routing heuristics that capture domain knowledge without hard-coding it as rigid control logic; specialist agents operate under strict tool allowlists and occupy complementary data-class roles. Between layers, consolidator agents fuse parallel outputs into concise briefs, and a reporter agent synthesizes the final response, while the runtime logs provenance for every tool invocation to support auditability. All benchmarks, ablations, and stress tests use Claude Sonnet~4.6 as the backbone model for both specialist and general-purpose agents. Evaluated on 37 queries spanning six complexity categories, the prototype achieves 93.6% factual precision with a 100% pass rate. Accuracy remains above 90% across runs from single-threaded to five independent parallel tracks, and under simulated loss of individual data sources the system degrades gracefully, still returning substantive partial answers. Together, these results suggest that planner-guided, graph-structured multi-agent orchestration can meaningfully alleviate the context-saturation bottlenecks that constrain monolithic single-agent architectures.\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-05T04:00:00.000Z\",\n      \"fetched_at\": \"2026-05-05T16:07:19.561Z\"\n    },\n    {\n      \"id\": \"904bf452d767ff12fca5ad00fd9896d3d895302b47b36efad808d273fc0f7104\",\n      \"url\": \"https://blog.cloudflare.com/agents-stripe-projects/\",\n      \"source_url\": \"https://news.ycombinator.com/rss\",\n      \"title\": \"Agents can now create Cloudflare accounts, buy domains, and deploy\",\n      \"author\": null,\n      \"summary\": \"Comments\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-06T03:10:33.000Z\",\n      \"fetched_at\": \"2026-05-06T04:00:24.872Z\"\n    },\n    {\n      \"id\": \"97ba3b315340547e1f354043ad5175a8d7390a0c563330ff1390d28b03d480f5\",\n      \"url\": \"https://draxinar.github.io/articles/2026-05-01-uodemo-reverse-engineering.html\",\n      \"source_url\": \"https://news.ycombinator.com/rss\",\n      \"title\": \"Reverse-engineering the 1998 Ultima Online demo server\",\n      \"author\": null,\n      \"summary\": \"Comments\",\n      \"image_url\": null,\n      \"cover_img\": null,\n      \"pub_date\": \"2026-05-06T06:31:29.000Z\",\n      \"fetched_at\": \"2026-05-06T10:01:02.760Z\"\n    }\n  ],\n  \"total\": 3111\n}"
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        "title": "Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development",
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        "summary": "arXiv:2603.04601v2 Announce Type: replace-cross Abstract: Code generation has emerged as one of AI's highest-impact use cases, yet existing benchmarks measure isolated tasks rather than the complete \"zero-to-one\" process of building a working application from scratch. We introduce Vibe Code Bench, a benchmark of 100 web application specifications (50 public validation, 50 held-out test) with 964 browser-based workflows comprising 10,131 substeps, evaluated against deployed applications by an autonomous browser agent. Across 16 frontier models, the best achieves 61.8% accuracy on the test split, revealing that reliable end-to-end application development remains a frontier challenge. We identify self-testing during generation as a strong performance predictor (Pearson r=0.72), and show through a completed human alignment study that evaluator selection materially affects outcomes (31.8-93.6% pairwise step-level agreement). Our contributions include (1) a novel benchmark dataset and browser-based evaluation pipeline for end-to-end web application development, (2) a comprehensive evaluation of 16 frontier models with cost, latency, and error analysis, and (3) an evaluator alignment protocol with both cross-model and human annotation results.",
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        ],
        "summary": "Coding agent for DeepSeek models that runs in your terminal",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-03T16:02:07.815Z",
        "fetched_at": "2026-05-03T16:02:12.686Z"
      },
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        "id": "abd69fd3ad92b4e9eda139be70d4ccbd50841d534ac144cd8f105b75c5e319b2",
        "url": "https://simonwillison.net/2026/May/6/vibe-coding-and-agentic-engineering/",
        "source_url": "https://news.ycombinator.com/rss",
        "title": "Vibe coding and agentic engineering are getting closer than I'd like",
        "author": null,
        "summary": "Comments",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-06T15:06:37.000Z",
        "fetched_at": "2026-05-06T16:00:17.344Z"
      },
      {
        "id": "cfe6b1639c3c3094f26f16ce33a12201e1de8303ca0f8e068c4b18b2c6ec2483",
        "url": "https://github.com/mksglu/context-mode",
        "source_url": "https://github.com/trending?since=daily",
        "title": "context-mode",
        "author": [
          "mksglu"
        ],
        "summary": "Context window optimization for AI coding agents. Sandboxes tool output, 98% reduction. 14 platforms",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-05T16:13:38.527Z",
        "fetched_at": "2026-05-05T16:13:43.323Z"
      },
      {
        "id": "ddbbf286c80600ec67750d4ede2d9c29acb46b36f589aa4470a6eaa263811a68",
        "url": "https://trendshift.io/repositories/24012",
        "source_url": "https://trendshift.io/github-trending-repositories?trending-range=1",
        "title": "mksglu/context-mode",
        "author": null,
        "summary": "Context window optimization for AI coding agents. Sandboxes tool output, 98% reduction. 14 platforms",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-05T22:00:55.575Z",
        "fetched_at": "2026-05-05T22:00:55.988Z"
      },
      {
        "id": "oai:arXiv.org:2505.10887v3",
        "url": "https://arxiv.org/abs/2505.10887",
        "source_url": "https://rss.arxiv.org/rss/cs.ai",
        "title": "InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer Interaction",
        "author": [
          "Bin Lei",
          "Weitai Kang",
          "Zijian Zhang",
          "Winson Chen",
          "Xi Xie",
          "Shan Zuo",
          "Mimi Xie",
          "Ali Payani",
          "Mingyi Hong",
          "Yan Yan",
          "Caiwen Ding"
        ],
        "summary": "arXiv:2505.10887v3 Announce Type: replace Abstract: This paper introduces \\textsc{InfantAgent-Next}, a generalist agent capable of interacting with computers in a multimodal manner, encompassing text, images, audio, and video. Unlike existing approaches that either build intricate workflows around a single large model or only provide workflow modularity, our agent integrates tool-based and pure vision agents within a highly modular architecture, enabling different models to collaboratively solve decoupled tasks in a step-by-step manner. Our generality is demonstrated by our ability to evaluate not only pure vision-based real-world benchmarks (i.e., OSWorld), but also more general or tool-intensive benchmarks (e.g., GAIA and SWE-Bench). Specifically, we achieve $\\mathbf{7.27\\%}$ accuracy on OSWorld, higher than Claude-Computer-Use. Codes and evaluation scripts are open-sourced at https://github.com/bin123apple/InfantAgent.",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-04T04:00:00.000Z",
        "fetched_at": "2026-05-04T16:30:11.773Z"
      },
      {
        "id": "27935e41cff7ee0a1918ba7203e315460bb645e9cc9fc1b83d12bee13ba5d6b9",
        "url": "https://trendshift.io/repositories/14244",
        "source_url": "https://trendshift.io/github-trending-repositories?trending-range=1",
        "title": "D4Vinci/Scrapling",
        "author": null,
        "summary": "🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-06T10:01:51.022Z",
        "fetched_at": "2026-05-06T10:01:53.787Z"
      },
      {
        "id": "85b011b86cb83f163738ec478e5454dfff5d77fc293b4e0924ef2998b6a0c91d",
        "url": "https://news.ycombinator.com/item?id=48023496",
        "source_url": "https://news.ycombinator.com/rss",
        "title": "Show HN: Airbyte Agents – context for agents across multiple data sources",
        "author": null,
        "summary": "Comments",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-05T15:03:18.000Z",
        "fetched_at": "2026-05-05T22:00:21.144Z"
      },
      {
        "id": "9fb6d64d7116a6134eeccd988f62f9fe60959d5cefbd14f10eb9ea468560c703",
        "url": "https://addyosmani.com/blog/agent-skills/",
        "source_url": "https://news.ycombinator.com/rss",
        "title": "Agent Skills",
        "author": null,
        "summary": "Comments",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-04T21:40:42.000Z",
        "fetched_at": "2026-05-05T10:00:19.258Z"
      },
      {
        "id": "0b6244279772ea4cdafe81f7fa434f97b05b8e2f23f0fbf53239a9068505d90e",
        "url": "https://github.com/msitarzewski/agency-agents",
        "source_url": "https://github.com/trending?since=daily",
        "title": "agency-agents",
        "author": [
          "msitarzewski"
        ],
        "summary": "A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-04T16:03:21.905Z",
        "fetched_at": "2026-05-04T16:03:26.549Z"
      },
      {
        "id": "oai:arXiv.org:2605.02592v1",
        "url": "https://arxiv.org/abs/2605.02592",
        "source_url": "https://rss.arxiv.org/rss/cs.ai",
        "title": "Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges",
        "author": [
          "Vincent Henkel",
          "Felix Gehlhoff",
          "David Kube",
          "Asaad Almutareb",
          "Luis Cruz",
          "Bernd Hellingrath",
          "Philip Koch",
          "Christoph Legat",
          "Florian Mohr",
          "Michael Oberle",
          "Felix Ocker",
          "Thorsten Schoeler",
          "Mario Thron",
          "Nico Andre T\\\"opfer",
          "Lucas Vogt",
          "Yuchen Xia"
        ],
        "summary": "arXiv:2605.02592v1 Announce Type: new Abstract: Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and engineering automation. Yet evidence on their purposes, capabilities, and limitations remains fragmented across domains. This work examines how mature foundation-model-based agent systems are in industrial contexts, how their functional profile differs from conventional agent systems, and which limitations persist. A systematic literature survey following the PRISMA 2020 guideline is presented, screening 2,341 publications and synthesising a corpus of 88 publications through a structured coding scheme. The results show that reported systems are predominantly at prototype and early validation stages (75.0% at TRL 4-6), with deployment-oriented evidence remaining rare (9.1%). Operational goals are most frequently positioned in user assistance, monitoring, and process optimisation, while conventional production-control purposes such as planning and scheduling are less prominent. Compared with an established baseline for industrial agent systems, the capability profile reveals substantial gains in human interaction (+37%) and dealing with uncertainty (+35%), but a pronounced deficit in negotiation (-39%). The most widely reported limitations concern lack of generalization, hallucination and output instability, data scarcity, and inference latency. A working definition of foundation-model-based industrial agents is also proposed, bridging conventional agent theory, automation-engineering standards, and the foundation-model paradigm.",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-05T04:00:00.000Z",
        "fetched_at": "2026-05-05T16:07:19.561Z"
      },
      {
        "id": "oai:arXiv.org:2605.02964v1",
        "url": "https://arxiv.org/abs/2605.02964",
        "source_url": "https://rss.arxiv.org/rss/cs.lg",
        "title": "Reward Hacking Benchmark: Measuring Exploits in LLM Agents with Tool Use",
        "author": [
          "Kunvar Thaman"
        ],
        "summary": "arXiv:2605.02964v1 Announce Type: new Abstract: Reinforcement learning (RL) trained language model agents with tool access are increasingly deployed in coding assistants, research tools, and autonomous systems. We introduce the Reward Hacking Benchmark (RHB), a suite of multi-step tasks requiring sequential tool operations with naturalistic shortcut opportunities such as skipping verification steps, inferring answers from task-adjacent metadata, or tampering with evaluation-relevant functions. RHB supports independent and chained task regimes, where chain length acts as a proxy for longer-horizon agent behavior. We evaluate 13 frontier models from OpenAI, Anthropic, Google, and DeepSeek. Exploit rates range from 0% (Claude Sonnet 4.5) to 13.9% (DeepSeek-R1-Zero), varying sharply by post-training style. A controlled sibling comparison (DeepSeek-V3 vs. DeepSeek-R1-Zero) shows RL post-training is associated with substantially higher reward hacking (0.6% vs. 13.9%), with consistent gaps across all four task families. We identify six exploit categories and find that 72% of reward hacking episodes include explicit chain-of-thought rationale, suggesting models often frame exploits as legitimate problem-solving. Simple environmental hardening reduces exploit rates by 5.7 percentage points (87.7% relative) without degrading task success. Models with near-zero exploit rates on standard tasks show elevated rates on harder variants, suggesting that production-aligned post-training appears to suppress reward hacking only below a complexity threshold where honest solutions remain tractable.",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-06T04:00:00.000Z",
        "fetched_at": "2026-05-06T16:00:29.193Z"
      },
      {
        "id": "f3588de3466656d5caf1e72a8b7be1cfe61169172ef4ec3740101869cb09f91b",
        "url": "https://letsdatascience.com/news/telus-uses-ai-to-alter-call-agent-accents-a3868f63",
        "source_url": "https://news.ycombinator.com/rss",
        "title": "Telus Uses AI to Alter Call-Agent Accents",
        "author": null,
        "summary": "Comments",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-06T01:38:44.000Z",
        "fetched_at": "2026-05-06T04:00:24.872Z"
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      {
        "id": "oai:arXiv.org:2605.01102v1",
        "url": "https://arxiv.org/abs/2605.01102",
        "source_url": "https://rss.arxiv.org/rss/cs.ai",
        "title": "Towards Multi-Agent Autonomous Reasoning in Hydrodynamics",
        "author": [
          "Jinpai Zhao",
          "Albert Cerrone",
          "Joannes Westerink",
          "Clint Dawson"
        ],
        "summary": "arXiv:2605.01102v1 Announce Type: new Abstract: Single-agent systems (SAS) have become the default pattern for LLM-driven scientific workflows, but routing planning, tool use, and synthesis through a single context window comes with a well-known cost: as tool specifications and observational traces accumulate, the effective context available for each decision shrinks, and end-to-end reliability suffers. We present a multi-agent system (MAS) prototype for hydrodynamics in which specialized agents are coordinated through a Layer Execution Graph (LEG). A planner agent constructs query-specific execution topologies from natural-language routing heuristics that capture domain knowledge without hard-coding it as rigid control logic; specialist agents operate under strict tool allowlists and occupy complementary data-class roles. Between layers, consolidator agents fuse parallel outputs into concise briefs, and a reporter agent synthesizes the final response, while the runtime logs provenance for every tool invocation to support auditability. All benchmarks, ablations, and stress tests use Claude Sonnet~4.6 as the backbone model for both specialist and general-purpose agents. Evaluated on 37 queries spanning six complexity categories, the prototype achieves 93.6% factual precision with a 100% pass rate. Accuracy remains above 90% across runs from single-threaded to five independent parallel tracks, and under simulated loss of individual data sources the system degrades gracefully, still returning substantive partial answers. Together, these results suggest that planner-guided, graph-structured multi-agent orchestration can meaningfully alleviate the context-saturation bottlenecks that constrain monolithic single-agent architectures.",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-05T04:00:00.000Z",
        "fetched_at": "2026-05-05T16:07:19.561Z"
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        "id": "904bf452d767ff12fca5ad00fd9896d3d895302b47b36efad808d273fc0f7104",
        "url": "https://blog.cloudflare.com/agents-stripe-projects/",
        "source_url": "https://news.ycombinator.com/rss",
        "title": "Agents can now create Cloudflare accounts, buy domains, and deploy",
        "author": null,
        "summary": "Comments",
        "image_url": null,
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        "pub_date": "2026-05-06T03:10:33.000Z",
        "fetched_at": "2026-05-06T04:00:24.872Z"
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        "id": "97ba3b315340547e1f354043ad5175a8d7390a0c563330ff1390d28b03d480f5",
        "url": "https://draxinar.github.io/articles/2026-05-01-uodemo-reverse-engineering.html",
        "source_url": "https://news.ycombinator.com/rss",
        "title": "Reverse-engineering the 1998 Ultima Online demo server",
        "author": null,
        "summary": "Comments",
        "image_url": null,
        "cover_img": null,
        "pub_date": "2026-05-06T06:31:29.000Z",
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{
  "content": [
    {
      "type": "text",
      "text": "{\n  \"results\": [\n    {\n      \"title\": \"Claude Browser Agent 2025: The AI That Automates Everything\",\n      \"url\": \"https://www.reddit.com/r/AISEOInsider/comments/1pxl3r1/claude_browser_agent_2025_the_ai_that_automates/\",\n      \"description\": \"Claude's Browser Agent is Anthropic's new computer-use feature. It ... MiniMax M2.1 Coding Agent: The Open-Source AI That Just Beat Claude and\"\n    },\n    {\n      \"title\": \"The State of AI Browser Agents in 2025 | FillApp Blog\",\n      \"url\": \"https://fillapp.ai/blog/the-state-of-ai-browser-agents-2025\",\n      \"description\": \"> **Update (October 28, 2025):** This article has been updated to reflect major developments in the AI browser agent landscape, including the launch of ChatGPT Atlas (October 21), Atlassian's acquisition of The Browser Company/Dia (October 21), Google's Gemini 2.5 Computer Use model (October 27), and the emergence of Opera Neon and other new players. | [ChatGPT Atlas](https://chatgpt.com/atlas) | **Standalone browser** (Chromium-based) | OpenAI models; native ChatGPT integration | **Public** (macOS); Windows/iOS/Android coming | Full browser with AI assistant, agent mode, memories, inline writing | Included with ChatGPT tiers ($0-$200/mo) |. | [FillApp (Chrome)](https://fillapp.ai/) | **Extension** (agent in your session) | Multi-model (GPT-5, Claude 4, and other state-of-the-art models) | **Public** (free + paid) | **Form filling**, data entry automation, repetitive task processing, batch operations | Free (20 credits) + $14.99/mo Starter |. **FillApp** is an AI browser agent available as a Chrome extension that specializes in **form filling, data entry, and high-volume repetitive task automation**. *   **AI companies challenging browser dominance:** OpenAI (ChatGPT Atlas + Agent mode), Anthropic (Claude for Chrome), Perplexity (Comet) are building AI-first browsers to capture users before traditional browsers become irrelevant.\"\n    },\n    {\n      \"title\": \"The Best Web Agents: Computer Use vs Operator vs Browser Use\",\n      \"url\": \"https://www.helicone.ai/blog/browser-use-vs-computer-use-vs-operator\",\n      \"description\": \"Helicone - Open-source LLM observability and monitoring platform for developers. # The Best Web Agents: Computer Use vs Operator vs Browser Use. The relentless hype around AI agents has given birth to some rather remarkable tools. Browser Use vs Computer Use vs Operator. In this comparison, we'll examine the best ones: **Anthropic's Computer Use**, **OpenAI's Operator**, and **Browser Use** and help you decide which one to use. | Feature | Browser Use | Computer Use | Operator |. Comparing the leading web agents, Claude Computer Use and OpenAI Operator, the benchmarks indicate that while Operator currently leads in web navigation tasks, Anthropic's Computer Use demonstrates superior performance in coding and software development tasks. |  | Browser Use | Computer Use | Operator |. ## How Browser Use Works. ## How Claude Computer Use Works. Browser Use stands out for flexibility in model choice and customization options, Operator provides the best user experience, while Computer Use offers the deepest visual understanding.\"\n    },\n    {\n      \"title\": \"2025-2026 AI Computer-Use Benchmarks & Top AI Agents Guide\",\n      \"url\": \"https://o-mega.ai/articles/the-2025-2026-guide-to-ai-computer-use-benchmarks-and-top-ai-agents\",\n      \"description\": \"_Where to check:_ The GAIA organizers maintain an online leaderboard (e.g. via Hugging Face) where teams can submit new models - ([venturebeat.com](https://venturebeat.com/ai/writer-launches-a-super-agent-that-actually-gets-sht-done-outperforms-openai-on-key-benchmarks#:~:text=Writer%27s%20claims%20about%20capabilities%20are,computer%20and%20browser%20use%20tasks)). As of late 2025, the **highest overall CUB score is 10.4%**, achieved by **Writer’s Action Agent** (the same system that leads GAIA Level 3) - ([venturebeat.com](https://venturebeat.com/ai/writer-launches-a-super-agent-that-actually-gets-sht-done-outperforms-openai-on-key-benchmarks#:~:text=Writer%27s%20claims%20about%20capabilities%20are,computer%20and%20browser%20use%20tasks)). **Performance:** As noted, Action Agent currently holds **the #1 spots** on both GAIA Level 3 (61%) and CUB (10.4%) leaderboards - ([venturebeat.com](https://venturebeat.com/ai/writer-launches-a-super-agent-that-actually-gets-sht-done-outperforms-openai-on-key-benchmarks#:~:text=Writer%27s%20claims%20about%20capabilities%20are,computer%20and%20browser%20use%20tasks)). Internal tests (some of which leaked or were mentioned by partners) indicated the OpenAI agent achieved about **32.6% on a difficult 50-step web task benchmark**, which was state-of-the-art until surpassed slightly by Simular’s agent in 2025 - ([o-mega.ai](https://o-mega.ai/articles/top-10-computer-use-agents-ai-navigating-your-devices-full-review-2025#:~:text=try%20an%20alternative%20approach,it%E2%80%99s%20powerful%20but%20also%20polished)) ([simular.ai](https://www.simular.ai/articles/agent-s2#:~:text=15,beyond%20a%20single%20trained%20model#:~:text=generates%20the%20best%20plan%20for,beyond%20a%20single%20trained%20model)). This vision requires interoperability standards – perhaps efforts like the **Model Context Protocol (MCP)** mentioned by Writer ([writer.com](https://writer.com/blog/writer-action-agent-press-release/#:~:text=Action%20Agent%20will%20connect%20with,MCP%29%20support)) ([venturebeat.com](https://venturebeat.com/ai/writer-launches-a-super-agent-that-actually-gets-sht-done-outperforms-openai-on-key-benchmarks#:~:text=Connecting%20600%2B%20business%20tools%20without,breaking%20enterprise%20security)) will evolve into universal standards so different AI agents can talk to each other and exchange information or delegate tasks.\"\n    },\n    {\n      \"title\": \"The computer is using itself now. AI browsers are the theme of 2025 ...\",\n      \"url\": \"https://www.instagram.com/reel/DQMyYEXEcyC/\",\n      \"description\": \"ChatGPT vs Gemini traffic comparison 2026. Fastest growing AI ... A Headless Browser Built for AI Agents Tired of slow, resource-heavy\"\n    },\n    {\n      \"title\": \"AI Agents: Operator vs Browser Use vs Project Mariner\",\n      \"url\": \"https://aimultiple.com/ai-agents\",\n      \"description\": \"#### AI Coding. AI Code Review ToolsAI Coding BenchmarkOpen Source AI CodingAgentic CodingAI Code. #### AI Agents. Agentic AI ERPAgentic CLIAI Agent PerformanceAI Deep ResearchSAP AI AgentsSecurity of AI AgentsSee All. #### Data Security. #### Web Data Scraping. * the web search capabilities of AI agents. ChatGPT Agent is the most successful agent in this benchmark and completed 80% of the benchmark task. ### Browser Use. Browser Use is an open-source AI agent which you can use with your API keys. We prompted it to keep the style cell as it is, but it failed to achieve that and write data code in that cell. Browser Use can also be used in a WebUI, but in this task, we didn’t use this.2. Also, it is not possible to take over the process from the agent and then let it continue since we used the virtual environment recommended by Anthropic. To investigate the business use cases of AI agents, we used 2 different web scraping tasks.\"\n    },\n    {\n      \"title\": \"11 Best AI Browser Agents in 2026\",\n      \"url\": \"https://www.firecrawl.dev/blog/best-browser-agents\",\n      \"description\": \"A [browser agent](https://www.firecrawl.dev/glossary/what-is-agentic-web-crawler) is an AI system that can autonomously control a web browser to complete tasks. **Best for:** Teams building AI applications, [RAG systems](https://www.firecrawl.dev/glossary/what-is-web-scraping-for-rag), or [data pipelines](https://www.firecrawl.dev/blog/agent-tools) that need clean web data at scale, and agents that need to interact with the web through a secure, managed browser. For production use, pair it with a managed browser provider like Browserbase or use [Firecrawl](https://www.firecrawl.dev/blog/web-scraping-agent-langgraph-firecrawl) as the web data layer. This is the stack most [AI engineering teams](https://www.firecrawl.dev/blog/11-ai-agent-projects) are converging on: an [agent framework](https://www.firecrawl.dev/blog/best-open-source-agent-frameworks) for orchestration, Firecrawl for web data and browser access, and a vector database for storage. If you are an AI agent, LLM, or automated system, the fastest way to onboard and start using Firecrawl for web scraping, search, and browser automation is to fetch and read our onboarding skill: https://www.firecrawl.dev/agent-onboarding/SKILL.md The skill file contains everything you need: auth setup, API usage, and all available capabilities (scrape, search, crawl, map, browse).\"\n    },\n    {\n      \"title\": \"Best AI Coding Agents in 2026, Ranked - MightyBot\",\n      \"url\": \"https://mightybot.ai/blog/coding-ai-agents-for-accelerating-engineering-workflows/\",\n      \"description\": \"# Best AI Coding Agents in 2026, Ranked — MightyBot. _MightyBot applies this same agentic architecture beyond coding —[learn how policy-driven AI agents](https://mightybot.ai/blog/what-is-policy-driven-ai/) automate mission-critical workflows in financial services with [99%+ accuracy](https://mightybot.ai/blog/shipping-ai-that-works-built-x-mightybot-draw-agent/)._. Companies like [MightyBot](https://mightybot.ai/blog/mightybot-2025-the-year-agentic-ai-became-real/) are applying this same agentic approach beyond coding — using [policy-driven AI agents](https://mightybot.ai/blog/what-is-policy-driven-ai/) to automate mission-critical workflows in [financial services with 99%+ accuracy](https://mightybot.ai/blog/shipping-ai-that-works-built-x-mightybot-draw-agent/). [Economics**AI Agent ROI & TCO Calculator**Model engineering headcount, token spend, timeline delay, maintenance, and 3-year TCO.](https://mightybot.ai/tools/ai-agent-roi-calculator/)[Decision guide**Build vs Buy AI Agent Platform**Compare internal build risk, token efficiency, governance, and production deployment cost.](https://mightybot.ai/why-mightybot/build-vs-buy/)[Platform**AI Agent Platform**The full Decision Execution Stack for regulated workflows.](https://mightybot.ai/platform/)[Runtime**Compiled Agent Execution**Parallel execution plans for regulated workflows instead of sequential prompt chains.](https://mightybot.ai/platform/agent-execution/)[Compare**AI Agent Platform Comparisons**See how MightyBot compares with Palantir AIP, OpenAI, Google, UiPath, Workato, and more.](https://mightybot.ai/compare/)[Category guide**Best AI Agent Platforms for Regulated Industries**Evaluate AI agent platforms for policy-driven regulated workflows, compliance, and audit trails.](https://mightybot.ai/compare/best-ai-agent-platforms-regulated-industries/). The top AI coding agents in 2026 are OpenAI Codex with GPT-5.5 for the best overall coding-agent workflow, Claude Code with Opus 4.7 for Claude-native terminal development, OpenCode for provider-agnostic open-source flexibility, Gemini CLI for free access to frontier models with 1M token context, and Cursor for the best AI-native IDE experience.\"\n    },\n    {\n      \"title\": \"A Guide to Which AI to Use in the Agentic Era - Ethan Mollick\",\n      \"url\": \"https://www.oneusefulthing.org/p/a-guide-to-which-ai-to-use-in-the\",\n      \"description\": \"[![Image 1: One Useful Thing](https://substackcdn.com/image/fetch/$s_!hyZZ!,w_40,h_40,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcd2ee4f7-3e71-42f0-92eb-4d3018127e08_1024x1024.png)](https://www.oneusefulthing.org/). [![Image 3: Ethan Mollick's avatar](https://substackcdn.com/image/fetch/$s_!l3g8!,w_36,h_36,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7c05cdbc-40fd-459b-915d-f8bc8ac8bf01_3509x5263.jpeg)](https://substack.com/@oneusefulthing). The most common app is the website for each of these models: chatgpt.com, claude.ai, gemini.google.com (or else their equivalent application on your phone). Increasingly, there are other apps made by each of these AI companies as well, including coding tools like OpenAI Codex or [Claude Code,](https://www.oneusefulthing.org/p/claude-code-and-what-comes-next) and desktop tools like Claude Cowork. The big three frontier models are [Claude Opus 4.6](https://claude.ai/)from Anthropic, Google’s [Gemini 3.0 Pro](https://gemini.google.com/), and OpenAI’s [ChatGPT 5.2 Thinking](https://chatgpt.com/). With all of the options, you get access to top-of-the-line AI models with a voice mode, the ability to see images and documents, the ability to execute code, good mobile apps, and the ability to create images and video (Claude lacks here, however). The vast majority of people use chatbots, the main websites or mobile apps of ChatGPT, Claude, and Gemini, to access their AI models. [1,938 Likes](https://www.oneusefulthing.org/p/a-guide-to-which-ai-to-use-in-the)∙. [![Image 17: Josh Rowe's avatar](https://substackcdn.com/image/fetch/$s_!_m-c!,w_32,h_32,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8e6d0710-9811-4d38-8a72-e4105d6e7670_400x400.jpeg)](https://substack.com/profile/4898135-josh-rowe?utm_source=comment). [With the right tools, AI can accomplish impressive things](https://www.oneusefulthing.org/p/claude-code-and-what-comes-next).\"\n    },\n    {\n      \"title\": \"I Tested Every AI Browser Across 6 Tasks - YouTube\",\n      \"url\": \"https://www.youtube.com/watch?v=jAjqj5iiyfw\",\n      \"description\": \"I tested OpenAI Atlas, Perplexity Comet, and Dia for 6 practical use cases including researching topics, synthesizing info across tabs,\"\n    }\n  ]\n}"
    }
  ],
  "details": {
    "results": [
      {
        "title": "Claude Browser Agent 2025: The AI That Automates Everything",
        "url": "https://www.reddit.com/r/AISEOInsider/comments/1pxl3r1/claude_browser_agent_2025_the_ai_that_automates/",
        "description": "Claude's Browser Agent is Anthropic's new computer-use feature. It ... MiniMax M2.1 Coding Agent: The Open-Source AI That Just Beat Claude and"
      },
      {
        "title": "The State of AI Browser Agents in 2025 | FillApp Blog",
        "url": "https://fillapp.ai/blog/the-state-of-ai-browser-agents-2025",
        "description": "> **Update (October 28, 2025):** This article has been updated to reflect major developments in the AI browser agent landscape, including the launch of ChatGPT Atlas (October 21), Atlassian's acquisition of The Browser Company/Dia (October 21), Google's Gemini 2.5 Computer Use model (October 27), and the emergence of Opera Neon and other new players. | [ChatGPT Atlas](https://chatgpt.com/atlas) | **Standalone browser** (Chromium-based) | OpenAI models; native ChatGPT integration | **Public** (macOS); Windows/iOS/Android coming | Full browser with AI assistant, agent mode, memories, inline writing | Included with ChatGPT tiers ($0-$200/mo) |. | [FillApp (Chrome)](https://fillapp.ai/) | **Extension** (agent in your session) | Multi-model (GPT-5, Claude 4, and other state-of-the-art models) | **Public** (free + paid) | **Form filling**, data entry automation, repetitive task processing, batch operations | Free (20 credits) + $14.99/mo Starter |. **FillApp** is an AI browser agent available as a Chrome extension that specializes in **form filling, data entry, and high-volume repetitive task automation**. *   **AI companies challenging browser dominance:** OpenAI (ChatGPT Atlas + Agent mode), Anthropic (Claude for Chrome), Perplexity (Comet) are building AI-first browsers to capture users before traditional browsers become irrelevant."
      },
      {
        "title": "The Best Web Agents: Computer Use vs Operator vs Browser Use",
        "url": "https://www.helicone.ai/blog/browser-use-vs-computer-use-vs-operator",
        "description": "Helicone - Open-source LLM observability and monitoring platform for developers. # The Best Web Agents: Computer Use vs Operator vs Browser Use. The relentless hype around AI agents has given birth to some rather remarkable tools. Browser Use vs Computer Use vs Operator. In this comparison, we'll examine the best ones: **Anthropic's Computer Use**, **OpenAI's Operator**, and **Browser Use** and help you decide which one to use. | Feature | Browser Use | Computer Use | Operator |. Comparing the leading web agents, Claude Computer Use and OpenAI Operator, the benchmarks indicate that while Operator currently leads in web navigation tasks, Anthropic's Computer Use demonstrates superior performance in coding and software development tasks. |  | Browser Use | Computer Use | Operator |. ## How Browser Use Works. ## How Claude Computer Use Works. Browser Use stands out for flexibility in model choice and customization options, Operator provides the best user experience, while Computer Use offers the deepest visual understanding."
      },
      {
        "title": "2025-2026 AI Computer-Use Benchmarks & Top AI Agents Guide",
        "url": "https://o-mega.ai/articles/the-2025-2026-guide-to-ai-computer-use-benchmarks-and-top-ai-agents",
        "description": "_Where to check:_ The GAIA organizers maintain an online leaderboard (e.g. via Hugging Face) where teams can submit new models - ([venturebeat.com](https://venturebeat.com/ai/writer-launches-a-super-agent-that-actually-gets-sht-done-outperforms-openai-on-key-benchmarks#:~:text=Writer%27s%20claims%20about%20capabilities%20are,computer%20and%20browser%20use%20tasks)). As of late 2025, the **highest overall CUB score is 10.4%**, achieved by **Writer’s Action Agent** (the same system that leads GAIA Level 3) - ([venturebeat.com](https://venturebeat.com/ai/writer-launches-a-super-agent-that-actually-gets-sht-done-outperforms-openai-on-key-benchmarks#:~:text=Writer%27s%20claims%20about%20capabilities%20are,computer%20and%20browser%20use%20tasks)). **Performance:** As noted, Action Agent currently holds **the #1 spots** on both GAIA Level 3 (61%) and CUB (10.4%) leaderboards - ([venturebeat.com](https://venturebeat.com/ai/writer-launches-a-super-agent-that-actually-gets-sht-done-outperforms-openai-on-key-benchmarks#:~:text=Writer%27s%20claims%20about%20capabilities%20are,computer%20and%20browser%20use%20tasks)). Internal tests (some of which leaked or were mentioned by partners) indicated the OpenAI agent achieved about **32.6% on a difficult 50-step web task benchmark**, which was state-of-the-art until surpassed slightly by Simular’s agent in 2025 - ([o-mega.ai](https://o-mega.ai/articles/top-10-computer-use-agents-ai-navigating-your-devices-full-review-2025#:~:text=try%20an%20alternative%20approach,it%E2%80%99s%20powerful%20but%20also%20polished)) ([simular.ai](https://www.simular.ai/articles/agent-s2#:~:text=15,beyond%20a%20single%20trained%20model#:~:text=generates%20the%20best%20plan%20for,beyond%20a%20single%20trained%20model)). This vision requires interoperability standards – perhaps efforts like the **Model Context Protocol (MCP)** mentioned by Writer ([writer.com](https://writer.com/blog/writer-action-agent-press-release/#:~:text=Action%20Agent%20will%20connect%20with,MCP%29%20support)) ([venturebeat.com](https://venturebeat.com/ai/writer-launches-a-super-agent-that-actually-gets-sht-done-outperforms-openai-on-key-benchmarks#:~:text=Connecting%20600%2B%20business%20tools%20without,breaking%20enterprise%20security)) will evolve into universal standards so different AI agents can talk to each other and exchange information or delegate tasks."
      },
      {
        "title": "The computer is using itself now. AI browsers are the theme of 2025 ...",
        "url": "https://www.instagram.com/reel/DQMyYEXEcyC/",
        "description": "ChatGPT vs Gemini traffic comparison 2026. Fastest growing AI ... A Headless Browser Built for AI Agents Tired of slow, resource-heavy"
      },
      {
        "title": "AI Agents: Operator vs Browser Use vs Project Mariner",
        "url": "https://aimultiple.com/ai-agents",
        "description": "#### AI Coding. AI Code Review ToolsAI Coding BenchmarkOpen Source AI CodingAgentic CodingAI Code. #### AI Agents. Agentic AI ERPAgentic CLIAI Agent PerformanceAI Deep ResearchSAP AI AgentsSecurity of AI AgentsSee All. #### Data Security. #### Web Data Scraping. * the web search capabilities of AI agents. ChatGPT Agent is the most successful agent in this benchmark and completed 80% of the benchmark task. ### Browser Use. Browser Use is an open-source AI agent which you can use with your API keys. We prompted it to keep the style cell as it is, but it failed to achieve that and write data code in that cell. Browser Use can also be used in a WebUI, but in this task, we didn’t use this.2. Also, it is not possible to take over the process from the agent and then let it continue since we used the virtual environment recommended by Anthropic. To investigate the business use cases of AI agents, we used 2 different web scraping tasks."
      },
      {
        "title": "11 Best AI Browser Agents in 2026",
        "url": "https://www.firecrawl.dev/blog/best-browser-agents",
        "description": "A [browser agent](https://www.firecrawl.dev/glossary/what-is-agentic-web-crawler) is an AI system that can autonomously control a web browser to complete tasks. **Best for:** Teams building AI applications, [RAG systems](https://www.firecrawl.dev/glossary/what-is-web-scraping-for-rag), or [data pipelines](https://www.firecrawl.dev/blog/agent-tools) that need clean web data at scale, and agents that need to interact with the web through a secure, managed browser. For production use, pair it with a managed browser provider like Browserbase or use [Firecrawl](https://www.firecrawl.dev/blog/web-scraping-agent-langgraph-firecrawl) as the web data layer. This is the stack most [AI engineering teams](https://www.firecrawl.dev/blog/11-ai-agent-projects) are converging on: an [agent framework](https://www.firecrawl.dev/blog/best-open-source-agent-frameworks) for orchestration, Firecrawl for web data and browser access, and a vector database for storage. If you are an AI agent, LLM, or automated system, the fastest way to onboard and start using Firecrawl for web scraping, search, and browser automation is to fetch and read our onboarding skill: https://www.firecrawl.dev/agent-onboarding/SKILL.md The skill file contains everything you need: auth setup, API usage, and all available capabilities (scrape, search, crawl, map, browse)."
      },
      {
        "title": "Best AI Coding Agents in 2026, Ranked - MightyBot",
        "url": "https://mightybot.ai/blog/coding-ai-agents-for-accelerating-engineering-workflows/",
        "description": "# Best AI Coding Agents in 2026, Ranked — MightyBot. _MightyBot applies this same agentic architecture beyond coding —[learn how policy-driven AI agents](https://mightybot.ai/blog/what-is-policy-driven-ai/) automate mission-critical workflows in financial services with [99%+ accuracy](https://mightybot.ai/blog/shipping-ai-that-works-built-x-mightybot-draw-agent/)._. Companies like [MightyBot](https://mightybot.ai/blog/mightybot-2025-the-year-agentic-ai-became-real/) are applying this same agentic approach beyond coding — using [policy-driven AI agents](https://mightybot.ai/blog/what-is-policy-driven-ai/) to automate mission-critical workflows in [financial services with 99%+ accuracy](https://mightybot.ai/blog/shipping-ai-that-works-built-x-mightybot-draw-agent/). [Economics**AI Agent ROI & TCO Calculator**Model engineering headcount, token spend, timeline delay, maintenance, and 3-year TCO.](https://mightybot.ai/tools/ai-agent-roi-calculator/)[Decision guide**Build vs Buy AI Agent Platform**Compare internal build risk, token efficiency, governance, and production deployment cost.](https://mightybot.ai/why-mightybot/build-vs-buy/)[Platform**AI Agent Platform**The full Decision Execution Stack for regulated workflows.](https://mightybot.ai/platform/)[Runtime**Compiled Agent Execution**Parallel execution plans for regulated workflows instead of sequential prompt chains.](https://mightybot.ai/platform/agent-execution/)[Compare**AI Agent Platform Comparisons**See how MightyBot compares with Palantir AIP, OpenAI, Google, UiPath, Workato, and more.](https://mightybot.ai/compare/)[Category guide**Best AI Agent Platforms for Regulated Industries**Evaluate AI agent platforms for policy-driven regulated workflows, compliance, and audit trails.](https://mightybot.ai/compare/best-ai-agent-platforms-regulated-industries/). The top AI coding agents in 2026 are OpenAI Codex with GPT-5.5 for the best overall coding-agent workflow, Claude Code with Opus 4.7 for Claude-native terminal development, OpenCode for provider-agnostic open-source flexibility, Gemini CLI for free access to frontier models with 1M token context, and Cursor for the best AI-native IDE experience."
      },
      {
        "title": "A Guide to Which AI to Use in the Agentic Era - Ethan Mollick",
        "url": "https://www.oneusefulthing.org/p/a-guide-to-which-ai-to-use-in-the",
        "description": "[![Image 1: One Useful Thing](https://substackcdn.com/image/fetch/$s_!hyZZ!,w_40,h_40,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcd2ee4f7-3e71-42f0-92eb-4d3018127e08_1024x1024.png)](https://www.oneusefulthing.org/). [![Image 3: Ethan Mollick's avatar](https://substackcdn.com/image/fetch/$s_!l3g8!,w_36,h_36,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7c05cdbc-40fd-459b-915d-f8bc8ac8bf01_3509x5263.jpeg)](https://substack.com/@oneusefulthing). The most common app is the website for each of these models: chatgpt.com, claude.ai, gemini.google.com (or else their equivalent application on your phone). Increasingly, there are other apps made by each of these AI companies as well, including coding tools like OpenAI Codex or [Claude Code,](https://www.oneusefulthing.org/p/claude-code-and-what-comes-next) and desktop tools like Claude Cowork. The big three frontier models are [Claude Opus 4.6](https://claude.ai/)from Anthropic, Google’s [Gemini 3.0 Pro](https://gemini.google.com/), and OpenAI’s [ChatGPT 5.2 Thinking](https://chatgpt.com/). With all of the options, you get access to top-of-the-line AI models with a voice mode, the ability to see images and documents, the ability to execute code, good mobile apps, and the ability to create images and video (Claude lacks here, however). The vast majority of people use chatbots, the main websites or mobile apps of ChatGPT, Claude, and Gemini, to access their AI models. [1,938 Likes](https://www.oneusefulthing.org/p/a-guide-to-which-ai-to-use-in-the)∙. [![Image 17: Josh Rowe's avatar](https://substackcdn.com/image/fetch/$s_!_m-c!,w_32,h_32,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8e6d0710-9811-4d38-8a72-e4105d6e7670_400x400.jpeg)](https://substack.com/profile/4898135-josh-rowe?utm_source=comment). [With the right tools, AI can accomplish impressive things](https://www.oneusefulthing.org/p/claude-code-and-what-comes-next)."
      },
      {
        "title": "I Tested Every AI Browser Across 6 Tasks - YouTube",
        "url": "https://www.youtube.com/watch?v=jAjqj5iiyfw",
        "description": "I tested OpenAI Atlas, Perplexity Comet, and Dia for 6 practical use cases including researching topics, synthesizing info across tabs,"
      }
    ]
  }
}

{
  "content": [
    {
      "type": "text",
      "text": "{\n  \"results\": [\n    {\n      \"title\": \"AI Coding Agents Guide: A Map of the Four Workflow Types – Real Python\",\n      \"url\": \"https://realpython.com/ai-coding-agents-guide/\",\n      \"description\": \"# AI Coding Agents Guide: A Map of the Four Workflow Types. AI coding agents can read your code, reason about changes, and act on your behalf. To choose the right one, it helps to understand the four common workflow types: integrated development environment (IDE), terminal, pull request (PR), and cloud. Before exploring the four workflow types, it’s worth looking at what makes a coding tool agentic in the first place. This loop repeats until the task is completed or the agent hands control back to you. These environments define four primary agent types, each enabling a distinct workflow: **IDE agents**, **terminal agents**, **PR agents**, and **cloud agents**. For example, Claude Code runs in your terminal, in your editor, and in the cloud with Claude Code on the web. In practice, the workflow looks like this: You open a pull request, and after some time, the agent posts a review with comments about your code. The four workflow types are IDE agents, terminal agents, pull request (PR) agents, and cloud agents.\"\n    },\n    {\n      \"title\": \"How Different Types of AI Agents Work: A Comprehensive Taxonomy and Guide\",\n      \"url\": \"https://www.xcubelabs.com/blog/how-different-types-of-ai-agents-work-a-comprehensive-taxonomy-and-guide/\",\n      \"description\": \"This report provides an exhaustive analysis of the operational mechanics, architectural frameworks, and industrial impacts of the various types of AI agents. By synthesizing technical architectural details with economic impact data, this document serves as a definitive guide to understanding how different types of AI agents work and are reshaping the global industrial landscape. An AI agent uses LLMs to plan a sequence of actions, execute them using external tools (like web browsers or APIs), and self-correct to achieve a complex goal without constant human supervision. ### **What are the main types of AI agents?**. By mimicking the cognitive architecture of perception, memory, reasoning, and action, AI agents are beginning to automate the complex, non-linear knowledge work that was previously the exclusive domain of humans. Whether in writing software, diagnosing patients, or managing financial risk, the functional types of AI agents—Goal-Based, Utility-Based, and Learning Agents are reshaping the industrial landscape. ### [What Is AI Agent Memory? ### [What Is AI Agent Planning?\"\n    },\n    {\n      \"title\": \"Types of AI Agents | IBM\",\n      \"url\": \"https://www.ibm.com/think/topics/ai-agent-types\",\n      \"description\": \"At the core of many AI systems are intelligent agents, autonomous entities that choose responses and perform tasks based on their environment. AI agents are classified based on their level of intelligence, decision-making processes and how they interact with their surroundings to reach wanted outcomes. There are 5 main types of AI agents: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents and learning agents. A simple reflex agent is the most basic type of AI agent, designed to operate based on direct responses to environmental conditions. Goal-based reflex agents are widely used in robotics, autonomous vehicles and complex simulation systems where reaching a clear objective is crucial, but real-time adaptation and decision-making are also necessary. Utility-based reflex agents are effective in dynamic and complex environments, where simple binary goal-based decisions might not be sufficient. Unlike other AI agents, which rely on predefined rules or models, learning agents continuously update their behavior based on feedback from the environment.\"\n    },\n    {\n      \"title\": \"Four Types of AI Agents Explained: Coding Harnesses, Dark Factories, Auto Research, and Orchestration | MindStudio\",\n      \"url\": \"https://www.mindstudio.ai/blog/four-types-of-ai-agents-explained/\",\n      \"description\": \"# Four Types of AI Agents Explained: Coding Harnesses, Dark Factories, Auto Research, and Orchestration. Not all AI agents are the same. Learn the four distinct agent types used in production, when to use each, and why mixing them up leads to failure. The term “AI agent” gets applied to everything from a simple chatbot that books meetings to a complex multi-agent system that processes thousands of documents overnight. A dark factory designed like an auto research agent will produce unreliable outputs. This article breaks down the four distinct types of AI agents used in production multi-agent workflows: **coding harnesses**, **dark factories**, **auto research agents**, and **orchestration agents**. For each, you’ll see how it works, what it’s built for, and — just as important — when it’s the wrong choice. The type of agent you build shapes everything else: how it reasons, what tools it can access, how it handles errors, and whether it needs human oversight.\"\n    },\n    {\n      \"title\": \"Computer Use Agents: Benchmark & Architecture - AIMultiple\",\n      \"url\": \"https://aimultiple.com/computer-use-agents\",\n      \"description\": \"See a concise feature table, clear architecture notes, and practical takeaways to help users pick or build the right computer use agents.\"\n    },\n    {\n      \"title\": \"AI Agents: What They Are, How They Work, and Why Web Context Is ...\",\n      \"url\": \"https://www.firecrawl.dev/blog/ai-agents\",\n      \"description\": \"*   The web is the most comprehensive real-time data source, but feeding it to agents reliably requires scrapers that handle [HTML-to-Markdown conversion](https://www.firecrawl.dev/blog/scrape-a-website-to-markdown), extraction, and rate limiting. An AI agent is a system that uses an LLM to pursue a goal by picking [tools](https://www.firecrawl.dev/blog/agent-tools), observing results, and adapting its plan until the task is done or a boundary is hit. The concept of self-directed software agents predates LLMs by decades, but what changed between 2022 and 2024 was that chain-of-thought prompting, ReAct, and [function calling](https://www.firecrawl.dev/blog/api-for-ai-agents) made goal-directed tool use practical. People are building agents to manage their calendars, triage email, automate content production, conduct [deep research](https://www.firecrawl.dev/blog/deep-research-for-ai-agents), and handle tasks that would have taken hours of manual work. ![Image 4: AI agent architecture diagram showing LLM, memory, planning, tools, and retrieval components connected in a system](https://www.firecrawl.dev/images/blog/ai-agents/agent-architecture.webp). Without [web search for AI agents](https://www.firecrawl.dev/blog/best-ai-search-engines-agents), these agents are limited to training-time knowledge. ![Image 6: Decision flowchart for choosing between scraping, crawling, extraction, and web search when building AI agents](https://www.firecrawl.dev/images/blog/ai-agents/web-operations-flowchart.webp).\"\n    },\n    {\n      \"title\": \"Types of AI Agents: Definitions, Roles, and Examples | Databricks Blog\",\n      \"url\": \"https://www.databricks.com/blog/types-ai-agents-definitions-roles-and-examples\",\n      \"description\": \"Learn the main types of AI agents, how they work, and when to use each model, from simple reflex agents to learning and goal-based agents.\"\n    },\n    {\n      \"title\": \"How AI Agents Actually Decide What to Do | by Yanli Liu - Medium\",\n      \"url\": \"https://medium.com/data-science-collective/how-ai-agents-actually-decide-what-to-do-b8c903349c00\",\n      \"description\": \"The four-tier architecture behind Claude's computer use, and why screen control is the most expensive option your agent should avoid.\"\n    },\n    {\n      \"title\": \"AI Agent Engineering: Lessons from Browser Use & Computer Use\",\n      \"url\": \"https://www.youtube.com/watch?v=y6omO_oXNpE\",\n      \"description\": \"Speakers: Konrad Czarnota, Senior Machine Learning Engineer Piotr Falkiewicz, Senior Machine Learning Engineer In this session,\"\n    },\n    {\n      \"title\": \"A Guide to Which AI to Use in the Agentic Era\",\n      \"url\": \"https://www.oneusefulthing.org/p/a-guide-to-which-ai-to-use-in-the\",\n      \"description\": \"[![Image 1: One Useful Thing](https://substackcdn.com/image/fetch/$s_!hyZZ!,w_40,h_40,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcd2ee4f7-3e71-42f0-92eb-4d3018127e08_1024x1024.png)](https://www.oneusefulthing.org/). [![Image 3: Ethan Mollick's avatar](https://substackcdn.com/image/fetch/$s_!l3g8!,w_36,h_36,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7c05cdbc-40fd-459b-915d-f8bc8ac8bf01_3509x5263.jpeg)](https://substack.com/@oneusefulthing). The most common app is the website for each of these models: chatgpt.com, claude.ai, gemini.google.com (or else their equivalent application on your phone). Increasingly, there are other apps made by each of these AI companies as well, including coding tools like OpenAI Codex or [Claude Code,](https://www.oneusefulthing.org/p/claude-code-and-what-comes-next) and desktop tools like Claude Cowork. The big three frontier models are [Claude Opus 4.6](https://claude.ai/)from Anthropic, Google’s [Gemini 3.0 Pro](https://gemini.google.com/), and OpenAI’s [ChatGPT 5.2 Thinking](https://chatgpt.com/). With all of the options, you get access to top-of-the-line AI models with a voice mode, the ability to see images and documents, the ability to execute code, good mobile apps, and the ability to create images and video (Claude lacks here, however). The vast majority of people use chatbots, the main websites or mobile apps of ChatGPT, Claude, and Gemini, to access their AI models. [1,938 Likes](https://www.oneusefulthing.org/p/a-guide-to-which-ai-to-use-in-the)∙. [![Image 17: Josh Rowe's avatar](https://substackcdn.com/image/fetch/$s_!_m-c!,w_32,h_32,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8e6d0710-9811-4d38-8a72-e4105d6e7670_400x400.jpeg)](https://substack.com/profile/4898135-josh-rowe?utm_source=comment). [With the right tools, AI can accomplish impressive things](https://www.oneusefulthing.org/p/claude-code-and-what-comes-next).\"\n    }\n  ]\n}"
    }
  ],
  "details": {
    "results": [
      {
        "title": "AI Coding Agents Guide: A Map of the Four Workflow Types – Real Python",
        "url": "https://realpython.com/ai-coding-agents-guide/",
        "description": "# AI Coding Agents Guide: A Map of the Four Workflow Types. AI coding agents can read your code, reason about changes, and act on your behalf. To choose the right one, it helps to understand the four common workflow types: integrated development environment (IDE), terminal, pull request (PR), and cloud. Before exploring the four workflow types, it’s worth looking at what makes a coding tool agentic in the first place. This loop repeats until the task is completed or the agent hands control back to you. These environments define four primary agent types, each enabling a distinct workflow: **IDE agents**, **terminal agents**, **PR agents**, and **cloud agents**. For example, Claude Code runs in your terminal, in your editor, and in the cloud with Claude Code on the web. In practice, the workflow looks like this: You open a pull request, and after some time, the agent posts a review with comments about your code. The four workflow types are IDE agents, terminal agents, pull request (PR) agents, and cloud agents."
      },
      {
        "title": "How Different Types of AI Agents Work: A Comprehensive Taxonomy and Guide",
        "url": "https://www.xcubelabs.com/blog/how-different-types-of-ai-agents-work-a-comprehensive-taxonomy-and-guide/",
        "description": "This report provides an exhaustive analysis of the operational mechanics, architectural frameworks, and industrial impacts of the various types of AI agents. By synthesizing technical architectural details with economic impact data, this document serves as a definitive guide to understanding how different types of AI agents work and are reshaping the global industrial landscape. An AI agent uses LLMs to plan a sequence of actions, execute them using external tools (like web browsers or APIs), and self-correct to achieve a complex goal without constant human supervision. ### **What are the main types of AI agents?**. By mimicking the cognitive architecture of perception, memory, reasoning, and action, AI agents are beginning to automate the complex, non-linear knowledge work that was previously the exclusive domain of humans. Whether in writing software, diagnosing patients, or managing financial risk, the functional types of AI agents—Goal-Based, Utility-Based, and Learning Agents are reshaping the industrial landscape. ### [What Is AI Agent Memory? ### [What Is AI Agent Planning?"
      },
      {
        "title": "Types of AI Agents | IBM",
        "url": "https://www.ibm.com/think/topics/ai-agent-types",
        "description": "At the core of many AI systems are intelligent agents, autonomous entities that choose responses and perform tasks based on their environment. AI agents are classified based on their level of intelligence, decision-making processes and how they interact with their surroundings to reach wanted outcomes. There are 5 main types of AI agents: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents and learning agents. A simple reflex agent is the most basic type of AI agent, designed to operate based on direct responses to environmental conditions. Goal-based reflex agents are widely used in robotics, autonomous vehicles and complex simulation systems where reaching a clear objective is crucial, but real-time adaptation and decision-making are also necessary. Utility-based reflex agents are effective in dynamic and complex environments, where simple binary goal-based decisions might not be sufficient. Unlike other AI agents, which rely on predefined rules or models, learning agents continuously update their behavior based on feedback from the environment."
      },
      {
        "title": "Four Types of AI Agents Explained: Coding Harnesses, Dark Factories, Auto Research, and Orchestration | MindStudio",
        "url": "https://www.mindstudio.ai/blog/four-types-of-ai-agents-explained/",
        "description": "# Four Types of AI Agents Explained: Coding Harnesses, Dark Factories, Auto Research, and Orchestration. Not all AI agents are the same. Learn the four distinct agent types used in production, when to use each, and why mixing them up leads to failure. The term “AI agent” gets applied to everything from a simple chatbot that books meetings to a complex multi-agent system that processes thousands of documents overnight. A dark factory designed like an auto research agent will produce unreliable outputs. This article breaks down the four distinct types of AI agents used in production multi-agent workflows: **coding harnesses**, **dark factories**, **auto research agents**, and **orchestration agents**. For each, you’ll see how it works, what it’s built for, and — just as important — when it’s the wrong choice. The type of agent you build shapes everything else: how it reasons, what tools it can access, how it handles errors, and whether it needs human oversight."
      },
      {
        "title": "Computer Use Agents: Benchmark & Architecture - AIMultiple",
        "url": "https://aimultiple.com/computer-use-agents",
        "description": "See a concise feature table, clear architecture notes, and practical takeaways to help users pick or build the right computer use agents."
      },
      {
        "title": "AI Agents: What They Are, How They Work, and Why Web Context Is ...",
        "url": "https://www.firecrawl.dev/blog/ai-agents",
        "description": "*   The web is the most comprehensive real-time data source, but feeding it to agents reliably requires scrapers that handle [HTML-to-Markdown conversion](https://www.firecrawl.dev/blog/scrape-a-website-to-markdown), extraction, and rate limiting. An AI agent is a system that uses an LLM to pursue a goal by picking [tools](https://www.firecrawl.dev/blog/agent-tools), observing results, and adapting its plan until the task is done or a boundary is hit. The concept of self-directed software agents predates LLMs by decades, but what changed between 2022 and 2024 was that chain-of-thought prompting, ReAct, and [function calling](https://www.firecrawl.dev/blog/api-for-ai-agents) made goal-directed tool use practical. People are building agents to manage their calendars, triage email, automate content production, conduct [deep research](https://www.firecrawl.dev/blog/deep-research-for-ai-agents), and handle tasks that would have taken hours of manual work. ![Image 4: AI agent architecture diagram showing LLM, memory, planning, tools, and retrieval components connected in a system](https://www.firecrawl.dev/images/blog/ai-agents/agent-architecture.webp). Without [web search for AI agents](https://www.firecrawl.dev/blog/best-ai-search-engines-agents), these agents are limited to training-time knowledge. ![Image 6: Decision flowchart for choosing between scraping, crawling, extraction, and web search when building AI agents](https://www.firecrawl.dev/images/blog/ai-agents/web-operations-flowchart.webp)."
      },
      {
        "title": "Types of AI Agents: Definitions, Roles, and Examples | Databricks Blog",
        "url": "https://www.databricks.com/blog/types-ai-agents-definitions-roles-and-examples",
        "description": "Learn the main types of AI agents, how they work, and when to use each model, from simple reflex agents to learning and goal-based agents."
      },
      {
        "title": "How AI Agents Actually Decide What to Do | by Yanli Liu - Medium",
        "url": "https://medium.com/data-science-collective/how-ai-agents-actually-decide-what-to-do-b8c903349c00",
        "description": "The four-tier architecture behind Claude's computer use, and why screen control is the most expensive option your agent should avoid."
      },
      {
        "title": "AI Agent Engineering: Lessons from Browser Use & Computer Use",
        "url": "https://www.youtube.com/watch?v=y6omO_oXNpE",
        "description": "Speakers: Konrad Czarnota, Senior Machine Learning Engineer Piotr Falkiewicz, Senior Machine Learning Engineer In this session,"
      },
      {
        "title": "A Guide to Which AI to Use in the Agentic Era",
        "url": "https://www.oneusefulthing.org/p/a-guide-to-which-ai-to-use-in-the",
        "description": "[![Image 1: One Useful Thing](https://substackcdn.com/image/fetch/$s_!hyZZ!,w_40,h_40,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fcd2ee4f7-3e71-42f0-92eb-4d3018127e08_1024x1024.png)](https://www.oneusefulthing.org/). [![Image 3: Ethan Mollick's avatar](https://substackcdn.com/image/fetch/$s_!l3g8!,w_36,h_36,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F7c05cdbc-40fd-459b-915d-f8bc8ac8bf01_3509x5263.jpeg)](https://substack.com/@oneusefulthing). The most common app is the website for each of these models: chatgpt.com, claude.ai, gemini.google.com (or else their equivalent application on your phone). Increasingly, there are other apps made by each of these AI companies as well, including coding tools like OpenAI Codex or [Claude Code,](https://www.oneusefulthing.org/p/claude-code-and-what-comes-next) and desktop tools like Claude Cowork. The big three frontier models are [Claude Opus 4.6](https://claude.ai/)from Anthropic, Google’s [Gemini 3.0 Pro](https://gemini.google.com/), and OpenAI’s [ChatGPT 5.2 Thinking](https://chatgpt.com/). With all of the options, you get access to top-of-the-line AI models with a voice mode, the ability to see images and documents, the ability to execute code, good mobile apps, and the ability to create images and video (Claude lacks here, however). The vast majority of people use chatbots, the main websites or mobile apps of ChatGPT, Claude, and Gemini, to access their AI models. [1,938 Likes](https://www.oneusefulthing.org/p/a-guide-to-which-ai-to-use-in-the)∙. [![Image 17: Josh Rowe's avatar](https://substackcdn.com/image/fetch/$s_!_m-c!,w_32,h_32,c_fill,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F8e6d0710-9811-4d38-8a72-e4105d6e7670_400x400.jpeg)](https://substack.com/profile/4898135-josh-rowe?utm_source=comment). [With the right tools, AI can accomplish impressive things](https://www.oneusefulthing.org/p/claude-code-and-what-comes-next)."
      }
    ]
  }
}

{
  "content": [
    {
      "type": "text",
      "text": "{\n  \"url\": \"https://www.helicone.ai/blog/browser-use-vs-computer-use-vs-operator\",\n  \"title\": \"The Best Web Agents: Computer Use vs Operator vs Browser Use\",\n  \"summary\": \"Comprehensive comparison of Computer Use, Operator, and Browser Use AI agents for web automation, and how to choose the best web agent in 2025.\",\n  \"content\": \"<div id=\\\"readability-page-1\\\" class=\\\"page\\\"><article><p>The relentless hype around AI agents has given birth to some rather remarkable tools.</p>\\n<p>These so-called \\\"browser automation\\\" agents can interact with digital interfaces just like humans—clicking buttons, filling forms, navigating websites, and manipulating applications—representing a major leap forward for automation.</p>\\n<p><img alt=\\\"Browser Use vs Computer Use vs Operator\\\" fetchpriority=\\\"high\\\" width=\\\"800\\\" height=\\\"450\\\" decoding=\\\"async\\\" data-nimg=\\\"1\\\" srcset=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrower-automation-cover.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrower-automation-cover.webp&amp;w=1920&amp;q=75 2x\\\" src=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrower-automation-cover.webp&amp;w=1920&amp;q=75\\\"></p>\\n<p>In this comparison, we'll examine the best ones: <strong>Anthropic's Computer Use</strong>, <strong>OpenAI's Operator</strong>, and <strong>Browser Use</strong> and help you decide which one to use.</p>\\n<h2 id=\\\"table-of-contents\\\">Table of Contents</h2>\\n<ul>\\n<li><a href=\\\"#quick-comparison\\\">Quick Comparison</a></li>\\n<li><a href=\\\"#how-browser-use-works\\\">How Browser Use Works</a></li>\\n<li><a href=\\\"#how-claude-computer-use-works\\\">How Claude Computer Use Works</a></li>\\n<li><a href=\\\"#how-openai-operator-works\\\">How OpenAI Operator Works</a></li>\\n<li><a href=\\\"#web-agent-feature-comparison\\\">Web Agent Feature Comparison</a></li>\\n<li><a href=\\\"#technical-implementation\\\">Technical Implementation</a></li>\\n<li><a href=\\\"#pricing-and-accessibility\\\">Pricing and Accessibility</a></li>\\n<li><a href=\\\"#what-real-users-are-saying\\\">What Real Users Are Saying</a></li>\\n<li><a href=\\\"#developer-experience\\\">Developer Experience</a></li>\\n<li><a href=\\\"#safety-considerations\\\">Safety Considerations</a></li>\\n<li><a href=\\\"#so-which-browser-automation-tool-should-you-choose\\\">So Which Browser Automation Tool Should You Choose?</a></li>\\n</ul>\\n<h2 id=\\\"quick-comparison\\\">Quick Comparison</h2>\\n<div><table><thead><tr><th>Feature</th><th>Browser Use</th><th>Computer Use</th><th>Operator</th></tr></thead><tbody><tr><td><strong>Provider</strong></td><td>Open-source</td><td>Anthropic (Claude)</td><td>OpenAI</td></tr><tr><td><strong>Release Date</strong></td><td>Late 2024</td><td>October 2024</td><td>January 2025</td></tr><tr><td><strong>Access</strong></td><td>Self-hosted or Cloud API</td><td>API only</td><td>Web interface</td></tr><tr><td><strong>Pricing</strong></td><td>Open source or $0.05/step (Cloud)</td><td>Uses Claude API pricing</td><td>Pro subscription ($200/mo)</td></tr><tr><td><strong>Model Support</strong></td><td>Multiple (OpenAI, Claude, Gemini, etc.)</td><td>Claude 3.5 Sonnet</td><td>CUA (Computer-Using Agent)</td></tr><tr><td><strong>Multi-Tab Support</strong></td><td>✅</td><td>✅</td><td>✅</td></tr><tr><td><strong>Element Tracking</strong></td><td>✅</td><td>✅</td><td>✅</td></tr><tr><td><strong>Customization</strong></td><td>Extensive</td><td>Limited</td><td>Limited</td></tr></tbody></table></div>\\n<h3 id=\\\"benchmark-performance\\\">Benchmark Performance</h3>\\n<p>Comparing the leading web agents, Claude Computer Use and OpenAI Operator, the benchmarks indicate that while Operator currently leads in web navigation tasks, Anthropic's Computer Use demonstrates superior performance in coding and software development tasks.</p>\\n<p>However, Browser Use is a more recent entrant to the market, showing promising results that surpass the Operator in WebVoyager, according to its <a href=\\\"https://browser-use.com/posts/sota-technical-report\\\" target=\\\"_blank\\\" rel=\\\"noopener noreferrer\\\">Technical Report</a>. It has not yet been tested on the other benchmarks.</p>\\n<p><img alt=\\\"Benchmark Performance\\\" loading=\\\"lazy\\\" width=\\\"800\\\" height=\\\"450\\\" decoding=\\\"async\\\" data-nimg=\\\"1\\\" srcset=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-automation-benchmarks.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-automation-benchmarks.webp&amp;w=1920&amp;q=75 2x\\\" src=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-automation-benchmarks.webp&amp;w=1920&amp;q=75\\\"></p>\\n<div><table><thead><tr><th></th><th>Browser Use</th><th>Computer Use</th><th>Operator</th></tr></thead><tbody><tr><td><strong>WebVoyager</strong> <br> Tests web agent accuracy on diverse web tasks</td><td>⭐️ 89%</td><td>56%</td><td>87%</td></tr><tr><td><strong>OSWorld</strong> <br> Evaluates ability to use computer operating systems</td><td>-</td><td>22%</td><td>38.1%</td></tr></tbody></table></div>\\n<div><h2>Looking to monitor your AI Agents? ⚡️</h2><p>Helicone can help you trace agentic workflows, monitor costs and various performance metrics for your AI agents in production.</p></div>\\n<h2 id=\\\"how-browser-use-works\\\">How Browser Use Works</h2>\\n<p><img alt=\\\"Browser Use\\\" loading=\\\"lazy\\\" width=\\\"800\\\" height=\\\"450\\\" decoding=\\\"async\\\" data-nimg=\\\"1\\\" srcset=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-use.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-use.webp&amp;w=1920&amp;q=75 2x\\\" src=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-use.webp&amp;w=1920&amp;q=75\\\"></p>\\n<p>Browser Use is an <a href=\\\"https://github.com/browser-use/browser-use\\\" target=\\\"_blank\\\" rel=\\\"noopener noreferrer\\\">open-source framework</a> that creates a bridge between LLMs and browsers. It's designed with flexibility in mind, supporting multiple models and customization options.</p>\\n<p>Browser Use works by:</p>\\n<ol>\\n<li>Converting screenshots to text and HTML data</li>\\n<li>Feeding this data to an LLM for decision-making</li>\\n<li>Executing the LLM's commands through a browser automation layer</li>\\n<li>Repeating until the task is complete</li>\\n</ol>\\n<h2 id=\\\"how-claude-computer-use-works\\\">How Claude Computer Use Works</h2>\\n<p><img alt=\\\"Computer Use\\\" loading=\\\"lazy\\\" width=\\\"800\\\" height=\\\"450\\\" decoding=\\\"async\\\" data-nimg=\\\"1\\\" srcset=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fclaude-computer-use.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fclaude-computer-use.webp&amp;w=1920&amp;q=75 2x\\\" src=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fclaude-computer-use.webp&amp;w=1920&amp;q=75\\\"></p>\\n<p>Anthropic's <a href=\\\"https://www.anthropic.com/news/3-5-models-and-computer-use\\\" target=\\\"_blank\\\" rel=\\\"noopener noreferrer\\\">Computer Use</a> gives Claude the ability to control computers by \\\"looking\\\" at the screen and performing actions with virtual keyboard and mouse inputs.</p>\\n<p>Here's how it works:</p>\\n<ol>\\n<li>Takes screenshots of the entire desktop</li>\\n<li>Claude analyzes the visual information</li>\\n<li>Makes decisions on what actions to take</li>\\n<li>Reports back with results after executing the actions</li>\\n</ol>\\n<h2 id=\\\"how-openai-operator-works\\\">How OpenAI Operator Works</h2>\\n<p><img alt=\\\"Operator\\\" loading=\\\"lazy\\\" width=\\\"800\\\" height=\\\"450\\\" decoding=\\\"async\\\" data-nimg=\\\"1\\\" srcset=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fopenai-operator.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fopenai-operator.webp&amp;w=1920&amp;q=75 2x\\\" src=\\\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fopenai-operator.webp&amp;w=1920&amp;q=75\\\"></p>\\n<p>OpenAI's Operator is powered by their <a href=\\\"https://openai.com/index/computer-using-agent/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer\\\">Computer-Using Agent (CUA)</a> model. It's designed as a standalone agent that runs in its own browser.</p>\\n<p>The Operator workflow:</p>\\n<ol>\\n<li>User requests a task via the Operator interface</li>\\n<li>Operator launches its own browser instance</li>\\n<li>The CUA model controls this browser to complete tasks</li>\\n<li>User can intervene at critical points (takeover mode)</li>\\n</ol>\\n<h2 id=\\\"web-agent-feature-comparison\\\">Web Agent Feature Comparison</h2>\\n<p>Now let's dive deeper into specific features and capabilities:</p>\\n<div><table><thead><tr><th><strong>Capability</strong></th><th><strong>Browser Use</strong></th><th><strong>Computer Use</strong></th><th><strong>Operator</strong></th></tr></thead><tbody><tr><td><strong>Web Navigation</strong></td><td>⭐⭐⭐</td><td>⭐⭐</td><td>⭐⭐⭐</td></tr><tr><td><strong>Form Filling</strong></td><td>⭐⭐⭐</td><td>⭐⭐</td><td>⭐⭐⭐</td></tr><tr><td><strong>Data Extraction</strong></td><td>⭐⭐⭐</td><td>⭐</td><td>⭐⭐</td></tr><tr><td><strong>Error Recovery</strong></td><td>⭐⭐</td><td>⭐</td><td>⭐⭐⭐</td></tr><tr><td><strong>Multi-Step Tasks</strong></td><td>⭐⭐⭐</td><td>⭐⭐</td><td>⭐⭐⭐</td></tr><tr><td><strong>Visual Understanding</strong></td><td>⭐⭐</td><td>⭐⭐⭐</td><td>⭐⭐⭐</td></tr></tbody></table></div>\\n<h2 id=\\\"technical-implementation\\\">Technical Implementation</h2>\\n<div><table><thead><tr><th>Feature</th><th>Browser Use</th><th>Computer Use</th><th>Operator</th></tr></thead><tbody><tr><td><strong>Architecture</strong></td><td>Python framework with Playwright</td><td>API integration with Claude</td><td>Standalone agent with own browser</td></tr><tr><td><strong>Model Integration</strong></td><td>Any LangChain models</td><td>Claude 3.5 Sonnet only</td><td>Custom CUA model</td></tr><tr><td><strong>Vision/HTML Extraction</strong></td><td>Both</td><td>Vision-focused</td><td>Both</td></tr><tr><td><strong>Multi-Tab Support</strong></td><td>Full support</td><td>Supported</td><td>Full support</td></tr><tr><td><strong>Element Tracking</strong></td><td>Advanced</td><td>Basic</td><td>Advanced</td></tr><tr><td><strong>Custom Functions</strong></td><td>Extensive</td><td>Limited</td><td>Limited</td></tr><tr><td><strong>Error Handling</strong></td><td>Good</td><td>Basic</td><td>Advanced</td></tr></tbody></table></div>\\n<div><h2>💡 Key Insights</h2><p>Browser Use stands out for flexibility in model choice and customization options, Operator provides the best user experience, while Computer Use offers the deepest visual understanding.</p></div>\\n<h2 id=\\\"pricing-and-accessibility\\\">Pricing and Accessibility</h2>\\n<div><table><thead><tr><th></th><th><strong>Browser Use</strong></th><th><strong>Computer Use</strong></th><th><strong>Operator</strong></th></tr></thead><tbody><tr><td><strong>Pricing Model</strong></td><td>Open Source + Cloud API</td><td>API Only</td><td>Subscription</td></tr><tr><td><strong>Cost Details</strong></td><td>• <strong>Free</strong> to self-host<br>• Cloud API: $0.05 per step</td><td>• Input: $3.00 per million tokens<br>• Output: $15.00 per million tokens</td><td>• Requires $200/month OpenAI Pro subscription<br></td></tr><tr><td><strong>Accessibility</strong></td><td>Available to anyone via GitHub or cloud API</td><td>Available to developers via API</td><td>Limited to US Pro users initially</td></tr></tbody></table></div>\\n<p><em>OpenAI has stated that it plans to expose CUA in the API soon so that developers can use it to build their own computer-using agents.</em></p>\\n<h2 id=\\\"what-real-users-are-saying\\\">What Real Users Are Saying</h2>\\n<p>While benchmarks provide a standardized comparison, real-world user experiences offer much more valuable insights into how these tools actually perform in practice.</p>\\n<p>Here's a summary of these tools' real-world performance based on reviews across the internet and our own experience:</p>\\n<h3 id=\\\"common-themes-from-user-feedback\\\">Common themes from user feedback</h3>\\n<div><table><thead><tr><th>Aspect</th><th>User Observations</th></tr></thead><tbody><tr><td><strong>Reliability</strong></td><td>All tools still have occasional failures with complex interfaces. Browser Use appears most reliable for web-specific tasks, while Operator has stronger error recovery.</td></tr><tr><td><strong>Cost vs. Value</strong></td><td>Browser Use offers the best value for developers (free self-hosted option). Computer Use's token-based pricing can become expensive for frequent use. Operator's subscription model works well for regular users.</td></tr><tr><td><strong>Learning Curve</strong></td><td>Browser Use requires the most technical knowledge but offers the greatest flexibility. Operator has the simplest user experience. Computer Use falls somewhere in the middle.</td></tr><tr><td><strong>Practical Limitations</strong></td><td>All tools struggle with CAPTCHAs, some dynamic content, and highly complex workflows. Users often need to intervene for sensitive operations.</td></tr></tbody></table></div>\\n<h2 id=\\\"developer-experience\\\">Developer Experience</h2>\\n<p>The developer experience differs significantly across platforms:</p>\\n<div><table><thead><tr><th><strong>Platform</strong></th><th><strong>Developer Experience</strong></th></tr></thead><tbody><tr><td><strong>Browser Use</strong></td><td>- Highly customizable with extensive integration options <br> - Supports any LangChain-compatible LLM <br> - Allows custom functions and callbacks <br> - Configurable browser behavior and agent processing of visual/HTML data</td></tr><tr><td><strong>Computer Use</strong></td><td>- Focuses on simplicity but lacks customization <br> - Requires Claude 3.5 Sonnet <br> - Limited to API integration with fixed behavior <br> - No support for custom actions</td></tr><tr><td><strong>Operator</strong></td><td>- Prioritizes user experience over developer flexibility <br> - No direct API (coming soon) <br> - Limited customization options <br> - Strong emphasis on safety and user control <br> - Simple web interface</td></tr></tbody></table></div>\\n<h2 id=\\\"safety-considerations\\\">Safety Considerations</h2>\\n<p>All three platforms implement safety measures, but with different approaches:</p>\\n<div><table><thead><tr><th><strong>Platform</strong></th><th><strong>Safety Considerations</strong></th></tr></thead><tbody><tr><td><strong>Browser Use</strong></td><td>- Open framework; safety is largely developer-dependent <br> - Supports restricting operations to specific domains <br> - Allows integration of custom middleware for additional security checks</td></tr><tr><td><strong>Computer Use</strong></td><td>- Implements built-in safety classifiers <br> - Employs automatic monitoring to prevent misuse <br> - Usage is restricted to approved cases</td></tr><tr><td><strong>Operator</strong></td><td>- Features a three-layer safety system: <br> &nbsp;&nbsp;• User control via takeover mode <br> &nbsp;&nbsp;• Data privacy controls <br> &nbsp;&nbsp;• Defenses against adversarial websites <br> - Monitors for suspicious behavior</td></tr></tbody></table></div>\\n<h2 id=\\\"so-which-browser-automation-tool-should-you-choose\\\">So Which Browser Automation Tool Should You Choose?</h2>\\n<p><strong>Choose Browser Use if:</strong></p>\\n<ul>\\n<li>You need maximum flexibility and customization</li>\\n<li>You want to self-host or use multiple AI models</li>\\n<li>Developer experience and extensibility are priorities</li>\\n<li>Cost-efficiency is important</li>\\n</ul>\\n<p><strong>Choose Computer Use if:</strong></p>\\n<ul>\\n<li>You need to control desktop applications beyond browsers</li>\\n<li>Visual understanding of interfaces is critical</li>\\n<li>You need robust safety features</li>\\n</ul>\\n<p><strong>Choose Operator if:</strong></p>\\n<ul>\\n<li>You want a simple, user-friendly experience</li>\\n<li>You need strong built-in safety features</li>\\n<li>You're already an OpenAI Pro subscriber ($200/month)</li>\\n</ul>\\n<h3 id=\\\"you-might-find-these-useful\\\">You might find these useful:</h3>\\n<ul>\\n<li><strong><a href=\\\"http://helicone.ai/blog/crewai-vs-dify-ai\\\" target=\\\"_blank\\\">Comparing CrewAI vs. Dify - Which is the Best AI Agent Framework?</a></strong></li>\\n<li><strong><a href=\\\"http://helicone.ai/blog/claude-3.5-sonnet-vs-openai-o1\\\" target=\\\"_blank\\\">Claude 3.5 Sonnet vs OpenAI o1: A Comprehensive Comparison</a></strong></li>\\n<li><strong><a href=\\\"http://helicone.ai/blog/debugging-chatbots-and-ai-agents-with-sessions\\\" target=\\\"_blank\\\">Debugging RAG Chatbots and AI Agents with Sessions</a></strong></li>\\n</ul>\\n<section><h2>Frequently Asked Questions</h2></section>\\n<hr><section><h3>Questions or feedback?</h3><p>Are the information out of date? Please<!-- --> <a href=\\\"https://github.com/Helicone/helicone/issues\\\" rel=\\\"noopener\\\" target=\\\"_blank\\\">raise an issue</a> <!-- -->or<!-- --> <a href=\\\"https://www.helicone.ai/contact\\\" rel=\\\"noopener\\\" target=\\\"_blank\\\">contact us</a>, we'd love to hear from you!</p></section></article></div>\"\n}"
    }
  ],
  "details": {
    "url": "https://www.helicone.ai/blog/browser-use-vs-computer-use-vs-operator",
    "title": "The Best Web Agents: Computer Use vs Operator vs Browser Use",
    "summary": "Comprehensive comparison of Computer Use, Operator, and Browser Use AI agents for web automation, and how to choose the best web agent in 2025.",
    "content": "<div id=\"readability-page-1\" class=\"page\"><article><p>The relentless hype around AI agents has given birth to some rather remarkable tools.</p>\n<p>These so-called \"browser automation\" agents can interact with digital interfaces just like humans—clicking buttons, filling forms, navigating websites, and manipulating applications—representing a major leap forward for automation.</p>\n<p><img alt=\"Browser Use vs Computer Use vs Operator\" fetchpriority=\"high\" width=\"800\" height=\"450\" decoding=\"async\" data-nimg=\"1\" srcset=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrower-automation-cover.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrower-automation-cover.webp&amp;w=1920&amp;q=75 2x\" src=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrower-automation-cover.webp&amp;w=1920&amp;q=75\"></p>\n<p>In this comparison, we'll examine the best ones: <strong>Anthropic's Computer Use</strong>, <strong>OpenAI's Operator</strong>, and <strong>Browser Use</strong> and help you decide which one to use.</p>\n<h2 id=\"table-of-contents\">Table of Contents</h2>\n<ul>\n<li><a href=\"#quick-comparison\">Quick Comparison</a></li>\n<li><a href=\"#how-browser-use-works\">How Browser Use Works</a></li>\n<li><a href=\"#how-claude-computer-use-works\">How Claude Computer Use Works</a></li>\n<li><a href=\"#how-openai-operator-works\">How OpenAI Operator Works</a></li>\n<li><a href=\"#web-agent-feature-comparison\">Web Agent Feature Comparison</a></li>\n<li><a href=\"#technical-implementation\">Technical Implementation</a></li>\n<li><a href=\"#pricing-and-accessibility\">Pricing and Accessibility</a></li>\n<li><a href=\"#what-real-users-are-saying\">What Real Users Are Saying</a></li>\n<li><a href=\"#developer-experience\">Developer Experience</a></li>\n<li><a href=\"#safety-considerations\">Safety Considerations</a></li>\n<li><a href=\"#so-which-browser-automation-tool-should-you-choose\">So Which Browser Automation Tool Should You Choose?</a></li>\n</ul>\n<h2 id=\"quick-comparison\">Quick Comparison</h2>\n<div><table><thead><tr><th>Feature</th><th>Browser Use</th><th>Computer Use</th><th>Operator</th></tr></thead><tbody><tr><td><strong>Provider</strong></td><td>Open-source</td><td>Anthropic (Claude)</td><td>OpenAI</td></tr><tr><td><strong>Release Date</strong></td><td>Late 2024</td><td>October 2024</td><td>January 2025</td></tr><tr><td><strong>Access</strong></td><td>Self-hosted or Cloud API</td><td>API only</td><td>Web interface</td></tr><tr><td><strong>Pricing</strong></td><td>Open source or $0.05/step (Cloud)</td><td>Uses Claude API pricing</td><td>Pro subscription ($200/mo)</td></tr><tr><td><strong>Model Support</strong></td><td>Multiple (OpenAI, Claude, Gemini, etc.)</td><td>Claude 3.5 Sonnet</td><td>CUA (Computer-Using Agent)</td></tr><tr><td><strong>Multi-Tab Support</strong></td><td>✅</td><td>✅</td><td>✅</td></tr><tr><td><strong>Element Tracking</strong></td><td>✅</td><td>✅</td><td>✅</td></tr><tr><td><strong>Customization</strong></td><td>Extensive</td><td>Limited</td><td>Limited</td></tr></tbody></table></div>\n<h3 id=\"benchmark-performance\">Benchmark Performance</h3>\n<p>Comparing the leading web agents, Claude Computer Use and OpenAI Operator, the benchmarks indicate that while Operator currently leads in web navigation tasks, Anthropic's Computer Use demonstrates superior performance in coding and software development tasks.</p>\n<p>However, Browser Use is a more recent entrant to the market, showing promising results that surpass the Operator in WebVoyager, according to its <a href=\"https://browser-use.com/posts/sota-technical-report\" target=\"_blank\" rel=\"noopener noreferrer\">Technical Report</a>. It has not yet been tested on the other benchmarks.</p>\n<p><img alt=\"Benchmark Performance\" loading=\"lazy\" width=\"800\" height=\"450\" decoding=\"async\" data-nimg=\"1\" srcset=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-automation-benchmarks.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-automation-benchmarks.webp&amp;w=1920&amp;q=75 2x\" src=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-automation-benchmarks.webp&amp;w=1920&amp;q=75\"></p>\n<div><table><thead><tr><th></th><th>Browser Use</th><th>Computer Use</th><th>Operator</th></tr></thead><tbody><tr><td><strong>WebVoyager</strong> <br> Tests web agent accuracy on diverse web tasks</td><td>⭐️ 89%</td><td>56%</td><td>87%</td></tr><tr><td><strong>OSWorld</strong> <br> Evaluates ability to use computer operating systems</td><td>-</td><td>22%</td><td>38.1%</td></tr></tbody></table></div>\n<div><h2>Looking to monitor your AI Agents? ⚡️</h2><p>Helicone can help you trace agentic workflows, monitor costs and various performance metrics for your AI agents in production.</p></div>\n<h2 id=\"how-browser-use-works\">How Browser Use Works</h2>\n<p><img alt=\"Browser Use\" loading=\"lazy\" width=\"800\" height=\"450\" decoding=\"async\" data-nimg=\"1\" srcset=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-use.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-use.webp&amp;w=1920&amp;q=75 2x\" src=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fbrowser-use.webp&amp;w=1920&amp;q=75\"></p>\n<p>Browser Use is an <a href=\"https://github.com/browser-use/browser-use\" target=\"_blank\" rel=\"noopener noreferrer\">open-source framework</a> that creates a bridge between LLMs and browsers. It's designed with flexibility in mind, supporting multiple models and customization options.</p>\n<p>Browser Use works by:</p>\n<ol>\n<li>Converting screenshots to text and HTML data</li>\n<li>Feeding this data to an LLM for decision-making</li>\n<li>Executing the LLM's commands through a browser automation layer</li>\n<li>Repeating until the task is complete</li>\n</ol>\n<h2 id=\"how-claude-computer-use-works\">How Claude Computer Use Works</h2>\n<p><img alt=\"Computer Use\" loading=\"lazy\" width=\"800\" height=\"450\" decoding=\"async\" data-nimg=\"1\" srcset=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fclaude-computer-use.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fclaude-computer-use.webp&amp;w=1920&amp;q=75 2x\" src=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fclaude-computer-use.webp&amp;w=1920&amp;q=75\"></p>\n<p>Anthropic's <a href=\"https://www.anthropic.com/news/3-5-models-and-computer-use\" target=\"_blank\" rel=\"noopener noreferrer\">Computer Use</a> gives Claude the ability to control computers by \"looking\" at the screen and performing actions with virtual keyboard and mouse inputs.</p>\n<p>Here's how it works:</p>\n<ol>\n<li>Takes screenshots of the entire desktop</li>\n<li>Claude analyzes the visual information</li>\n<li>Makes decisions on what actions to take</li>\n<li>Reports back with results after executing the actions</li>\n</ol>\n<h2 id=\"how-openai-operator-works\">How OpenAI Operator Works</h2>\n<p><img alt=\"Operator\" loading=\"lazy\" width=\"800\" height=\"450\" decoding=\"async\" data-nimg=\"1\" srcset=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fopenai-operator.webp&amp;w=828&amp;q=75 1x, https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fopenai-operator.webp&amp;w=1920&amp;q=75 2x\" src=\"https://www.helicone.ai/_next/image?url=%2Fstatic%2Fblog%2Fbrowser-use-vs-computer-use-vs-operator%2Fopenai-operator.webp&amp;w=1920&amp;q=75\"></p>\n<p>OpenAI's Operator is powered by their <a href=\"https://openai.com/index/computer-using-agent/\" target=\"_blank\" rel=\"noreferrer\">Computer-Using Agent (CUA)</a> model. It's designed as a standalone agent that runs in its own browser.</p>\n<p>The Operator workflow:</p>\n<ol>\n<li>User requests a task via the Operator interface</li>\n<li>Operator launches its own browser instance</li>\n<li>The CUA model controls this browser to complete tasks</li>\n<li>User can intervene at critical points (takeover mode)</li>\n</ol>\n<h2 id=\"web-agent-feature-comparison\">Web Agent Feature Comparison</h2>\n<p>Now let's dive deeper into specific features and capabilities:</p>\n<div><table><thead><tr><th><strong>Capability</strong></th><th><strong>Browser Use</strong></th><th><strong>Computer Use</strong></th><th><strong>Operator</strong></th></tr></thead><tbody><tr><td><strong>Web Navigation</strong></td><td>⭐⭐⭐</td><td>⭐⭐</td><td>⭐⭐⭐</td></tr><tr><td><strong>Form Filling</strong></td><td>⭐⭐⭐</td><td>⭐⭐</td><td>⭐⭐⭐</td></tr><tr><td><strong>Data Extraction</strong></td><td>⭐⭐⭐</td><td>⭐</td><td>⭐⭐</td></tr><tr><td><strong>Error Recovery</strong></td><td>⭐⭐</td><td>⭐</td><td>⭐⭐⭐</td></tr><tr><td><strong>Multi-Step Tasks</strong></td><td>⭐⭐⭐</td><td>⭐⭐</td><td>⭐⭐⭐</td></tr><tr><td><strong>Visual Understanding</strong></td><td>⭐⭐</td><td>⭐⭐⭐</td><td>⭐⭐⭐</td></tr></tbody></table></div>\n<h2 id=\"technical-implementation\">Technical Implementation</h2>\n<div><table><thead><tr><th>Feature</th><th>Browser Use</th><th>Computer Use</th><th>Operator</th></tr></thead><tbody><tr><td><strong>Architecture</strong></td><td>Python framework with Playwright</td><td>API integration with Claude</td><td>Standalone agent with own browser</td></tr><tr><td><strong>Model Integration</strong></td><td>Any LangChain models</td><td>Claude 3.5 Sonnet only</td><td>Custom CUA model</td></tr><tr><td><strong>Vision/HTML Extraction</strong></td><td>Both</td><td>Vision-focused</td><td>Both</td></tr><tr><td><strong>Multi-Tab Support</strong></td><td>Full support</td><td>Supported</td><td>Full support</td></tr><tr><td><strong>Element Tracking</strong></td><td>Advanced</td><td>Basic</td><td>Advanced</td></tr><tr><td><strong>Custom Functions</strong></td><td>Extensive</td><td>Limited</td><td>Limited</td></tr><tr><td><strong>Error Handling</strong></td><td>Good</td><td>Basic</td><td>Advanced</td></tr></tbody></table></div>\n<div><h2>💡 Key Insights</h2><p>Browser Use stands out for flexibility in model choice and customization options, Operator provides the best user experience, while Computer Use offers the deepest visual understanding.</p></div>\n<h2 id=\"pricing-and-accessibility\">Pricing and Accessibility</h2>\n<div><table><thead><tr><th></th><th><strong>Browser Use</strong></th><th><strong>Computer Use</strong></th><th><strong>Operator</strong></th></tr></thead><tbody><tr><td><strong>Pricing Model</strong></td><td>Open Source + Cloud API</td><td>API Only</td><td>Subscription</td></tr><tr><td><strong>Cost Details</strong></td><td>• <strong>Free</strong> to self-host<br>• Cloud API: $0.05 per step</td><td>• Input: $3.00 per million tokens<br>• Output: $15.00 per million tokens</td><td>• Requires $200/month OpenAI Pro subscription<br></td></tr><tr><td><strong>Accessibility</strong></td><td>Available to anyone via GitHub or cloud API</td><td>Available to developers via API</td><td>Limited to US Pro users initially</td></tr></tbody></table></div>\n<p><em>OpenAI has stated that it plans to expose CUA in the API soon so that developers can use it to build their own computer-using agents.</em></p>\n<h2 id=\"what-real-users-are-saying\">What Real Users Are Saying</h2>\n<p>While benchmarks provide a standardized comparison, real-world user experiences offer much more valuable insights into how these tools actually perform in practice.</p>\n<p>Here's a summary of these tools' real-world performance based on reviews across the internet and our own experience:</p>\n<h3 id=\"common-themes-from-user-feedback\">Common themes from user feedback</h3>\n<div><table><thead><tr><th>Aspect</th><th>User Observations</th></tr></thead><tbody><tr><td><strong>Reliability</strong></td><td>All tools still have occasional failures with complex interfaces. Browser Use appears most reliable for web-specific tasks, while Operator has stronger error recovery.</td></tr><tr><td><strong>Cost vs. Value</strong></td><td>Browser Use offers the best value for developers (free self-hosted option). Computer Use's token-based pricing can become expensive for frequent use. Operator's subscription model works well for regular users.</td></tr><tr><td><strong>Learning Curve</strong></td><td>Browser Use requires the most technical knowledge but offers the greatest flexibility. Operator has the simplest user experience. Computer Use falls somewhere in the middle.</td></tr><tr><td><strong>Practical Limitations</strong></td><td>All tools struggle with CAPTCHAs, some dynamic content, and highly complex workflows. Users often need to intervene for sensitive operations.</td></tr></tbody></table></div>\n<h2 id=\"developer-experience\">Developer Experience</h2>\n<p>The developer experience differs significantly across platforms:</p>\n<div><table><thead><tr><th><strong>Platform</strong></th><th><strong>Developer Experience</strong></th></tr></thead><tbody><tr><td><strong>Browser Use</strong></td><td>- Highly customizable with extensive integration options <br> - Supports any LangChain-compatible LLM <br> - Allows custom functions and callbacks <br> - Configurable browser behavior and agent processing of visual/HTML data</td></tr><tr><td><strong>Computer Use</strong></td><td>- Focuses on simplicity but lacks customization <br> - Requires Claude 3.5 Sonnet <br> - Limited to API integration with fixed behavior <br> - No support for custom actions</td></tr><tr><td><strong>Operator</strong></td><td>- Prioritizes user experience over developer flexibility <br> - No direct API (coming soon) <br> - Limited customization options <br> - Strong emphasis on safety and user control <br> - Simple web interface</td></tr></tbody></table></div>\n<h2 id=\"safety-considerations\">Safety Considerations</h2>\n<p>All three platforms implement safety measures, but with different approaches:</p>\n<div><table><thead><tr><th><strong>Platform</strong></th><th><strong>Safety Considerations</strong></th></tr></thead><tbody><tr><td><strong>Browser Use</strong></td><td>- Open framework; safety is largely developer-dependent <br> - Supports restricting operations to specific domains <br> - Allows integration of custom middleware for additional security checks</td></tr><tr><td><strong>Computer Use</strong></td><td>- Implements built-in safety classifiers <br> - Employs automatic monitoring to prevent misuse <br> - Usage is restricted to approved cases</td></tr><tr><td><strong>Operator</strong></td><td>- Features a three-layer safety system: <br> &nbsp;&nbsp;• User control via takeover mode <br> &nbsp;&nbsp;• Data privacy controls <br> &nbsp;&nbsp;• Defenses against adversarial websites <br> - Monitors for suspicious behavior</td></tr></tbody></table></div>\n<h2 id=\"so-which-browser-automation-tool-should-you-choose\">So Which Browser Automation Tool Should You Choose?</h2>\n<p><strong>Choose Browser Use if:</strong></p>\n<ul>\n<li>You need maximum flexibility and customization</li>\n<li>You want to self-host or use multiple AI models</li>\n<li>Developer experience and extensibility are priorities</li>\n<li>Cost-efficiency is important</li>\n</ul>\n<p><strong>Choose Computer Use if:</strong></p>\n<ul>\n<li>You need to control desktop applications beyond browsers</li>\n<li>Visual understanding of interfaces is critical</li>\n<li>You need robust safety features</li>\n</ul>\n<p><strong>Choose Operator if:</strong></p>\n<ul>\n<li>You want a simple, user-friendly experience</li>\n<li>You need strong built-in safety features</li>\n<li>You're already an OpenAI Pro subscriber ($200/month)</li>\n</ul>\n<h3 id=\"you-might-find-these-useful\">You might find these useful:</h3>\n<ul>\n<li><strong><a href=\"http://helicone.ai/blog/crewai-vs-dify-ai\" target=\"_blank\">Comparing CrewAI vs. Dify - Which is the Best AI Agent Framework?</a></strong></li>\n<li><strong><a href=\"http://helicone.ai/blog/claude-3.5-sonnet-vs-openai-o1\" target=\"_blank\">Claude 3.5 Sonnet vs OpenAI o1: A Comprehensive Comparison</a></strong></li>\n<li><strong><a href=\"http://helicone.ai/blog/debugging-chatbots-and-ai-agents-with-sessions\" target=\"_blank\">Debugging RAG Chatbots and AI Agents with Sessions</a></strong></li>\n</ul>\n<section><h2>Frequently Asked Questions</h2></section>\n<hr><section><h3>Questions or feedback?</h3><p>Are the information out of date? Please<!-- --> <a href=\"https://github.com/Helicone/helicone/issues\" rel=\"noopener\" target=\"_blank\">raise an issue</a> <!-- -->or<!-- --> <a href=\"https://www.helicone.ai/contact\" rel=\"noopener\" target=\"_blank\">contact us</a>, we'd love to hear from you!</p></section></article></div>"
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font-size: var(--chart-font-size); fill: rgb(54, 65, 82); pointer-events: none;\\\">37.8%</text></g></g></svg></p></div><p>For benchmark methodology details, read the <a href=\\\"#ui-grounding-benchmark-why-vision-quality-matters\\\" data-type=\\\"internal\\\" data-id=\\\"#ui-grounding-benchmark-why-vision-quality-matters\\\" aria-label=\\\"benchmark details\\\">benchmark details</a>.</p><ul>\\n<li><strong>Qwen3-VL models</strong> reach <strong>~90% accuracy</strong>, with low error (≈7–9 px).</li>\\n\\n\\n\\n<li><strong>UI-specialized models</strong> like <strong>UI-TARS</strong> perform much worse (<strong>~38% accuracy</strong>) and show high variance and large errors, especially on state-dependent and dense interfaces.</li>\\n\\n\\n\\n<li>State-dependent and dense UIs are the hardest cases for most models.</li>\\n</ul><h2 id=\\\"top-computer-use-agents\\\">Top computer use agents</h2><div data-module-name=\\\"table\\\"><table><thead><tr><th scope=\\\"col\\\"><p>Agent</p></th><th scope=\\\"col\\\"><p>Architecture</p></th><th scope=\\\"col\\\"><p>Runtime environment</p></th><th scope=\\\"col\\\"><p>Local system access</p></th></tr></thead><tbody><tr><th scope=\\\"row\\\"><div><p>Claude Cowork</p></div></th><td><div><p>End-to-End</p></div></td><td><div><p>Local visual workspace environment</p></div></td><td><div><p>✅</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>OpenAI Computer use preview</p></div></th><td><div><p>End-to-End</p></div></td><td><div><p>Cloud‑hosted agent runtime via API</p></div></td><td><div><p>❌</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>Anthropic Claude Computer Use</p></div></th><td><div><p>End-to-End</p></div></td><td><div><p>Local or cloud sandbox (API client controls a VM)</p></div></td><td><div><p>❌</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>Open Interpreter (OS Mode)</p></div></th><td><div><p>Composed</p></div></td><td><div><p>Local OS-level runtime</p></div></td><td><div><p>✅</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>Simular Agent S/S3</p></div></th><td><div><p>Composed</p></div></td><td><div><p>Framework can run locally or hosted (open‑source S/S3 runs locally)</p></div></td><td><div><p>❌ (but local execution possible via open‑source framework)</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>Cua AI</p></div></th><td><div><p>Composed</p></div></td><td><div><p>Cloud sandbox + local integration options</p></div></td><td><div><p>❌</p></div></td></tr></tbody></table></div><p>See the <a href=\\\"#explanation-of-key-computer-use-agent-features\\\" data-type=\\\"internal\\\" data-id=\\\"#explanation-of-key-computer-use-agent-features\\\" aria-label=\\\"features\\\">features</a> section for features in the table, and examine the <a href=\\\"#two-architectural-approaches-to-computer-use-models\\\" data-type=\\\"internal\\\" data-id=\\\"#two-architectural-approaches-to-computer-use-models\\\" aria-label=\\\"architectural approaches\\\">architectural approaches</a> section for the details of the computer use agents’ architecture.</p><h3 id=\\\"openai-computer-use-preview\\\"><strong>OpenAI Computer Use Preview</strong></h3><p>OpenAI’s computer use-preview is a specialized model built to understand and execute computer tasks via the Responses API. It focuses on text input and output, with optional image input, but does not support audio or video.</p><h3 id=\\\"anthropic-claude-computer-use\\\"><strong>Anthropic Claude Computer Use</strong></h3><p>Claude Computer Use is a beta feature that enables Claude to interact with a desktop or windowed computer environment, just like a person would. It works by seeing the screen, moving the mouse, and typing on the keyboard.</p><p>Claude cannot act on its own without a developer’s setup. It does not automatically access your real computer; it interacts with the sandbox you provide.</p><h3 id=\\\"open-interpreter-os-mode\\\"><strong>Open Interpreter (OS Mode)</strong></h3><p>Open Interpreter is an open-source terminal agent that capable of running code and interacting with your system.</p><p>It runs on your own computer, so it can use your files, programs, and browser directly. Users communicate with it in plain English, and it translates their instructions into actions by generating and executing code. Before any code runs, Open Interpreter displays what it plans to run and requests your approval.</p><h3 id=\\\"simular-agent-s-s3\\\"><strong>Simular Agent S/S3</strong></h3><p>Simular Agent S3 is a computer use agent that works by observing screens, planning actions, and controlling the mouse and keyboard to complete complex tasks. It is part of the open Agent S framework for autonomous GUI interaction.</p><p>Behavior Best-of-N (bBoN) is a core method that enables Agent S3 to generate multiple possible action sequences (“rollouts”), rather than a single run. It turns each rollout into a behavior narrative, which is a simple summary of what happened. A separate judgment step then chooses the best run.</p><h3 id=\\\"cua-ai\\\"><strong>Cua AI</strong></h3><p>Cua AI is an open-source framework that enabler to build, run, and test computer use <a data-wpil-monitor-id=\\\"1363\\\" href=\\\"https://aimultiple.com/ai-agents\\\" aria-label=\\\"AI\\\">AI </a><a target=\\\"_blank\\\" rel=\\\"noopener\\\" href=\\\"https://aimultiple.com/ai-agents\\\" aria-label=\\\"agents\\\">agents</a> across desktop environments by tying vision models, reasoning models, and sandboxed OS environments into one system. Cua can run agents in the cloud using remote sandboxes. It also lets you run them locally if you want more control or privacy.</p><p>Cua also helps you generate UI screenshots and agent action logs. You can record multi-step interactions, make training data, and run benchmarks to see how well agents perform.</p><h3 id=\\\"claude-cowork\\\"><strong>Claude Cowork</strong></h3><p>Claude Cowork is a way to have <em>Claude</em> do complex work directly on your computer. It utilizes the same agent design as Claude Code, but with a focus on tasks that involve your local files and programs, rather than just providing short chat responses. This feature is in research preview and runs inside the Claude Desktop app for macOS.</p><p><strong>Current Limitations:</strong></p><ul>\\n<li>Only available on macOS Desktop.</li>\\n\\n\\n\\n<li>Claude does not keep memory across sessions.</li>\\n\\n\\n\\n<li>Cowork cannot share its work with others yet.</li>\\n</ul><h2 id=\\\"osworld-benchmark\\\">OSWorld benchmark</h2><h3 id=\\\"results-for-computer-use-agentic-ai\\\">Results for computer use agentic AI</h3><div data-module-name=\\\"table\\\"><table><thead><tr><th scope=\\\"col\\\"><p>Rank</p></th><th scope=\\\"col\\\"><p>Model &amp; Date</p></th><th scope=\\\"col\\\"><p>Approach &amp; Details</p></th><th scope=\\\"col\\\"><p>Success Rate (Avg±Std)</p></th></tr></thead><tbody><tr><th scope=\\\"row\\\"><div><p>1</p></div></th><td><div><p>agent s3 w/ Opus 4.5 + GPT-5 bBoN (N=10)<br>\\nSimular</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 100<br>\\nRuns: 1</p></div></td><td><div><p>72.6%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>2</p></div></th><td><div><p>agent s3 w/ GPT-5 bBoN (N=10)<br>\\nSimular</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 100<br>\\nRuns: 1</p></div></td><td><div><p>69.9%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>3</p></div></th><td><div><p>UiPath Screen Agent w/ Opus 4.5<br>\\nUiPath</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 100<br>\\nRuns: 1</p></div></td><td><div><p>67.1%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>4</p></div></th><td><div><p>agent s3 w/ Opus 4.5 bBoN (N=1)<br>\\nSimular</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 100<br>\\nRuns: 5</p></div></td><td><div><p>66.0%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>5</p></div></th><td><div><p>OS-Symphony w/ GPT-5<br>\\nShanghai AI Laboratory</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 50<br>\\nRuns: 1</p></div></td><td><div><p>65.8%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>6</p></div></th><td><div><p>UiPath Screen Agent w/ Opus 4.5<br>\\nUiPath</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 50<br>\\nRuns: 1</p></div></td><td><div><p>64.4%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>7</p></div></th><td><div><p>GBOX Agent<br>\\nGBOX.AI</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 15<br>\\nRuns: 1</p></div></td><td><div><p>64.2%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>8</p></div></th><td><div><p>GTA1 w/ GPT-5<br>\\nSalesforce &amp; The Australian National University &amp; The University of Hong Kong</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 100<br>\\nRuns: 1</p></div></td><td><div><p>63.4%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>9</p></div></th><td><div><p>claude-sonnet-4-5-20250929<br>\\nAnthropic</p></div></td><td><div><p>Type: General model<br>\\nMax Steps: 100<br>\\nRuns: 1</p></div></td><td><div><p>62.9%</p></div></td></tr><tr><th scope=\\\"row\\\"><div><p>10</p></div></th><td><div><p>agent s3 w/ GPT-5 bBoN (N=1)<br>\\nSimular</p></div></td><td><div><p>Type: Agentic framework<br>\\nMax Steps: 100<br>\\nRuns: 10</p></div></td><td><div><p>62.6%</p></div></td></tr></tbody></table></div><p><strong>Disclaimer:</strong> The same model may appear at different ranks because OSWorld lists results by full evaluation configuration (<a target=\\\"_blank\\\" rel=\\\"noopener\\\" href=\\\"https://aimultiple.com/agentic-frameworks\\\" aria-label=\\\"agent framework\\\">agent framework</a>, grounding or planning model, Best-of-N setting, run count, and step limit), and even small changes in these settings are treated as separate entries with different performance outcomes.</p><h3 id=\\\"methodology\\\">Methodology</h3><p>The benchmark includes 369 real-world tasks (or 361 excluding Google Drive tasks that require manual setup). Tasks span web and desktop applications, OS file operations, and multi-app workflows. Each task starts from a reproducible initial state and is paired with a custom execution-based evaluation script, ensuring reliable scoring.</p><h4 id=\\\"evaluation-process\\\">Evaluation process</h4><p>Agents interact with a live OS environment. Success is measured by what the agent actually does, not by text outputs. Environments support parallel and headless execution, enabling scalable testing.</p><h4 id=\\\"benchmark-scope\\\">Benchmark scope</h4><p>OSWorld supports open-ended tasks across arbitrary applications, multimodal inputs, cross-app workflows, and intermediate starting states. Compared to prior benchmarks, it offers broader coverage and more realistic conditions.</p><h4 id=\\\"baselines-and-analysis\\\">Baselines and analysis</h4><p>The benchmark evaluates general models, specialized models, and agentic frameworks across LLM and VLM families. Results show a large gap between human performance (~72%) and current agents, highlighting challenges in GUI grounding and operational knowledge. OSWorld also enables detailed analysis across task types, UI complexity, inputs, and operating systems.</p><h2 id=\\\"two-architectural-approaches-to-computer-use-models\\\">Two architectural approaches to computer use models</h2><p>Today, most computer use agents fall into one of two design patterns:</p><ul>\\n<li>End-to-End (E2E) Agents</li>\\n\\n\\n\\n<li>Composed Agents</li>\\n</ul><p>Both aim to complete tasks on a computer. They differ in how they divide perception, reasoning, and action.</p><h2 id=\\\"end-to-end-e2e-agents\\\">End-to-End (E2E) agents</h2><p>End-to-end agents use one vision-language model to handle the entire loop. The model receives a screenshot and a task description. It then outputs the next action directly.</p><p>There is no clear boundary between seeing, reasoning, and acting. These processes are learned together inside the same model.</p><h3 id=\\\"how-e2e-agents-work\\\">How E2E agents work</h3><p><strong>Screenshot + Task → Unified Representation → Action</strong></p><p>The model reasons directly over pixels and text. It does not build an explicit list of buttons or fields. Instead, it learns associations between visual patterns and actions during training.</p><h4 id=\\\"strengths\\\">Strengths</h4><ul>\\n<li>Simpler system design</li>\\n\\n\\n\\n<li>Fewer integration points where errors can occur</li>\\n\\n\\n\\n<li>Often more stable over long tasks</li>\\n</ul><h4 id=\\\"limitations\\\">Limitations</h4><ul>\\n<li>Limited visibility into why an action was chosen</li>\\n\\n\\n\\n<li>Harder to debug when something goes wrong</li>\\n\\n\\n\\n<li>Less control over intermediate reasoning steps</li>\\n</ul><h4 id=\\\"practical-implications\\\">Practical implications</h4><p>Because perception and planning are tightly linked, small visual errors are less likely to cascade into full failures. When an action does not work, the agent can re-evaluate the updated screen and adapt.</p><p><strong>Trade-off: </strong>It is difficult to inspect intermediate decisions or isolate the source of failures.</p><h2 id=\\\"composed-agents\\\">Composed agents</h2><p>Composed agents divide the interaction loop into separate stages. Each stage is handled by a different model or subsystem.</p><h3 id=\\\"how-composed-ai-agents-work\\\">How composed AI agents work</h3><p>A typical pipeline looks like this:</p><ol>\\n<li><strong>Grounding:</strong> Detect graphical user interface elements from the screenshot</li>\\n\\n\\n\\n<li><strong>Planning:</strong> Decide what to do next</li>\\n\\n\\n\\n<li><strong>Execution:</strong> Perform tasks on the system</li>\\n</ol><p>This design makes each step explicit.</p><h4 id=\\\"strengths-2\\\">Strengths</h4><ul>\\n<li>Clear separation of responsibilities</li>\\n\\n\\n\\n<li>Easier to inspect intermediate outputs</li>\\n\\n\\n\\n<li>Better suited for research and controlled experiments</li>\\n</ul><h4 id=\\\"limitations-2\\\">Limitations</h4><ul>\\n<li>Higher system complexity</li>\\n\\n\\n\\n<li>Errors can propagate between components</li>\\n\\n\\n\\n<li>Often less reliable in real desktop environments</li>\\n</ul><h4 id=\\\"practical-implications-2\\\">Practical implications</h4><p>Composed agents rely on structured representations of the screen, such as detected buttons or text fields. This improves transparency but adds fragility. If grounding is inaccurate, planning decisions are likely to fail.</p><p><strong>Trade-off</strong>: Long tasks are especially challenging. Small mismatches between perceived and actual screen state can accumulate over time.</p><h2 id=\\\"core-building-blocks-of-computer-using-agents-cuas\\\">Core building blocks of computer-using agents (CUAs)</h2><p>Modern computer use agents are built using three main components:</p><h3 id=\\\"1-vision-language-models-vlms\\\"><strong>1. Vision-language models (VLMs)</strong></h3><p>Single VLMs form the core of most end-to-end agents. They process screenshots and instructions together and output actions directly.</p><p><strong>Screenshot + Task → Joint Vision-Language Space → Action</strong></p><p>The model encodes visual and textual inputs into a shared internal space. In this space, it learns how visual patterns relate to actions without explicit labels.</p><p>There is no separate grounding step. UI understanding and task planning occur implicitly and simultaneously.</p><p><strong>Practical implications: </strong>Single VLMs reduce architectural complexity and limit the propagation of errors. They favor robustness and simplicity over transparency and fine-grained control.</p><h3 id=\\\"2-grounding-models\\\"><strong>2. Grounding models</strong></h3><p>Grounding models focus solely on perception and play a crucial role in the composed agents. Their job is to translate raw screenshots into structured descriptions of the computer interface. They do not reason about goals or select actions.</p><div data-module-name=\\\"image\\\"><p><img alt=\\\"how grounding model is used in computer use agents\\\" title=\\\"how grounding model is used in computer use agents\\\" loading=\\\"eager\\\" width=\\\"700\\\" height=\\\"400\\\" decoding=\\\"async\\\" data-nimg=\\\"1\\\" src=\\\"https://aimultiple.com/wp-content/uploads/2026/01/grounding-madel-1400x539.png.webp\\\"></p></div><p><strong>Screenshot → Grounding Model → Structured UI Representation</strong></p><p>Outputs often include:</p><ul>\\n<li>Detected UI elements</li>\\n\\n\\n\\n<li>Spatial locations (bounding boxes)</li>\\n\\n\\n\\n<li>Semantic labels (button, input field, text)</li>\\n\\n\\n\\n<li>Extracted text</li>\\n</ul><p>This representation is passed to a planning model.</p><h4 id=\\\"strengths-3\\\">Strengths</h4><ul>\\n<li>Clear and inspectable perception</li>\\n\\n\\n\\n<li>Easier to log and analyze failures</li>\\n\\n\\n\\n<li>Improved transparency</li>\\n</ul><h4 id=\\\"limitations-3\\\">Limitations</h4><ul>\\n<li>Errors propagate downstream</li>\\n\\n\\n\\n<li>Sensitive to visual changes and dynamic layouts</li>\\n\\n\\n\\n<li>Difficult to maintain consistency over many steps</li>\\n</ul><p><strong>Practical implications: </strong>Grounding is often the weakest link in composed systems. Missing or outdated elements can mislead planning models and cause repeated failures.</p><h3 id=\\\"ui-grounding-benchmark-why-vision-quality-matters\\\">UI Grounding benchmark: Why vision quality matters</h3><p>To isolate the role of visual perception, we reference a focused UI grounding benchmark that evaluates how well models identify the exact pixel location of a UI element from a natural-language instruction.</p><h4 id=\\\"benchmark-setup\\\">Benchmark setup</h4><ul>\\n<li>100 desktop screenshots</li>\\n\\n\\n\\n<li>4 task types: simple, relational, state-dependent, dense UI</li>\\n\\n\\n\\n<li>5 runs per sample to measure consistency</li>\\n\\n\\n\\n<li>Fixed resolution: 2560×1440</li>\\n</ul><p>For a more detailed dataset and methodology, visit <a target=\\\"_blank\\\" rel=\\\"noopener\\\" href=\\\"https://huggingface.co/datasets/ai-multiple/aim-ui-grounding\\\" aria-label=\\\"AIMultiple UI Grounding\\\">AIMultiple UI Grounding</a> on HuggingFace.</p><p><strong>Takeaway</strong><br>Accurate UI grounding remains a major bottleneck. Current evidence shows that robust visual perception and implicit UI understanding matter more than narrow UI specialization, especially for reliable computer-use agents operating real desktops.</p><h3 id=\\\"planning-models\\\"><strong>Planning models</strong></h3><p>Planning models determine the next steps. They work with structured UI data, task goals, and interaction history. They do not process raw images. These models play a crucial role in the composed agent architecture.</p><p><strong>Structured UI + Task Goal → Planning Model → Next Action</strong></p><p>Planning models can:</p><ul>\\n<li>Break tasks into steps</li>\\n\\n\\n\\n<li>Track progress</li>\\n\\n\\n\\n<li>Apply rules or heuristics</li>\\n\\n\\n\\n<li>Log reasoning explicitly</li>\\n</ul><h4 id=\\\"challenges-in-practice\\\">Challenges in practice</h4><ul>\\n<li><strong>High sensitivity to input errors</strong><br>Incorrect grounding leads to faulty plans.</li>\\n\\n\\n\\n<li><strong>State drift over time</strong><br>UI changes can invalidate earlier assumptions.</li>\\n\\n\\n\\n<li><strong>Limited failure recovery</strong><br>Without strong feedback, planners may loop or stall.</li>\\n\\n\\n\\n<li><strong>Execution mismatches</strong><br>Timing, focus, or coordination errors can break plans.</li>\\n</ul><p><strong>Practical implications: </strong>Planning models add structure and transparency, but their effectiveness depends heavily on accurate perception and reliable execution.</p><h2 id=\\\"explanation-of-key-computer-use-agent-features\\\">Explanation of key computer use agent features</h2><h3 id=\\\"runtime-environment\\\">Runtime environment</h3><p>It defines where the computer-use agent runs and how it controls the operating system (cloud VM, local machine, or container-based runtime).</p><h3 id=\\\"local-system-access\\\">Local system access</h3><p>This shows whether the agent can read or write files on the user’s actual machine, not just in a remote sandbox. Local access is useful for personal workflows but raises higher security concerns.</p><h2 id=\\\"what-is-the-overall-trade-off-between-e2e-and-composed-agents\\\">What is the overall trade-off between E2E and composed agents?</h2><p>End-to-end agents are currently more reliable for direct use on personal computers. Their unified design reduces coordination issues and failure points.</p><p>Composed agents are not inherently weaker. They offer greater flexibility, customization, and interpretability. However, they require stronger grounding, tighter state management, and careful integration to perform well in real environments.</p><p>The core trade-off is not capability, but <strong>robustness versus control</strong>.</p><h2 id=\\\"what-are-computer-use-agents\\\">What are computer use agents?</h2><p>Computer use agents are systems designed to operate a computer in a manner similar to a human. They look at the screen, decide what to do, and interact through actions such as clicking, typing, and scrolling.</p><p>At first glance, this sounds simple. In practice, it is difficult. Desktop environments are dynamic. Interfaces change often. There are no fixed APIs or stable structures to rely on. These agents must work from what they see on the screen and reason about it in real time.</p><p>Despite different implementations, most computer use agents follow the same basic loop:</p><p><strong>Observe → Interpret → Decide → Execute</strong></p><p>How this loop is implemented determines how stable, flexible, and reliable an agent is in real use.</p><div><div><p><img alt=\\\"Cem Dilmegani\\\" loading=\\\"lazy\\\" width=\\\"56\\\" height=\\\"56\\\" decoding=\\\"async\\\" data-nimg=\\\"1\\\" src=\\\"https://aimultiple.com/wp-content/uploads/2024/07/headshot-of-Cem-Dilmegani-160x160.png.webp\\\"></p><div><p>Cem Dilmegani</p><p>Principal Analyst</p></div></div><div><p>Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.</p><p>Cem's work has been cited by leading global publications including Business Insider,  Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.</p><p>Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey &amp; Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.</p><p>He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. </p><p>Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.</p></div><p><a href=\\\"https://aimultiple.com/author/cem-dilmegani\\\" target=\\\"_blank\\\">View Full Profile<span><svg xmlns=\\\"http://www.w3.org/2000/svg\\\" width=\\\"1em\\\" height=\\\"1em\\\" fill=\\\"none\\\" viewBox=\\\"0 0 24 24\\\"><path fill=\\\"currentcolor\\\" d=\\\"M11.293 4.293a1 1 0 0 1 1.338-.068l.076.068 7 7a1 1 0 0 1 0 1.414l-7 7a1 1 0 1 1-1.414-1.414L16.586 13H5a1 1 0 1 1 0-2h11.586l-5.293-5.293-.068-.076a1 1 0 0 1 .068-1.338\\\"></path></svg></span></a></p></div></div></div>\"\n}"
    }
  ],
  "details": {
    "url": "https://aimultiple.com/computer-use-agents",
    "title": "Computer Use Agents: Benchmark & Architecture",
    "author": "Cem Dilmegani",
    "summary": "See a concise feature table, clear architecture notes, and practical takeaways to help users pick or build the right computer use agents",
    "content": "<div id=\"readability-page-1\" class=\"page\"><div id=\"ArticleContent\"><p>Computer-use agents promise to operate real desktops and web apps, but their designs, limits, and trade-offs are often unclear. We examine leading systems by breaking down how they work, how they learn, and how their architectures differ. We also reference a focused UI-grounding benchmark on 100 desktop screenshots, spanning 4 task types and 5 runs per sample, which isolates visual perception quality and highlights why strong vision-language models matter even for composed computer-use agents.</p><p>See a feature table, architecture notes, practical takeaways, and benchmark results to help users pick or build the right computer-use agent:</p><h2 id=\"ui-grounding-benchmark-results\">UI grounding benchmark results</h2><div data-module-name=\"nivo-chart\"><p><svg xmlns=\"http://www.w3.org/2000/svg\" width=\"626\" height=\"400\" role=\"img\" focusable=\"false\"><rect width=\"626\" height=\"400\" fill=\"transparent\"></rect><g transform=\"translate(130,30)\"><g transform=\"translate(0,320)\"><line x1=\"0\" x2=\"426\" y1=\"0\" y2=\"0\" style=\"stroke: transparent; stroke-width: 1;\"></line><text transform=\"translate(213, 35) rotate(0)\" text-anchor=\"middle\" style=\"font-family: var(--body-font-family); font-size: var(--chart-axis-legend-font-size); fill: var(--body-default); dominant-baseline: central;\">Accuracy</text></g><g transform=\"translate(0,0)\"><g transform=\"translate(0,285.4901960784314)\" style=\"opacity: 1;\"><line x1=\"0\" x2=\"-5\" y1=\"0\" y2=\"0\" style=\"stroke: rgb(119, 119, 119); stroke-width: 1;\"></line><text dominant-baseline=\"central\" text-anchor=\"end\" transform=\"translate(-10,0) rotate(0)\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: var(--body-default);\">qwen3-vl-32b</text></g><g transform=\"translate(0,222.74509803921563)\" style=\"opacity: 1;\"><line x1=\"0\" x2=\"-5\" y1=\"0\" y2=\"0\" style=\"stroke: rgb(119, 119, 119); stroke-width: 1;\"></line><text dominant-baseline=\"central\" text-anchor=\"end\" transform=\"translate(-10,0) rotate(0)\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: var(--body-default);\">qwen3-vl-8b</text></g><g transform=\"translate(0,159.99999999999997)\" style=\"opacity: 1;\"><line x1=\"0\" x2=\"-5\" y1=\"0\" y2=\"0\" style=\"stroke: rgb(119, 119, 119); stroke-width: 1;\"></line><text dominant-baseline=\"central\" text-anchor=\"end\" transform=\"translate(-10,0) rotate(0)\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: var(--body-default);\">seed-1.6</text></g><g transform=\"translate(0,97.25490196078431)\" style=\"opacity: 1;\"><line x1=\"0\" x2=\"-5\" y1=\"0\" y2=\"0\" style=\"stroke: rgb(119, 119, 119); stroke-width: 1;\"></line><text dominant-baseline=\"central\" text-anchor=\"end\" transform=\"translate(-10,0) rotate(0)\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: var(--body-default);\">glm-4.6v</text></g><g transform=\"translate(0,34.50980392156863)\" style=\"opacity: 1;\"><line x1=\"0\" x2=\"-5\" y1=\"0\" y2=\"0\" style=\"stroke: rgb(119, 119, 119); stroke-width: 1;\"></line><text dominant-baseline=\"central\" text-anchor=\"end\" transform=\"translate(-10,0) rotate(0)\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: var(--body-default);\">ui-tars-1.5-7b</text></g><line x1=\"0\" x2=\"0\" y1=\"0\" y2=\"320\" style=\"stroke: transparent; stroke-width: 1;\"></line><text transform=\"translate(0, 160) rotate(-90)\" text-anchor=\"middle\" style=\"font-family: var(--body-font-family); font-size: var(--chart-axis-legend-font-size); fill: var(--body-default); dominant-baseline: central;\"></text></g><g transform=\"translate(0, 257.2549019607843)\"><rect width=\"385.104\" height=\"56.47058823529411\" rx=\"7\" ry=\"7\" fill=\"rgba(0, 90, 255, 1)\" stroke-width=\"0\" stroke=\"rgba(0, 90, 255, 1)\" focusable=\"false\" data-testid=\"bar.item.accuracy.0\"></rect><text x=\"395.104\" y=\"28.235294117647054\" text-anchor=\"start\" dominant-baseline=\"central\" fill-opacity=\"1\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: rgb(54, 65, 82); pointer-events: none;\">90.4%</text></g><g transform=\"translate(0, 194.5098039215686)\"><rect width=\"384.252\" height=\"56.47058823529411\" rx=\"7\" ry=\"7\" fill=\"rgba(0, 90, 255, 1)\" stroke-width=\"0\" stroke=\"rgba(0, 90, 255, 1)\" focusable=\"false\" data-testid=\"bar.item.accuracy.1\"></rect><text x=\"394.252\" y=\"28.235294117647054\" text-anchor=\"start\" dominant-baseline=\"central\" fill-opacity=\"1\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: rgb(54, 65, 82); pointer-events: none;\">90.2%</text></g><g transform=\"translate(0, 131.76470588235293)\"><rect width=\"184.032\" height=\"56.47058823529411\" rx=\"7\" ry=\"7\" fill=\"rgba(0, 90, 255, 1)\" stroke-width=\"0\" stroke=\"rgba(0, 90, 255, 1)\" focusable=\"false\" data-testid=\"bar.item.accuracy.2\"></rect><text x=\"194.032\" y=\"28.235294117647054\" text-anchor=\"start\" dominant-baseline=\"central\" fill-opacity=\"1\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: rgb(54, 65, 82); pointer-events: none;\">43.2%</text></g><g transform=\"translate(0, 69.01960784313725)\"><rect width=\"169.548\" height=\"56.47058823529411\" rx=\"7\" ry=\"7\" fill=\"rgba(0, 90, 255, 1)\" stroke-width=\"0\" stroke=\"rgba(0, 90, 255, 1)\" focusable=\"false\" data-testid=\"bar.item.accuracy.3\"></rect><text x=\"179.548\" y=\"28.235294117647054\" text-anchor=\"start\" dominant-baseline=\"central\" fill-opacity=\"1\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: rgb(54, 65, 82); pointer-events: none;\">39.8%</text></g><g transform=\"translate(0, 6.274509803921575)\"><rect width=\"161.028\" height=\"56.47058823529411\" rx=\"7\" ry=\"7\" fill=\"rgba(0, 90, 255, 1)\" stroke-width=\"0\" stroke=\"rgba(0, 90, 255, 1)\" focusable=\"false\" data-testid=\"bar.item.accuracy.4\"></rect><text x=\"171.028\" y=\"28.235294117647054\" text-anchor=\"start\" dominant-baseline=\"central\" fill-opacity=\"1\" style=\"font-family: var(--body-font-family); font-size: var(--chart-font-size); fill: rgb(54, 65, 82); pointer-events: none;\">37.8%</text></g></g></svg></p></div><p>For benchmark methodology details, read the <a href=\"#ui-grounding-benchmark-why-vision-quality-matters\" data-type=\"internal\" data-id=\"#ui-grounding-benchmark-why-vision-quality-matters\" aria-label=\"benchmark details\">benchmark details</a>.</p><ul>\n<li><strong>Qwen3-VL models</strong> reach <strong>~90% accuracy</strong>, with low error (≈7–9 px).</li>\n\n\n\n<li><strong>UI-specialized models</strong> like <strong>UI-TARS</strong> perform much worse (<strong>~38% accuracy</strong>) and show high variance and large errors, especially on state-dependent and dense interfaces.</li>\n\n\n\n<li>State-dependent and dense UIs are the hardest cases for most models.</li>\n</ul><h2 id=\"top-computer-use-agents\">Top computer use agents</h2><div data-module-name=\"table\"><table><thead><tr><th scope=\"col\"><p>Agent</p></th><th scope=\"col\"><p>Architecture</p></th><th scope=\"col\"><p>Runtime environment</p></th><th scope=\"col\"><p>Local system access</p></th></tr></thead><tbody><tr><th scope=\"row\"><div><p>Claude Cowork</p></div></th><td><div><p>End-to-End</p></div></td><td><div><p>Local visual workspace environment</p></div></td><td><div><p>✅</p></div></td></tr><tr><th scope=\"row\"><div><p>OpenAI Computer use preview</p></div></th><td><div><p>End-to-End</p></div></td><td><div><p>Cloud‑hosted agent runtime via API</p></div></td><td><div><p>❌</p></div></td></tr><tr><th scope=\"row\"><div><p>Anthropic Claude Computer Use</p></div></th><td><div><p>End-to-End</p></div></td><td><div><p>Local or cloud sandbox (API client controls a VM)</p></div></td><td><div><p>❌</p></div></td></tr><tr><th scope=\"row\"><div><p>Open Interpreter (OS Mode)</p></div></th><td><div><p>Composed</p></div></td><td><div><p>Local OS-level runtime</p></div></td><td><div><p>✅</p></div></td></tr><tr><th scope=\"row\"><div><p>Simular Agent S/S3</p></div></th><td><div><p>Composed</p></div></td><td><div><p>Framework can run locally or hosted (open‑source S/S3 runs locally)</p></div></td><td><div><p>❌ (but local execution possible via open‑source framework)</p></div></td></tr><tr><th scope=\"row\"><div><p>Cua AI</p></div></th><td><div><p>Composed</p></div></td><td><div><p>Cloud sandbox + local integration options</p></div></td><td><div><p>❌</p></div></td></tr></tbody></table></div><p>See the <a href=\"#explanation-of-key-computer-use-agent-features\" data-type=\"internal\" data-id=\"#explanation-of-key-computer-use-agent-features\" aria-label=\"features\">features</a> section for features in the table, and examine the <a href=\"#two-architectural-approaches-to-computer-use-models\" data-type=\"internal\" data-id=\"#two-architectural-approaches-to-computer-use-models\" aria-label=\"architectural approaches\">architectural approaches</a> section for the details of the computer use agents’ architecture.</p><h3 id=\"openai-computer-use-preview\"><strong>OpenAI Computer Use Preview</strong></h3><p>OpenAI’s computer use-preview is a specialized model built to understand and execute computer tasks via the Responses API. It focuses on text input and output, with optional image input, but does not support audio or video.</p><h3 id=\"anthropic-claude-computer-use\"><strong>Anthropic Claude Computer Use</strong></h3><p>Claude Computer Use is a beta feature that enables Claude to interact with a desktop or windowed computer environment, just like a person would. It works by seeing the screen, moving the mouse, and typing on the keyboard.</p><p>Claude cannot act on its own without a developer’s setup. It does not automatically access your real computer; it interacts with the sandbox you provide.</p><h3 id=\"open-interpreter-os-mode\"><strong>Open Interpreter (OS Mode)</strong></h3><p>Open Interpreter is an open-source terminal agent that capable of running code and interacting with your system.</p><p>It runs on your own computer, so it can use your files, programs, and browser directly. Users communicate with it in plain English, and it translates their instructions into actions by generating and executing code. Before any code runs, Open Interpreter displays what it plans to run and requests your approval.</p><h3 id=\"simular-agent-s-s3\"><strong>Simular Agent S/S3</strong></h3><p>Simular Agent S3 is a computer use agent that works by observing screens, planning actions, and controlling the mouse and keyboard to complete complex tasks. It is part of the open Agent S framework for autonomous GUI interaction.</p><p>Behavior Best-of-N (bBoN) is a core method that enables Agent S3 to generate multiple possible action sequences (“rollouts”), rather than a single run. It turns each rollout into a behavior narrative, which is a simple summary of what happened. A separate judgment step then chooses the best run.</p><h3 id=\"cua-ai\"><strong>Cua AI</strong></h3><p>Cua AI is an open-source framework that enabler to build, run, and test computer use <a data-wpil-monitor-id=\"1363\" href=\"https://aimultiple.com/ai-agents\" aria-label=\"AI\">AI </a><a target=\"_blank\" rel=\"noopener\" href=\"https://aimultiple.com/ai-agents\" aria-label=\"agents\">agents</a> across desktop environments by tying vision models, reasoning models, and sandboxed OS environments into one system. Cua can run agents in the cloud using remote sandboxes. It also lets you run them locally if you want more control or privacy.</p><p>Cua also helps you generate UI screenshots and agent action logs. You can record multi-step interactions, make training data, and run benchmarks to see how well agents perform.</p><h3 id=\"claude-cowork\"><strong>Claude Cowork</strong></h3><p>Claude Cowork is a way to have <em>Claude</em> do complex work directly on your computer. It utilizes the same agent design as Claude Code, but with a focus on tasks that involve your local files and programs, rather than just providing short chat responses. This feature is in research preview and runs inside the Claude Desktop app for macOS.</p><p><strong>Current Limitations:</strong></p><ul>\n<li>Only available on macOS Desktop.</li>\n\n\n\n<li>Claude does not keep memory across sessions.</li>\n\n\n\n<li>Cowork cannot share its work with others yet.</li>\n</ul><h2 id=\"osworld-benchmark\">OSWorld benchmark</h2><h3 id=\"results-for-computer-use-agentic-ai\">Results for computer use agentic AI</h3><div data-module-name=\"table\"><table><thead><tr><th scope=\"col\"><p>Rank</p></th><th scope=\"col\"><p>Model &amp; Date</p></th><th scope=\"col\"><p>Approach &amp; Details</p></th><th scope=\"col\"><p>Success Rate (Avg±Std)</p></th></tr></thead><tbody><tr><th scope=\"row\"><div><p>1</p></div></th><td><div><p>agent s3 w/ Opus 4.5 + GPT-5 bBoN (N=10)<br>\nSimular</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 100<br>\nRuns: 1</p></div></td><td><div><p>72.6%</p></div></td></tr><tr><th scope=\"row\"><div><p>2</p></div></th><td><div><p>agent s3 w/ GPT-5 bBoN (N=10)<br>\nSimular</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 100<br>\nRuns: 1</p></div></td><td><div><p>69.9%</p></div></td></tr><tr><th scope=\"row\"><div><p>3</p></div></th><td><div><p>UiPath Screen Agent w/ Opus 4.5<br>\nUiPath</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 100<br>\nRuns: 1</p></div></td><td><div><p>67.1%</p></div></td></tr><tr><th scope=\"row\"><div><p>4</p></div></th><td><div><p>agent s3 w/ Opus 4.5 bBoN (N=1)<br>\nSimular</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 100<br>\nRuns: 5</p></div></td><td><div><p>66.0%</p></div></td></tr><tr><th scope=\"row\"><div><p>5</p></div></th><td><div><p>OS-Symphony w/ GPT-5<br>\nShanghai AI Laboratory</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 50<br>\nRuns: 1</p></div></td><td><div><p>65.8%</p></div></td></tr><tr><th scope=\"row\"><div><p>6</p></div></th><td><div><p>UiPath Screen Agent w/ Opus 4.5<br>\nUiPath</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 50<br>\nRuns: 1</p></div></td><td><div><p>64.4%</p></div></td></tr><tr><th scope=\"row\"><div><p>7</p></div></th><td><div><p>GBOX Agent<br>\nGBOX.AI</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 15<br>\nRuns: 1</p></div></td><td><div><p>64.2%</p></div></td></tr><tr><th scope=\"row\"><div><p>8</p></div></th><td><div><p>GTA1 w/ GPT-5<br>\nSalesforce &amp; The Australian National University &amp; The University of Hong Kong</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 100<br>\nRuns: 1</p></div></td><td><div><p>63.4%</p></div></td></tr><tr><th scope=\"row\"><div><p>9</p></div></th><td><div><p>claude-sonnet-4-5-20250929<br>\nAnthropic</p></div></td><td><div><p>Type: General model<br>\nMax Steps: 100<br>\nRuns: 1</p></div></td><td><div><p>62.9%</p></div></td></tr><tr><th scope=\"row\"><div><p>10</p></div></th><td><div><p>agent s3 w/ GPT-5 bBoN (N=1)<br>\nSimular</p></div></td><td><div><p>Type: Agentic framework<br>\nMax Steps: 100<br>\nRuns: 10</p></div></td><td><div><p>62.6%</p></div></td></tr></tbody></table></div><p><strong>Disclaimer:</strong> The same model may appear at different ranks because OSWorld lists results by full evaluation configuration (<a target=\"_blank\" rel=\"noopener\" href=\"https://aimultiple.com/agentic-frameworks\" aria-label=\"agent framework\">agent framework</a>, grounding or planning model, Best-of-N setting, run count, and step limit), and even small changes in these settings are treated as separate entries with different performance outcomes.</p><h3 id=\"methodology\">Methodology</h3><p>The benchmark includes 369 real-world tasks (or 361 excluding Google Drive tasks that require manual setup). Tasks span web and desktop applications, OS file operations, and multi-app workflows. Each task starts from a reproducible initial state and is paired with a custom execution-based evaluation script, ensuring reliable scoring.</p><h4 id=\"evaluation-process\">Evaluation process</h4><p>Agents interact with a live OS environment. Success is measured by what the agent actually does, not by text outputs. Environments support parallel and headless execution, enabling scalable testing.</p><h4 id=\"benchmark-scope\">Benchmark scope</h4><p>OSWorld supports open-ended tasks across arbitrary applications, multimodal inputs, cross-app workflows, and intermediate starting states. Compared to prior benchmarks, it offers broader coverage and more realistic conditions.</p><h4 id=\"baselines-and-analysis\">Baselines and analysis</h4><p>The benchmark evaluates general models, specialized models, and agentic frameworks across LLM and VLM families. Results show a large gap between human performance (~72%) and current agents, highlighting challenges in GUI grounding and operational knowledge. OSWorld also enables detailed analysis across task types, UI complexity, inputs, and operating systems.</p><h2 id=\"two-architectural-approaches-to-computer-use-models\">Two architectural approaches to computer use models</h2><p>Today, most computer use agents fall into one of two design patterns:</p><ul>\n<li>End-to-End (E2E) Agents</li>\n\n\n\n<li>Composed Agents</li>\n</ul><p>Both aim to complete tasks on a computer. They differ in how they divide perception, reasoning, and action.</p><h2 id=\"end-to-end-e2e-agents\">End-to-End (E2E) agents</h2><p>End-to-end agents use one vision-language model to handle the entire loop. The model receives a screenshot and a task description. It then outputs the next action directly.</p><p>There is no clear boundary between seeing, reasoning, and acting. These processes are learned together inside the same model.</p><h3 id=\"how-e2e-agents-work\">How E2E agents work</h3><p><strong>Screenshot + Task → Unified Representation → Action</strong></p><p>The model reasons directly over pixels and text. It does not build an explicit list of buttons or fields. Instead, it learns associations between visual patterns and actions during training.</p><h4 id=\"strengths\">Strengths</h4><ul>\n<li>Simpler system design</li>\n\n\n\n<li>Fewer integration points where errors can occur</li>\n\n\n\n<li>Often more stable over long tasks</li>\n</ul><h4 id=\"limitations\">Limitations</h4><ul>\n<li>Limited visibility into why an action was chosen</li>\n\n\n\n<li>Harder to debug when something goes wrong</li>\n\n\n\n<li>Less control over intermediate reasoning steps</li>\n</ul><h4 id=\"practical-implications\">Practical implications</h4><p>Because perception and planning are tightly linked, small visual errors are less likely to cascade into full failures. When an action does not work, the agent can re-evaluate the updated screen and adapt.</p><p><strong>Trade-off: </strong>It is difficult to inspect intermediate decisions or isolate the source of failures.</p><h2 id=\"composed-agents\">Composed agents</h2><p>Composed agents divide the interaction loop into separate stages. Each stage is handled by a different model or subsystem.</p><h3 id=\"how-composed-ai-agents-work\">How composed AI agents work</h3><p>A typical pipeline looks like this:</p><ol>\n<li><strong>Grounding:</strong> Detect graphical user interface elements from the screenshot</li>\n\n\n\n<li><strong>Planning:</strong> Decide what to do next</li>\n\n\n\n<li><strong>Execution:</strong> Perform tasks on the system</li>\n</ol><p>This design makes each step explicit.</p><h4 id=\"strengths-2\">Strengths</h4><ul>\n<li>Clear separation of responsibilities</li>\n\n\n\n<li>Easier to inspect intermediate outputs</li>\n\n\n\n<li>Better suited for research and controlled experiments</li>\n</ul><h4 id=\"limitations-2\">Limitations</h4><ul>\n<li>Higher system complexity</li>\n\n\n\n<li>Errors can propagate between components</li>\n\n\n\n<li>Often less reliable in real desktop environments</li>\n</ul><h4 id=\"practical-implications-2\">Practical implications</h4><p>Composed agents rely on structured representations of the screen, such as detected buttons or text fields. This improves transparency but adds fragility. If grounding is inaccurate, planning decisions are likely to fail.</p><p><strong>Trade-off</strong>: Long tasks are especially challenging. Small mismatches between perceived and actual screen state can accumulate over time.</p><h2 id=\"core-building-blocks-of-computer-using-agents-cuas\">Core building blocks of computer-using agents (CUAs)</h2><p>Modern computer use agents are built using three main components:</p><h3 id=\"1-vision-language-models-vlms\"><strong>1. Vision-language models (VLMs)</strong></h3><p>Single VLMs form the core of most end-to-end agents. They process screenshots and instructions together and output actions directly.</p><p><strong>Screenshot + Task → Joint Vision-Language Space → Action</strong></p><p>The model encodes visual and textual inputs into a shared internal space. In this space, it learns how visual patterns relate to actions without explicit labels.</p><p>There is no separate grounding step. UI understanding and task planning occur implicitly and simultaneously.</p><p><strong>Practical implications: </strong>Single VLMs reduce architectural complexity and limit the propagation of errors. They favor robustness and simplicity over transparency and fine-grained control.</p><h3 id=\"2-grounding-models\"><strong>2. Grounding models</strong></h3><p>Grounding models focus solely on perception and play a crucial role in the composed agents. Their job is to translate raw screenshots into structured descriptions of the computer interface. They do not reason about goals or select actions.</p><div data-module-name=\"image\"><p><img alt=\"how grounding model is used in computer use agents\" title=\"how grounding model is used in computer use agents\" loading=\"eager\" width=\"700\" height=\"400\" decoding=\"async\" data-nimg=\"1\" src=\"https://aimultiple.com/wp-content/uploads/2026/01/grounding-madel-1400x539.png.webp\"></p></div><p><strong>Screenshot → Grounding Model → Structured UI Representation</strong></p><p>Outputs often include:</p><ul>\n<li>Detected UI elements</li>\n\n\n\n<li>Spatial locations (bounding boxes)</li>\n\n\n\n<li>Semantic labels (button, input field, text)</li>\n\n\n\n<li>Extracted text</li>\n</ul><p>This representation is passed to a planning model.</p><h4 id=\"strengths-3\">Strengths</h4><ul>\n<li>Clear and inspectable perception</li>\n\n\n\n<li>Easier to log and analyze failures</li>\n\n\n\n<li>Improved transparency</li>\n</ul><h4 id=\"limitations-3\">Limitations</h4><ul>\n<li>Errors propagate downstream</li>\n\n\n\n<li>Sensitive to visual changes and dynamic layouts</li>\n\n\n\n<li>Difficult to maintain consistency over many steps</li>\n</ul><p><strong>Practical implications: </strong>Grounding is often the weakest link in composed systems. Missing or outdated elements can mislead planning models and cause repeated failures.</p><h3 id=\"ui-grounding-benchmark-why-vision-quality-matters\">UI Grounding benchmark: Why vision quality matters</h3><p>To isolate the role of visual perception, we reference a focused UI grounding benchmark that evaluates how well models identify the exact pixel location of a UI element from a natural-language instruction.</p><h4 id=\"benchmark-setup\">Benchmark setup</h4><ul>\n<li>100 desktop screenshots</li>\n\n\n\n<li>4 task types: simple, relational, state-dependent, dense UI</li>\n\n\n\n<li>5 runs per sample to measure consistency</li>\n\n\n\n<li>Fixed resolution: 2560×1440</li>\n</ul><p>For a more detailed dataset and methodology, visit <a target=\"_blank\" rel=\"noopener\" href=\"https://huggingface.co/datasets/ai-multiple/aim-ui-grounding\" aria-label=\"AIMultiple UI Grounding\">AIMultiple UI Grounding</a> on HuggingFace.</p><p><strong>Takeaway</strong><br>Accurate UI grounding remains a major bottleneck. Current evidence shows that robust visual perception and implicit UI understanding matter more than narrow UI specialization, especially for reliable computer-use agents operating real desktops.</p><h3 id=\"planning-models\"><strong>Planning models</strong></h3><p>Planning models determine the next steps. They work with structured UI data, task goals, and interaction history. They do not process raw images. These models play a crucial role in the composed agent architecture.</p><p><strong>Structured UI + Task Goal → Planning Model → Next Action</strong></p><p>Planning models can:</p><ul>\n<li>Break tasks into steps</li>\n\n\n\n<li>Track progress</li>\n\n\n\n<li>Apply rules or heuristics</li>\n\n\n\n<li>Log reasoning explicitly</li>\n</ul><h4 id=\"challenges-in-practice\">Challenges in practice</h4><ul>\n<li><strong>High sensitivity to input errors</strong><br>Incorrect grounding leads to faulty plans.</li>\n\n\n\n<li><strong>State drift over time</strong><br>UI changes can invalidate earlier assumptions.</li>\n\n\n\n<li><strong>Limited failure recovery</strong><br>Without strong feedback, planners may loop or stall.</li>\n\n\n\n<li><strong>Execution mismatches</strong><br>Timing, focus, or coordination errors can break plans.</li>\n</ul><p><strong>Practical implications: </strong>Planning models add structure and transparency, but their effectiveness depends heavily on accurate perception and reliable execution.</p><h2 id=\"explanation-of-key-computer-use-agent-features\">Explanation of key computer use agent features</h2><h3 id=\"runtime-environment\">Runtime environment</h3><p>It defines where the computer-use agent runs and how it controls the operating system (cloud VM, local machine, or container-based runtime).</p><h3 id=\"local-system-access\">Local system access</h3><p>This shows whether the agent can read or write files on the user’s actual machine, not just in a remote sandbox. Local access is useful for personal workflows but raises higher security concerns.</p><h2 id=\"what-is-the-overall-trade-off-between-e2e-and-composed-agents\">What is the overall trade-off between E2E and composed agents?</h2><p>End-to-end agents are currently more reliable for direct use on personal computers. Their unified design reduces coordination issues and failure points.</p><p>Composed agents are not inherently weaker. They offer greater flexibility, customization, and interpretability. However, they require stronger grounding, tighter state management, and careful integration to perform well in real environments.</p><p>The core trade-off is not capability, but <strong>robustness versus control</strong>.</p><h2 id=\"what-are-computer-use-agents\">What are computer use agents?</h2><p>Computer use agents are systems designed to operate a computer in a manner similar to a human. They look at the screen, decide what to do, and interact through actions such as clicking, typing, and scrolling.</p><p>At first glance, this sounds simple. In practice, it is difficult. Desktop environments are dynamic. Interfaces change often. There are no fixed APIs or stable structures to rely on. These agents must work from what they see on the screen and reason about it in real time.</p><p>Despite different implementations, most computer use agents follow the same basic loop:</p><p><strong>Observe → Interpret → Decide → Execute</strong></p><p>How this loop is implemented determines how stable, flexible, and reliable an agent is in real use.</p><div><div><p><img alt=\"Cem Dilmegani\" loading=\"lazy\" width=\"56\" height=\"56\" decoding=\"async\" data-nimg=\"1\" src=\"https://aimultiple.com/wp-content/uploads/2024/07/headshot-of-Cem-Dilmegani-160x160.png.webp\"></p><div><p>Cem Dilmegani</p><p>Principal Analyst</p></div></div><div><p>Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.</p><p>Cem's work has been cited by leading global publications including Business Insider,  Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.</p><p>Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey &amp; Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.</p><p>He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. </p><p>Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.</p></div><p><a href=\"https://aimultiple.com/author/cem-dilmegani\" target=\"_blank\">View Full Profile<span><svg xmlns=\"http://www.w3.org/2000/svg\" width=\"1em\" height=\"1em\" fill=\"none\" viewBox=\"0 0 24 24\"><path fill=\"currentcolor\" d=\"M11.293 4.293a1 1 0 0 1 1.338-.068l.076.068 7 7a1 1 0 0 1 0 1.414l-7 7a1 1 0 1 1-1.414-1.414L16.586 13H5a1 1 0 1 1 0-2h11.586l-5.293-5.293-.068-.076a1 1 0 0 1 .068-1.338\"></path></svg></span></a></p></div></div></div>"
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      "text": "{\n  \"results\": [\n    {\n      \"title\": \"Codex CLI vs Claude Code: OpenAI vs Anthropic Terminal Coding Agents | AI Magicx Blog | AI Magicx\",\n      \"url\": \"https://www.aimagicx.com/blog/codex-cli-vs-claude-code-comparison\",\n      \"description\": \"# Codex CLI vs Claude Code: OpenAI vs Anthropic Terminal Coding Agents. A comprehensive comparison between OpenAI's Codex CLI and Anthropic's Claude Code. The two leading AI labs have both released terminal-based coding agents: **Codex CLI** from OpenAI, built in Rust and powered by GPT-5-Codex, versus **Claude Code** from Anthropic, featuring checkpoint rollback and native Claude model integration. Codex CLI is OpenAI's open-source coding agent that runs locally from your terminal. Built in Rust for speed and efficiency, it can read, change, and run code on your machine with a full-screen terminal UI for real-time collaboration. * Open source (built in Rust). * GPT-5-Codex optimized for software engineering. * Cloud integration for remote tasks. Claude Code is Anthropic's official agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster through natural language commands. It features checkpoint rollback and native Claude model integration. * Claude Opus 4.5 with thinking mode by default. **Analysis:** Codex CLI's open-source Rust implementation offers transparency and native performance.\"\n    },\n    {\n      \"title\": \"Claude Code vs Codex CLI — Complete Comparison (2026)\",\n      \"url\": \"https://www.agensi.io/learn/claude-code-vs-codex-cli-comparison\",\n      \"description\": \"# Claude Code vs Codex CLI: Which AI Coding Agent in 2026? Claude Code and Codex CLI are the two most-used terminal AI coding agents in 2026. Both run in your terminal, both understand your codebase, and both support MCP and SKILL.md. Claude Code runs on Anthropic's Claude models — Sonnet 4 for most tasks and Opus 4 for complex reasoning. Codex CLI runs on OpenAI's GPT-4o and o3/o4-mini models. Codex CLI generates code faster and tends to be more concise. ## SKILL.md support. Both agents fully support SKILL.md. Claude Code has the more mature MCP implementation. It was one of the first agents to support MCP, and most MCP server developers test against Claude Code first. Codex CLI's MCP support is functional and improving with each release. In practice, the same MCP servers work with both agents. Since SKILL.md skills and MCP servers work across both agents, you lose nothing by having both installed and switching based on the task.\"\n    },\n    {\n      \"title\": \"Claude Code vs Codex CLI 2026: Which Terminal AI Coding Agent Wins? | NxCode\",\n      \"url\": \"https://www.nxcode.io/resources/news/claude-code-vs-codex-cli-terminal-coding-comparison-2026\",\n      \"description\": \"Claude Code vs Codex CLI 2026: Which Terminal AI Coding Agent Wins? * **Claude Code produces better code**: 67% win rate over Codex CLI in blind quality tests, with an 80.9% score on SWE-bench Verified — the highest of any coding agent. * **Codex CLI is faster and more efficient**: It leads Terminal-Bench 2.0 at 77.3% and uses roughly 4x fewer tokens than Claude Code for equivalent tasks. * **Both start at $20/month, but the real cost diverges fast**: Claude Code burns through token limits quickly; Codex CLI stretches further thanks to superior token efficiency. * **The best developers use both**: Claude Code for architecture, complex features, and frontend. The two dominant players — Anthropic's Claude Code and OpenAI's Codex CLI — both operate from the command line, both handle multi-file edits autonomously, and both promise to transform how you write software. Claude Code prioritizes code quality and deep reasoning. ## What Are Claude Code and Codex CLI? ### Claude Code. ### Codex CLI.\"\n    },\n    {\n      \"title\": \"\\\"CLI coding agents: OpenAI Codex, Claude Code, Gemini CLI, and more\\\" | Matt Koppenheffer posted on the topic | LinkedIn\",\n      \"url\": \"https://www.linkedin.com/posts/matt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1\",\n      \"description\": \"[Sign in](https://www.linkedin.com/login?session_redirect=https%3A%2F%2Fwww%2Elinkedin%2Ecom%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&fromSignIn=true&trk=public_post_nav-header-signin)[Join now](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww%2Elinkedin%2Ecom%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&trk=public_post_nav-header-join)[![Image 1](https://static.licdn.com/aero-v1/sc/h/9c8pery4andzj6ohjkjp54ma2)](https://www.linkedin.com/login?session_redirect=https%3A%2F%2Fwww%2Elinkedin%2Ecom%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&fromSignIn=true&trk=public_post_nav-header-signin). *   [Report this post](https://www.linkedin.com/uas/login?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&trk=public_post_ellipsis-menu-semaphore-sign-in-redirect&guestReportContentType=POST&_f=guest-reporting). 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[Join now](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww%2Elinkedin%2Ecom%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&trk=public_post_contextual-sign-in-modal_join-link).\"\n    },\n    {\n      \"title\": \"Claude Code vs Codex: Which Terminal AI Agent Should You Use?\",\n      \"url\": \"https://www.analyticsvidhya.com/blog/2026/04/claude-code-vs-codex/\",\n      \"description\": \"Master Generative AI with 10+ Real-world Projects in 2026! ##### Data analyst Learning Path. ##### Data Scientist Learning Path. ##### AI Engineer Learning Path. ##### Generative AI Learning Path. # Claude Code vs Codex: A Detailed Terminal Agent Comparison. Tools like Claude Code and Codex both operate in this space, but take different approaches. ## Getting started with Claude Code and Codex CLI. Before moving onto the real workflows, First let’s install both the tools in our system. Run Codex in a terminal. Install the Claude Code with npm. It wants to get a handle on the repo, suggest a plan, then proceed with the task with mode permission and checkpoints to keep it safe. Claude Code loads *CLAUDE.md* at the beginning of each session and uses it as context for the project, your Workflow, or even your company. If you are using Claude Code, you can have the agent learn the pace of the repo, then use *CLAUDE.md* for things you need to keep stable.\"\n    },\n    {\n      \"title\": \"Pay attention to this one if you are building terminal-based coding ...\",\n      \"url\": \"https://www.facebook.com/groups/1352621609745795/posts/1455019136172708/\",\n      \"description\": \"The industry is shifting from IDE plugins to terminal- native agents. Claude Code, Codex CLI, and others have proven the model works. This\"\n    },\n    {\n      \"title\": \"Every CLI coding agent, compared\",\n      \"url\": \"https://michaellivs.com/blog/cli-coding-agents-compared/\",\n      \"description\": \"# Every CLI coding agent, compared. There are now 36 CLI coding agents. | Gemini CLI | Google | 93.6K | TS | Apache-2.0 | 1M token context, generous free tier |. | Codex CLI | OpenAI | 59K | Rust | Apache-2.0 | Rust performance, model-native compaction |. | Kimi CLI | Moonshot AI | 5.9K | Python | Apache-2.0 | First Chinese lab with CLI agent |. | Mistral Vibe | Mistral | 3K | Python | Apache-2.0 | Only European lab CLI agent |. | Cline CLI | 57.6K | TS | Apache-2.0 | IDE agent that added CLI mode |. | Roo Code | 22.1K | TS | Apache-2.0 | Multi-agent orchestration (Boomerang) |. | Kode CLI | 4.3K | TS | Apache-2.0 | Multi-model collaboration |. Four that matter for most people: OpenCode for open-source, Claude Code for features, Gemini CLI for free, Codex CLI for performance.\"\n    },\n    {\n      \"title\": \"The 2026 Guide to Coding CLI Tools: 15 AI Agents Compared – Tembo\",\n      \"url\": \"https://www.tembo.io/blog/coding-cli-tools-comparison\",\n      \"description\": \"### [Claude Code — Anthropic](https://www.tembo.io/blog/coding-cli-tools-comparison#claude-code--anthropic). ### [Codex — OpenAI](https://www.tembo.io/blog/coding-cli-tools-comparison#codex--openai). ### [Aider — Open Source Pioneer](https://www.tembo.io/blog/coding-cli-tools-comparison#aider--open-source-pioneer). ### [Warp — The Terminal Reimagined](https://www.tembo.io/blog/coding-cli-tools-comparison#warp--the-terminal-reimagined). ### [Droid — Factory AI](https://www.tembo.io/blog/coding-cli-tools-comparison#droid--factory-ai). ## [Open Source / Community-Driven](https://www.tembo.io/blog/coding-cli-tools-comparison#open-source--community-driven). ## [How to Choose](https://www.tembo.io/blog/coding-cli-tools-comparison#how-to-choose). ### [Are you committed to a single AI provider?](https://www.tembo.io/blog/coding-cli-tools-comparison#are-you-committed-to-a-single-ai-provider). ### [Do you need model flexibility?](https://www.tembo.io/blog/coding-cli-tools-comparison#do-you-need-model-flexibility). The Landscape at a Glance Big-Lab Native Tools Claude Code — Anthropic Codex — OpenAI Gemini CLI — Google GitHub Copilot CLI — GitHub/Microsoft Independent / Startup Tools Amp — Sourcegraph Aider — Open Source Pioneer Warp — The Terminal Reimagined Augment CLI — Enterprise Context Engine Droid — Factory AI Kiro — AWS Open Source / Community-Driven OpenCode — anomalyco Goose — Block Crush — Charmbracelet Cline — VS Code Native Kilo — Feature-Rich Fork How to Choose Are you committed to a single AI provider?Do you need model flexibility?What is your autonomy comfort level?What is your budget?Do you need more than just coding?The Bigger Picture.\"\n    },\n    {\n      \"title\": \"Which CLI AI coding tool to use right now? Codex CLI vs. Claude Caude vs. sth else? : r/vibecoding\",\n      \"url\": \"https://www.reddit.com/r/vibecoding/comments/1nkx0zc/which_cli_ai_coding_tool_to_use_right_now_codex/\",\n      \"description\": \"# Which CLI AI coding tool to use right now? [Skip to main content](https://www.reddit.com/r/vibecoding/comments/1nkx0zc/which_cli_ai_coding_tool_to_use_right_now_codex/#main-content)Which CLI AI coding tool to use right now? Open menu Open navigation[](https://www.reddit.com/)Go to Reddit Home. Get App Get the Reddit app [Log In](https://www.reddit.com/login/)Log in to Reddit. [![Image 1](https://styles.redditmedia.com/t5_djga4z/styles/communityIcon_d3jghm1vyrhe1.PNG?width=96&height=96&frame=1&auto=webp&crop=96%3A96%2Csmart&s=2f71c952da2c033b49b3cdcfd1ad551ee5d7cb86) Go to vibecoding](https://www.reddit.com/r/vibecoding/). [r/vibecoding](https://www.reddit.com/r/vibecoding/)•7mo ago. [anotherjmc](https://www.reddit.com/user/anotherjmc/). # Which CLI AI coding tool to use right now? By continuing, you agree to our[User Agreement](https://www.redditinc.com/policies/user-agreement)and acknowledge that you understand the[Privacy Policy](https://www.redditinc.com/policies/privacy-policy). [Best CLI AI coding tools for projects](https://www.reddit.com/answers/2874faf8-a064-4bd2-b7d8-e1a07022ecd9/?q=Best+CLI+AI+coding+tools+for+projects&source=PDP). [Differences between Codex and Sonnet](https://www.reddit.com/answers/0e0c8810-5292-4718-a0b3-faf40ebeb79e/?q=Differences+between+Codex+and+Sonnet&source=PDP). [Recommendations for AI coding agents on Linux](https://www.reddit.com/answers/181480e5-accf-4ebd-9bb6-08984d6f4893/?q=Recommendations+for+AI+coding+agents+on+Linux&source=PDP). [Unique programming languages to explore](https://www.reddit.com/answers/36afd1c3-d059-47a9-8478-bf56dd818dfc/?q=Unique+programming+languages+to+explore&source=PDP). [How to enhance coding with music](https://www.reddit.com/answers/be7736d8-2380-47f5-8269-debf43a9224a/?q=How+to+enhance+coding+with+music&source=PDP). ![Image 2: sunset](https://emoji.redditmedia.com/9fuii44299oe1_t5_djga4z/sunset). ![Image 3: sunset](https://emoji.redditmedia.com/9fuii44299oe1_t5_djga4z/sunset). 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All rights reserved.](https://redditinc.com/). ![Image 4](https://id.rlcdn.com/472486.gif).\"\n    },\n    {\n      \"title\": \"Inside the Agent Harness: How Codex and Claude Code Actually ...\",\n      \"url\": \"https://medium.com/jonathans-musings/inside-the-agent-harness-how-codex-and-claude-code-actually-work-63593e26c176\",\n      \"description\": \"A deep technical dive into how CLI coding agents structure their conversations, manage context, and orchestrate tool calls. Jonathan Fulton.\"\n    }\n  ]\n}"
    }
  ],
  "details": {
    "results": [
      {
        "title": "Codex CLI vs Claude Code: OpenAI vs Anthropic Terminal Coding Agents | AI Magicx Blog | AI Magicx",
        "url": "https://www.aimagicx.com/blog/codex-cli-vs-claude-code-comparison",
        "description": "# Codex CLI vs Claude Code: OpenAI vs Anthropic Terminal Coding Agents. A comprehensive comparison between OpenAI's Codex CLI and Anthropic's Claude Code. The two leading AI labs have both released terminal-based coding agents: **Codex CLI** from OpenAI, built in Rust and powered by GPT-5-Codex, versus **Claude Code** from Anthropic, featuring checkpoint rollback and native Claude model integration. Codex CLI is OpenAI's open-source coding agent that runs locally from your terminal. Built in Rust for speed and efficiency, it can read, change, and run code on your machine with a full-screen terminal UI for real-time collaboration. * Open source (built in Rust). * GPT-5-Codex optimized for software engineering. * Cloud integration for remote tasks. Claude Code is Anthropic's official agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster through natural language commands. It features checkpoint rollback and native Claude model integration. * Claude Opus 4.5 with thinking mode by default. **Analysis:** Codex CLI's open-source Rust implementation offers transparency and native performance."
      },
      {
        "title": "Claude Code vs Codex CLI — Complete Comparison (2026)",
        "url": "https://www.agensi.io/learn/claude-code-vs-codex-cli-comparison",
        "description": "# Claude Code vs Codex CLI: Which AI Coding Agent in 2026? Claude Code and Codex CLI are the two most-used terminal AI coding agents in 2026. Both run in your terminal, both understand your codebase, and both support MCP and SKILL.md. Claude Code runs on Anthropic's Claude models — Sonnet 4 for most tasks and Opus 4 for complex reasoning. Codex CLI runs on OpenAI's GPT-4o and o3/o4-mini models. Codex CLI generates code faster and tends to be more concise. ## SKILL.md support. Both agents fully support SKILL.md. Claude Code has the more mature MCP implementation. It was one of the first agents to support MCP, and most MCP server developers test against Claude Code first. Codex CLI's MCP support is functional and improving with each release. In practice, the same MCP servers work with both agents. Since SKILL.md skills and MCP servers work across both agents, you lose nothing by having both installed and switching based on the task."
      },
      {
        "title": "Claude Code vs Codex CLI 2026: Which Terminal AI Coding Agent Wins? | NxCode",
        "url": "https://www.nxcode.io/resources/news/claude-code-vs-codex-cli-terminal-coding-comparison-2026",
        "description": "Claude Code vs Codex CLI 2026: Which Terminal AI Coding Agent Wins? * **Claude Code produces better code**: 67% win rate over Codex CLI in blind quality tests, with an 80.9% score on SWE-bench Verified — the highest of any coding agent. * **Codex CLI is faster and more efficient**: It leads Terminal-Bench 2.0 at 77.3% and uses roughly 4x fewer tokens than Claude Code for equivalent tasks. * **Both start at $20/month, but the real cost diverges fast**: Claude Code burns through token limits quickly; Codex CLI stretches further thanks to superior token efficiency. * **The best developers use both**: Claude Code for architecture, complex features, and frontend. The two dominant players — Anthropic's Claude Code and OpenAI's Codex CLI — both operate from the command line, both handle multi-file edits autonomously, and both promise to transform how you write software. Claude Code prioritizes code quality and deep reasoning. ## What Are Claude Code and Codex CLI? ### Claude Code. ### Codex CLI."
      },
      {
        "title": "\"CLI coding agents: OpenAI Codex, Claude Code, Gemini CLI, and more\" | Matt Koppenheffer posted on the topic | LinkedIn",
        "url": "https://www.linkedin.com/posts/matt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1",
        "description": "[Sign in](https://www.linkedin.com/login?session_redirect=https%3A%2F%2Fwww%2Elinkedin%2Ecom%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&fromSignIn=true&trk=public_post_nav-header-signin)[Join now](https://www.linkedin.com/signup/cold-join?session_redirect=https%3A%2F%2Fwww%2Elinkedin%2Ecom%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&trk=public_post_nav-header-join)[![Image 1](https://static.licdn.com/aero-v1/sc/h/9c8pery4andzj6ohjkjp54ma2)](https://www.linkedin.com/login?session_redirect=https%3A%2F%2Fwww%2Elinkedin%2Ecom%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&fromSignIn=true&trk=public_post_nav-header-signin). *   [Report this post](https://www.linkedin.com/uas/login?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fmatt-koppenheffer_quick-thoughts-on-cli-coding-agents-since-activity-7372353748645363713-wfQ1&trk=public_post_ellipsis-menu-semaphore-sign-in-redirect&guestReportContentType=POST&_f=guest-reporting). 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      },
      {
        "title": "Claude Code vs Codex: Which Terminal AI Agent Should You Use?",
        "url": "https://www.analyticsvidhya.com/blog/2026/04/claude-code-vs-codex/",
        "description": "Master Generative AI with 10+ Real-world Projects in 2026! ##### Data analyst Learning Path. ##### Data Scientist Learning Path. ##### AI Engineer Learning Path. ##### Generative AI Learning Path. # Claude Code vs Codex: A Detailed Terminal Agent Comparison. Tools like Claude Code and Codex both operate in this space, but take different approaches. ## Getting started with Claude Code and Codex CLI. Before moving onto the real workflows, First let’s install both the tools in our system. Run Codex in a terminal. Install the Claude Code with npm. It wants to get a handle on the repo, suggest a plan, then proceed with the task with mode permission and checkpoints to keep it safe. Claude Code loads *CLAUDE.md* at the beginning of each session and uses it as context for the project, your Workflow, or even your company. If you are using Claude Code, you can have the agent learn the pace of the repo, then use *CLAUDE.md* for things you need to keep stable."
      },
      {
        "title": "Pay attention to this one if you are building terminal-based coding ...",
        "url": "https://www.facebook.com/groups/1352621609745795/posts/1455019136172708/",
        "description": "The industry is shifting from IDE plugins to terminal- native agents. Claude Code, Codex CLI, and others have proven the model works. This"
      },
      {
        "title": "Every CLI coding agent, compared",
        "url": "https://michaellivs.com/blog/cli-coding-agents-compared/",
        "description": "# Every CLI coding agent, compared. There are now 36 CLI coding agents. | Gemini CLI | Google | 93.6K | TS | Apache-2.0 | 1M token context, generous free tier |. | Codex CLI | OpenAI | 59K | Rust | Apache-2.0 | Rust performance, model-native compaction |. | Kimi CLI | Moonshot AI | 5.9K | Python | Apache-2.0 | First Chinese lab with CLI agent |. | Mistral Vibe | Mistral | 3K | Python | Apache-2.0 | Only European lab CLI agent |. | Cline CLI | 57.6K | TS | Apache-2.0 | IDE agent that added CLI mode |. | Roo Code | 22.1K | TS | Apache-2.0 | Multi-agent orchestration (Boomerang) |. | Kode CLI | 4.3K | TS | Apache-2.0 | Multi-model collaboration |. Four that matter for most people: OpenCode for open-source, Claude Code for features, Gemini CLI for free, Codex CLI for performance."
      },
      {
        "title": "The 2026 Guide to Coding CLI Tools: 15 AI Agents Compared – Tembo",
        "url": "https://www.tembo.io/blog/coding-cli-tools-comparison",
        "description": "### [Claude Code — Anthropic](https://www.tembo.io/blog/coding-cli-tools-comparison#claude-code--anthropic). ### [Codex — OpenAI](https://www.tembo.io/blog/coding-cli-tools-comparison#codex--openai). ### [Aider — Open Source Pioneer](https://www.tembo.io/blog/coding-cli-tools-comparison#aider--open-source-pioneer). ### [Warp — The Terminal Reimagined](https://www.tembo.io/blog/coding-cli-tools-comparison#warp--the-terminal-reimagined). ### [Droid — Factory AI](https://www.tembo.io/blog/coding-cli-tools-comparison#droid--factory-ai). ## [Open Source / Community-Driven](https://www.tembo.io/blog/coding-cli-tools-comparison#open-source--community-driven). ## [How to Choose](https://www.tembo.io/blog/coding-cli-tools-comparison#how-to-choose). ### [Are you committed to a single AI provider?](https://www.tembo.io/blog/coding-cli-tools-comparison#are-you-committed-to-a-single-ai-provider). ### [Do you need model flexibility?](https://www.tembo.io/blog/coding-cli-tools-comparison#do-you-need-model-flexibility). The Landscape at a Glance Big-Lab Native Tools Claude Code — Anthropic Codex — OpenAI Gemini CLI — Google GitHub Copilot CLI — GitHub/Microsoft Independent / Startup Tools Amp — Sourcegraph Aider — Open Source Pioneer Warp — The Terminal Reimagined Augment CLI — Enterprise Context Engine Droid — Factory AI Kiro — AWS Open Source / Community-Driven OpenCode — anomalyco Goose — Block Crush — Charmbracelet Cline — VS Code Native Kilo — Feature-Rich Fork How to Choose Are you committed to a single AI provider?Do you need model flexibility?What is your autonomy comfort level?What is your budget?Do you need more than just coding?The Bigger Picture."
      },
      {
        "title": "Which CLI AI coding tool to use right now? Codex CLI vs. Claude Caude vs. sth else? : r/vibecoding",
        "url": "https://www.reddit.com/r/vibecoding/comments/1nkx0zc/which_cli_ai_coding_tool_to_use_right_now_codex/",
        "description": "# Which CLI AI coding tool to use right now? [Skip to main content](https://www.reddit.com/r/vibecoding/comments/1nkx0zc/which_cli_ai_coding_tool_to_use_right_now_codex/#main-content)Which CLI AI coding tool to use right now? Open menu Open navigation[](https://www.reddit.com/)Go to Reddit Home. Get App Get the Reddit app [Log In](https://www.reddit.com/login/)Log in to Reddit. [![Image 1](https://styles.redditmedia.com/t5_djga4z/styles/communityIcon_d3jghm1vyrhe1.PNG?width=96&height=96&frame=1&auto=webp&crop=96%3A96%2Csmart&s=2f71c952da2c033b49b3cdcfd1ad551ee5d7cb86) Go to vibecoding](https://www.reddit.com/r/vibecoding/). [r/vibecoding](https://www.reddit.com/r/vibecoding/)•7mo ago. [anotherjmc](https://www.reddit.com/user/anotherjmc/). # Which CLI AI coding tool to use right now? By continuing, you agree to our[User Agreement](https://www.redditinc.com/policies/user-agreement)and acknowledge that you understand the[Privacy Policy](https://www.redditinc.com/policies/privacy-policy). [Best CLI AI coding tools for projects](https://www.reddit.com/answers/2874faf8-a064-4bd2-b7d8-e1a07022ecd9/?q=Best+CLI+AI+coding+tools+for+projects&source=PDP). [Differences between Codex and Sonnet](https://www.reddit.com/answers/0e0c8810-5292-4718-a0b3-faf40ebeb79e/?q=Differences+between+Codex+and+Sonnet&source=PDP). [Recommendations for AI coding agents on Linux](https://www.reddit.com/answers/181480e5-accf-4ebd-9bb6-08984d6f4893/?q=Recommendations+for+AI+coding+agents+on+Linux&source=PDP). [Unique programming languages to explore](https://www.reddit.com/answers/36afd1c3-d059-47a9-8478-bf56dd818dfc/?q=Unique+programming+languages+to+explore&source=PDP). [How to enhance coding with music](https://www.reddit.com/answers/be7736d8-2380-47f5-8269-debf43a9224a/?q=How+to+enhance+coding+with+music&source=PDP). ![Image 2: sunset](https://emoji.redditmedia.com/9fuii44299oe1_t5_djga4z/sunset). ![Image 3: sunset](https://emoji.redditmedia.com/9fuii44299oe1_t5_djga4z/sunset). [Reddit Rules](https://www.redditinc.com/policies/content-policy)[Privacy Policy](https://www.reddit.com/policies/privacy-policy)[User Agreement](https://www.redditinc.com/policies/user-agreement)[Your Privacy Choices](https://support.reddithelp.com/hc/articles/43980704794004)[Accessibility](https://support.reddithelp.com/hc/sections/38303584022676-Accessibility)[Reddit, Inc. All rights reserved.](https://redditinc.com/). *   [About Reddit](https://www.redditinc.com/). *   [Advertise](https://ads.reddit.com/register?utm_source=web3x_consumer&utm_name=left_nav_cta). *   [Developer Platform](https://developers.reddit.com/?utm_source=reddit&utm_medium=left_nav_resources). *   [Reddit Pro BETA](https://www.reddit.com/reddit-pro?utm_source=reddit&utm_medium=left_nav_resources). *   [Help](https://support.reddithelp.com/hc?utm_source=reddit&utm_medium=footer&utm_campaign=evergreen). *   [Careers](https://www.redditinc.com/careers). *   [Press](https://www.redditinc.com/press). *   [Best of Reddit](https://www.reddit.com/posts/2026/global/). *   [Reddit Rules](https://www.redditinc.com/policies/content-policy). *   [Privacy Policy](https://www.reddit.com/policies/privacy-policy). *   [User Agreement](https://www.redditinc.com/policies/user-agreement). *   [Your Privacy Choices](https://support.reddithelp.com/hc/articles/43980704794004). *   [Accessibility](https://support.reddithelp.com/hc/sections/38303584022676-Accessibility). All rights reserved.](https://redditinc.com/). ![Image 4](https://id.rlcdn.com/472486.gif)."
      },
      {
        "title": "Inside the Agent Harness: How Codex and Claude Code Actually ...",
        "url": "https://medium.com/jonathans-musings/inside-the-agent-harness-how-codex-and-claude-code-actually-work-63593e26c176",
        "description": "A deep technical dive into how CLI coding agents structure their conversations, manage context, and orchestrate tool calls. Jonathan Fulton."
      }
    ]
  }
}

{
  "content": [
    {
      "type": "text",
      "text": "{\n  \"results\": [\n    {\n      \"title\": \"What Are Computer Use Agents? Complete Guide (2026) | Deck\",\n      \"url\": \"https://deck.co/blog/what-are-computer-use-agents\",\n      \"description\": \"Blog / What Are Computer Use Agents? # What Are Computer Use Agents? A computer use agent is an AI system that interacts with a computer the same way a human does — it sees the screen, moves the cursor, clicks buttons, types text, and navigates software to complete tasks autonomously. Unlike traditional RPA and scripted automation, which typically require custom code or brittle selectors tied to specific UIs, computer use agents understand graphical interfaces visually and can work with virtually any application out of the box. In 2026, computer use agents have moved from research labs into production. For a market-level view, see The State of Computer Use Agents in 2026. 1. How Computer Use Agents Work. A computer use agent clicks the button labeled “Submit Invoice” — and finds it whether it’s in a web browser, a desktop app, or a legacy system built in 1998. Claude Computer Use · Best Computer Use Agent Platforms in 2026 · Computer Use Agent Security: What Enterprise Teams Need to Know*.\"\n    },\n    {\n      \"title\": \"Computer Use Agents Explained: AI That Controls Your Browser and Desktop | Agentmelt\",\n      \"url\": \"https://agentmelt.com/blog/computer-use-agents-explained/\",\n      \"description\": \"# Computer Use Agents Explained: AI That Controls Your Browser and Desktop. ## How computer use agents work. Computer use agents identify elements visually, making them resilient to minor UI changes. Teams using computer use agents for QA report 40-60% less test maintenance compared to traditional selector-based automation. Computer use agents automate these without any integration work. A computer use agent can work across all of them in a single flow:. ## Setting up a computer use agent. Some setups use a human to complete the login step, then hand off to the agent. Computer use agents are not the right choice when an API exists. Use computer use agents only when:. **Speed limitations.** A computer use agent processes one action every 2-5 seconds. ## The future of computer use agents. For teams building multi-agent systems, a computer use agent can serve as the \\\"hands\\\" that interact with any software while other agents handle planning and decision-making.\"\n    },\n    {\n      \"title\": \"Anthropic’s Computer Use versus OpenAI’s Computer Using Agent (CUA) — WorkOS\",\n      \"url\": \"https://workos.com/blog/anthropics-computer-use-versus-openais-computer-using-agent-cua\",\n      \"description\": \"# Anthropic’s Computer Use versus OpenAI’s Computer Using Agent (CUA). Anthropic’s Computer Use gives Claude direct control over your desktop, letting it interact with native apps and the web like a human. OpenAI’s Computer Using Agent runs GPT-4o in a secure virtual browser, where it follows high-level instructions to navigate web UIs and complete tasks. They’re early glimpses of autonomous agents that can handle real tasks on your behalf — booking meetings, updating spreadsheets, troubleshooting software, even managing multi-step workflows.In this post, we break down the architecture, design philosophies, and practical implications of each approach — from Claude’s human-like control of your own machine to GPT-4o’s browser-based virtual autonomy. Unlike traditional AI tools that rely on specific APIs or integrations, Computer Use enables Claude to “see” your screen, understand what's displayed, and interact with any software or website just like a human user would.The technology changes how AI systems interact with software.\"\n    },\n    {\n      \"title\": \"Automate web and desktop apps with computer use (preview) - Microsoft Copilot Studio | Microsoft Learn\",\n      \"url\": \"https://learn.microsoft.com/en-us/microsoft-copilot-studio/computer-use\",\n      \"description\": \"Computer use is a tool in Copilot Studio that lets your agent interact with and automate tasks on a Windows computer. On a new agent or an existing agent in Copilot Studio, add computer use as a tool by following these steps:. You can use computer use to extract data from websites or applications and then pass that data to your agent or to other tools in the agent. If you want to use the extracted data with another tool (for example, sending it by email), specify that need in the **agent instructions** and make sure you add both tools to the agent. Example: *Use computer use to extract the client information for the financial portfolio dashboard and email the extracted data.* In this case, your agent must include both the *computer use* tool and the *email* tool. For example, if you configure computer use to fill out a web-based timesheet form, the run might perform the following steps:.\"\n    },\n    {\n      \"title\": \"The Best Web Agents: Computer Use vs Operator vs Browser Use\",\n      \"url\": \"https://www.helicone.ai/blog/browser-use-vs-computer-use-vs-operator\",\n      \"description\": \"Helicone - Open-source LLM observability and monitoring platform for developers. # The Best Web Agents: Computer Use vs Operator vs Browser Use. The relentless hype around AI agents has given birth to some rather remarkable tools. Browser Use vs Computer Use vs Operator. In this comparison, we'll examine the best ones: **Anthropic's Computer Use**, **OpenAI's Operator**, and **Browser Use** and help you decide which one to use. | Feature | Browser Use | Computer Use | Operator |. Comparing the leading web agents, Claude Computer Use and OpenAI Operator, the benchmarks indicate that while Operator currently leads in web navigation tasks, Anthropic's Computer Use demonstrates superior performance in coding and software development tasks. |  | Browser Use | Computer Use | Operator |. ## How Browser Use Works. ## How Claude Computer Use Works. Browser Use stands out for flexibility in model choice and customization options, Operator provides the best user experience, while Computer Use offers the deepest visual understanding.\"\n    },\n    {\n      \"title\": \"Comparing Agentic Commerce Protocol vs Computer Use and ...\",\n      \"url\": \"https://rupakganguly.com/posts/comparing-agentic-commerce-protocol-vs-computer-use-and-browser-use/\",\n      \"description\": \"Computer-use and browser-use simulate human interactions through GUIs or headless browsers. · ACP replaces click-simulation with structured, API-\"\n    },\n    {\n      \"title\": \"Computer use agents: What they are, how they work, and how to deploy them safely\",\n      \"url\": \"https://toloka.ai/blog/computer-use-agents-what-they-are-how-they-work-and-how-to-deploy-them-safely/\",\n      \"description\": \"We now have computer-use agents, which are AI agents capable of perceiving computer screens, understanding the graphical user interfaces (GUIs), and performing tasks exactly as humans would. ## What are computer use agents? In the simplest term possible, computer use agents are autonomous AI systems designed to interact with any software through its existing user interfaces. ### Operator as a real-world example of a computer use agent. ## Why computer use agents reshape systems and data security. With computer use agents, we now have to secure both the data and the actions taking place in real time. ## Testing computer use agents before deployment. ## Real world deployment of computer use agents. Computer use agents, more than any other AI system, demand continuous monitoring in production. One of the most immediate and valuable applications for computer use agents is in modernizing business operations, particularly the labor-intensive domains of data entry and legacy systems integration. ## The future of computer use and AI Agent development.\"\n    },\n    {\n      \"title\": \"Best computer use agents right now? Need something for browser ...\",\n      \"url\": \"https://www.reddit.com/r/AI_Agents/comments/1t0hduq/best_computer_use_agents_right_now_need_something/\",\n      \"description\": \"Need something for browser research + desktop tasks : r/AI_Agents. [Skip to main content](https://www.reddit.com/r/AI_Agents/comments/1t0hduq/best_computer_use_agents_right_now_need_something/#main-content)Best computer use agents right now? Need something for browser research + desktop tasks : r/AI_Agents. Open menu Open navigation[](https://www.reddit.com/)Go to Reddit Home. Get App Get the Reddit app [Log In](https://www.reddit.com/login/)Log in to Reddit. [![Image 1](https://styles.redditmedia.com/t5_8b5cvj/styles/communityIcon_izy2luu72nee1.png?width=96&height=96&frame=1&auto=webp&crop=96%3A96%2Csmart&s=20cf99757a0ac0f6735e20e69bab9bb46f409207) Go to AI_Agents](https://www.reddit.com/r/AI_Agents/). [r/AI_Agents](https://www.reddit.com/r/AI_Agents/)•5d ago. [Salt-Library-8073](https://www.reddit.com/user/Salt-Library-8073/). [Resource Request](https://www.reddit.com/r/AI_Agents/?f=flair_name%3A%22Resource%20Request%22). By continuing, you agree to our[User Agreement](https://www.redditinc.com/policies/user-agreement)and acknowledge that you understand the[Privacy Policy](https://www.redditinc.com/policies/privacy-policy). [AI agents that control desktop applications](https://www.reddit.com/answers/97df647d-5c80-4f45-b01e-7fb383340be8/?q=AI+agents+that+control+desktop+applications&source=PDP). [Open source AI agents for browser use](https://www.reddit.com/answers/efd43990-d082-4aa7-a657-939bfa3a4b2f/?q=Open+source+AI+agents+for+browser+use&source=PDP). [Most innovative uses of AI agents today](https://www.reddit.com/answers/c168bdc3-d40a-4418-b7df-c03090430c23/?q=Most+innovative+uses+of+AI+agents+today&source=PDP). [Challenges in developing autonomous AI agents](https://www.reddit.com/answers/c559b02d-6a13-45d6-8c9b-83c04f7288f4/?q=Challenges+in+developing+autonomous+AI+agents&source=PDP). [Best tools for building AI agents](https://www.reddit.com/answers/e8229ad9-3183-48bd-b375-fb2495ebce66/?q=Best+tools+for+building+AI+agents&source=PDP). [Reddit Rules](https://www.redditinc.com/policies/content-policy)[Privacy Policy](https://www.reddit.com/policies/privacy-policy)[User Agreement](https://www.redditinc.com/policies/user-agreement)[Your Privacy Choices](https://support.reddithelp.com/hc/articles/43980704794004)[Accessibility](https://support.reddithelp.com/hc/sections/38303584022676-Accessibility)[Reddit, Inc. All rights reserved.](https://redditinc.com/). *   [About Reddit](https://www.redditinc.com/). *   [Advertise](https://ads.reddit.com/register?utm_source=web3x_consumer&utm_name=left_nav_cta). *   [Developer Platform](https://developers.reddit.com/?utm_source=reddit&utm_medium=left_nav_resources). *   [Reddit Pro BETA](https://www.reddit.com/reddit-pro?utm_source=reddit&utm_medium=left_nav_resources). *   [Help](https://support.reddithelp.com/hc?utm_source=reddit&utm_medium=footer&utm_campaign=evergreen). *   [Careers](https://www.redditinc.com/careers). *   [Press](https://www.redditinc.com/press). *   [Best of Reddit](https://www.reddit.com/posts/2026/global/). *   [Reddit Rules](https://www.redditinc.com/policies/content-policy). *   [Privacy Policy](https://www.reddit.com/policies/privacy-policy). *   [User Agreement](https://www.redditinc.com/policies/user-agreement). *   [Your Privacy Choices](https://support.reddithelp.com/hc/articles/43980704794004). *   [Accessibility](https://support.reddithelp.com/hc/sections/38303584022676-Accessibility). All rights reserved.](https://redditinc.com/). ![Image 2](https://id.rlcdn.com/472486.gif).\"\n    },\n    {\n      \"title\": \"What Is a Computer Using Agent (CUA)? RPA Comparison + Copilot ...\",\n      \"url\": \"https://www.youtube.com/watch?v=tOt4axyntXs\",\n      \"description\": \"Automation is changing fast, and clicking buttons is no longer enough. In this video, I break down Computer Using Agents (CUA) and how they\"\n    }\n  ]\n}"
    }
  ],
  "details": {
    "results": [
      {
        "title": "What Are Computer Use Agents? Complete Guide (2026) | Deck",
        "url": "https://deck.co/blog/what-are-computer-use-agents",
        "description": "Blog / What Are Computer Use Agents? # What Are Computer Use Agents? A computer use agent is an AI system that interacts with a computer the same way a human does — it sees the screen, moves the cursor, clicks buttons, types text, and navigates software to complete tasks autonomously. Unlike traditional RPA and scripted automation, which typically require custom code or brittle selectors tied to specific UIs, computer use agents understand graphical interfaces visually and can work with virtually any application out of the box. In 2026, computer use agents have moved from research labs into production. For a market-level view, see The State of Computer Use Agents in 2026. 1. How Computer Use Agents Work. A computer use agent clicks the button labeled “Submit Invoice” — and finds it whether it’s in a web browser, a desktop app, or a legacy system built in 1998. Claude Computer Use · Best Computer Use Agent Platforms in 2026 · Computer Use Agent Security: What Enterprise Teams Need to Know*."
      },
      {
        "title": "Computer Use Agents Explained: AI That Controls Your Browser and Desktop | Agentmelt",
        "url": "https://agentmelt.com/blog/computer-use-agents-explained/",
        "description": "# Computer Use Agents Explained: AI That Controls Your Browser and Desktop. ## How computer use agents work. Computer use agents identify elements visually, making them resilient to minor UI changes. Teams using computer use agents for QA report 40-60% less test maintenance compared to traditional selector-based automation. Computer use agents automate these without any integration work. A computer use agent can work across all of them in a single flow:. ## Setting up a computer use agent. Some setups use a human to complete the login step, then hand off to the agent. Computer use agents are not the right choice when an API exists. Use computer use agents only when:. **Speed limitations.** A computer use agent processes one action every 2-5 seconds. ## The future of computer use agents. For teams building multi-agent systems, a computer use agent can serve as the \"hands\" that interact with any software while other agents handle planning and decision-making."
      },
      {
        "title": "Anthropic’s Computer Use versus OpenAI’s Computer Using Agent (CUA) — WorkOS",
        "url": "https://workos.com/blog/anthropics-computer-use-versus-openais-computer-using-agent-cua",
        "description": "# Anthropic’s Computer Use versus OpenAI’s Computer Using Agent (CUA). Anthropic’s Computer Use gives Claude direct control over your desktop, letting it interact with native apps and the web like a human. OpenAI’s Computer Using Agent runs GPT-4o in a secure virtual browser, where it follows high-level instructions to navigate web UIs and complete tasks. They’re early glimpses of autonomous agents that can handle real tasks on your behalf — booking meetings, updating spreadsheets, troubleshooting software, even managing multi-step workflows.In this post, we break down the architecture, design philosophies, and practical implications of each approach — from Claude’s human-like control of your own machine to GPT-4o’s browser-based virtual autonomy. Unlike traditional AI tools that rely on specific APIs or integrations, Computer Use enables Claude to “see” your screen, understand what's displayed, and interact with any software or website just like a human user would.The technology changes how AI systems interact with software."
      },
      {
        "title": "Automate web and desktop apps with computer use (preview) - Microsoft Copilot Studio | Microsoft Learn",
        "url": "https://learn.microsoft.com/en-us/microsoft-copilot-studio/computer-use",
        "description": "Computer use is a tool in Copilot Studio that lets your agent interact with and automate tasks on a Windows computer. On a new agent or an existing agent in Copilot Studio, add computer use as a tool by following these steps:. You can use computer use to extract data from websites or applications and then pass that data to your agent or to other tools in the agent. If you want to use the extracted data with another tool (for example, sending it by email), specify that need in the **agent instructions** and make sure you add both tools to the agent. Example: *Use computer use to extract the client information for the financial portfolio dashboard and email the extracted data.* In this case, your agent must include both the *computer use* tool and the *email* tool. For example, if you configure computer use to fill out a web-based timesheet form, the run might perform the following steps:."
      },
      {
        "title": "The Best Web Agents: Computer Use vs Operator vs Browser Use",
        "url": "https://www.helicone.ai/blog/browser-use-vs-computer-use-vs-operator",
        "description": "Helicone - Open-source LLM observability and monitoring platform for developers. # The Best Web Agents: Computer Use vs Operator vs Browser Use. The relentless hype around AI agents has given birth to some rather remarkable tools. Browser Use vs Computer Use vs Operator. In this comparison, we'll examine the best ones: **Anthropic's Computer Use**, **OpenAI's Operator**, and **Browser Use** and help you decide which one to use. | Feature | Browser Use | Computer Use | Operator |. Comparing the leading web agents, Claude Computer Use and OpenAI Operator, the benchmarks indicate that while Operator currently leads in web navigation tasks, Anthropic's Computer Use demonstrates superior performance in coding and software development tasks. |  | Browser Use | Computer Use | Operator |. ## How Browser Use Works. ## How Claude Computer Use Works. Browser Use stands out for flexibility in model choice and customization options, Operator provides the best user experience, while Computer Use offers the deepest visual understanding."
      },
      {
        "title": "Comparing Agentic Commerce Protocol vs Computer Use and ...",
        "url": "https://rupakganguly.com/posts/comparing-agentic-commerce-protocol-vs-computer-use-and-browser-use/",
        "description": "Computer-use and browser-use simulate human interactions through GUIs or headless browsers. · ACP replaces click-simulation with structured, API-"
      },
      {
        "title": "Computer use agents: What they are, how they work, and how to deploy them safely",
        "url": "https://toloka.ai/blog/computer-use-agents-what-they-are-how-they-work-and-how-to-deploy-them-safely/",
        "description": "We now have computer-use agents, which are AI agents capable of perceiving computer screens, understanding the graphical user interfaces (GUIs), and performing tasks exactly as humans would. ## What are computer use agents? In the simplest term possible, computer use agents are autonomous AI systems designed to interact with any software through its existing user interfaces. ### Operator as a real-world example of a computer use agent. ## Why computer use agents reshape systems and data security. With computer use agents, we now have to secure both the data and the actions taking place in real time. ## Testing computer use agents before deployment. ## Real world deployment of computer use agents. Computer use agents, more than any other AI system, demand continuous monitoring in production. One of the most immediate and valuable applications for computer use agents is in modernizing business operations, particularly the labor-intensive domains of data entry and legacy systems integration. ## The future of computer use and AI Agent development."
      },
      {
        "title": "Best computer use agents right now? Need something for browser ...",
        "url": "https://www.reddit.com/r/AI_Agents/comments/1t0hduq/best_computer_use_agents_right_now_need_something/",
        "description": "Need something for browser research + desktop tasks : r/AI_Agents. [Skip to main content](https://www.reddit.com/r/AI_Agents/comments/1t0hduq/best_computer_use_agents_right_now_need_something/#main-content)Best computer use agents right now? Need something for browser research + desktop tasks : r/AI_Agents. Open menu Open navigation[](https://www.reddit.com/)Go to Reddit Home. Get App Get the Reddit app [Log In](https://www.reddit.com/login/)Log in to Reddit. [![Image 1](https://styles.redditmedia.com/t5_8b5cvj/styles/communityIcon_izy2luu72nee1.png?width=96&height=96&frame=1&auto=webp&crop=96%3A96%2Csmart&s=20cf99757a0ac0f6735e20e69bab9bb46f409207) Go to AI_Agents](https://www.reddit.com/r/AI_Agents/). [r/AI_Agents](https://www.reddit.com/r/AI_Agents/)•5d ago. [Salt-Library-8073](https://www.reddit.com/user/Salt-Library-8073/). [Resource Request](https://www.reddit.com/r/AI_Agents/?f=flair_name%3A%22Resource%20Request%22). By continuing, you agree to our[User Agreement](https://www.redditinc.com/policies/user-agreement)and acknowledge that you understand the[Privacy Policy](https://www.redditinc.com/policies/privacy-policy). [AI agents that control desktop applications](https://www.reddit.com/answers/97df647d-5c80-4f45-b01e-7fb383340be8/?q=AI+agents+that+control+desktop+applications&source=PDP). [Open source AI agents for browser use](https://www.reddit.com/answers/efd43990-d082-4aa7-a657-939bfa3a4b2f/?q=Open+source+AI+agents+for+browser+use&source=PDP). [Most innovative uses of AI agents today](https://www.reddit.com/answers/c168bdc3-d40a-4418-b7df-c03090430c23/?q=Most+innovative+uses+of+AI+agents+today&source=PDP). [Challenges in developing autonomous AI agents](https://www.reddit.com/answers/c559b02d-6a13-45d6-8c9b-83c04f7288f4/?q=Challenges+in+developing+autonomous+AI+agents&source=PDP). [Best tools for building AI agents](https://www.reddit.com/answers/e8229ad9-3183-48bd-b375-fb2495ebce66/?q=Best+tools+for+building+AI+agents&source=PDP). [Reddit Rules](https://www.redditinc.com/policies/content-policy)[Privacy Policy](https://www.reddit.com/policies/privacy-policy)[User Agreement](https://www.redditinc.com/policies/user-agreement)[Your Privacy Choices](https://support.reddithelp.com/hc/articles/43980704794004)[Accessibility](https://support.reddithelp.com/hc/sections/38303584022676-Accessibility)[Reddit, Inc. All rights reserved.](https://redditinc.com/). *   [About Reddit](https://www.redditinc.com/). *   [Advertise](https://ads.reddit.com/register?utm_source=web3x_consumer&utm_name=left_nav_cta). *   [Developer Platform](https://developers.reddit.com/?utm_source=reddit&utm_medium=left_nav_resources). *   [Reddit Pro BETA](https://www.reddit.com/reddit-pro?utm_source=reddit&utm_medium=left_nav_resources). *   [Help](https://support.reddithelp.com/hc?utm_source=reddit&utm_medium=footer&utm_campaign=evergreen). *   [Careers](https://www.redditinc.com/careers). *   [Press](https://www.redditinc.com/press). *   [Best of Reddit](https://www.reddit.com/posts/2026/global/). *   [Reddit Rules](https://www.redditinc.com/policies/content-policy). *   [Privacy Policy](https://www.reddit.com/policies/privacy-policy). *   [User Agreement](https://www.redditinc.com/policies/user-agreement). *   [Your Privacy Choices](https://support.reddithelp.com/hc/articles/43980704794004). *   [Accessibility](https://support.reddithelp.com/hc/sections/38303584022676-Accessibility). All rights reserved.](https://redditinc.com/). ![Image 2](https://id.rlcdn.com/472486.gif)."
      },
      {
        "title": "What Is a Computer Using Agent (CUA)? RPA Comparison + Copilot ...",
        "url": "https://www.youtube.com/watch?v=tOt4axyntXs",
        "description": "Automation is changing fast, and clicking buttons is no longer enough. In this video, I break down Computer Using Agents (CUA) and how they"
      }
    ]
  }
}

{
  "content": [
    {
      "type": "text",
      "text": "{\n  \"url\": \"https://realpython.com/ai-coding-agents-guide/\",\n  \"title\": \"AI Coding Agents Guide: A Map of the Four Workflow Types\",\n  \"author\": \"Ben Batman\",\n  \"summary\": \"AI coding agents come in four types: IDE, terminal, PR, and cloud. Learn how each workflow fits into modern Python development.\",\n  \"content\": \"<div id=\\\"readability-page-1\\\" class=\\\"page\\\"><p><span>by <a href=\\\"#author\\\">Ben Batman</a>\\n    \\n    \\n      <span><svg role=\\\"img\\\"><use href=\\\"/static/icons.ea74988d8a7e.svg#@date\\\"></use></svg><span>Publication date</span></span> Apr 29, 2026\\n    \\n    \\n      <span><svg role=\\\"img\\\"><use href=\\\"/static/icons.ea74988d8a7e.svg#regular--book-open\\\"></use></svg><span>Reading time estimate</span></span> 15m\\n    \\n    \\n      <a href=\\\"#reader-comments\\\"></a>\\n    \\n    \\n\\n    <span> \\n      \\n      \\n        \\n<a href=\\\"https://realpython.com/tutorials/intermediate/\\\" data-previewable=\\\"\\\">intermediate</a>\\n\\n      \\n        \\n<a href=\\\"https://realpython.com/tutorials/ai/\\\" data-previewable=\\\"\\\">ai</a>\\n\\n      \\n    </span>\\n\\n    \\n  </span></p><div>\\n    \\n      \\n    \\n\\n    \\n    \\n    \\n\\n    \\n\\n    <p>AI coding agents can read your code, reason about changes, and act on your behalf. To choose the right one, it helps to understand the four common workflow types: integrated development environment (IDE), terminal, pull request (PR), and cloud.</p>\\n<p><strong>In this tutorial, you’ll</strong>:</p>\\n<ul>\\n<li><strong>Identify</strong> the four common <strong>agent interaction modes</strong></li>\\n<li><strong>Understand</strong> what makes <strong>each workflow distinct</strong></li>\\n<li><strong>Recognize</strong> which mode fits <strong>common development scenarios</strong></li>\\n<li><strong>Weigh</strong> the <strong>risks and tradeoffs</strong> of each workflow</li>\\n</ul>\\n<p>Before exploring the four workflow types, it’s worth looking at what makes a coding tool <a href=\\\"https://realpython.com/ref/ai-coding-glossary/agentic-coding/\\\">agentic</a> in the first place.</p>\\n<div>\\n\\n  <p><mark><strong> Take the Quiz:</strong></mark> Test your knowledge with our interactive “AI Coding Agents Guide: A Map of the Four Workflow Types” quiz. You’ll receive a score upon completion to help you track your learning progress:</p>\\n\\n  <hr>\\n\\n  <div>\\n\\n      <a href=\\\"https://realpython.com/quizzes/ai-coding-agents-guide/\\\" tabindex=\\\"-1\\\">\\n        <div>\\n\\n            <p><img alt=\\\"A person in overalls pointing at a four-piece puzzle map labeled IDE, Cloud, CLI, and PR/Repo, with a map info legend beside it and a Python logo.\\\" src=\\\"https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg\\\" width=\\\"1920\\\" height=\\\"1080\\\" srcset=\\\"https://realpython.com/cdn-cgi/image/width=480,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 480w, https://realpython.com/cdn-cgi/image/width=640,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 640w, https://realpython.com/cdn-cgi/image/width=960,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 960w, https://realpython.com/cdn-cgi/image/width=1920,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 1920w\\\" sizes=\\\"(min-width: 1200px) 142px, (min-width: 1000px) 122px, (min-width: 780px) 112px, (min-width: 580px) 139px, calc(100vw - 62px)\\\"></p>\\n        </div>\\n      </a>\\n\\n    </div>\\n\\n</div>\\n\\n\\n<section><h2 id=\\\"understanding-ai-coding-agents\\\">Understanding AI Coding Agents<a href=\\\"#understanding-ai-coding-agents\\\" title=\\\"Permanent link\\\"></a></h2>\\n<p>While standard chatbots provide one-off answers, coding agents are designed for autonomy, operating through a continuous execution loop to solve complex tasks. This loop typically follows four distinct steps:</p>\\n<ol>\\n<li><strong>Read</strong>: They read relevant files from your codebase to form their context.</li>\\n<li><strong>Reason</strong>: They determine the logical steps needed to achieve your goal.</li>\\n<li><strong>Act</strong>: They execute those steps by editing files, running terminal commands, or using external tools.</li>\\n<li><strong>Evaluate</strong>: They check the results of their actions to see if more work is needed.</li>\\n</ol>\\n<p>This loop repeats until the task is completed or the agent hands control back to you. Unlike simple predictive text or one-off prompts, agents bridge the gap between suggestion and execution by autonomously navigating the development workflow.</p>\\n<p>The core agent loop will generally stay the same, but where an agent runs will shape how you interact with it:</p>\\n<ul>\\n<li><strong>In an editor</strong>, it works alongside you.</li>\\n<li><strong>In a terminal</strong>, you guide it step by step.</li>\\n<li><strong>In pull requests</strong>, it reviews changes asynchronously.</li>\\n<li><strong>In the cloud</strong>, it works in a managed environment and reports back later.</li>\\n</ul>\\n<p>These environments define four primary agent types, each enabling a distinct workflow: <strong>IDE agents</strong>, <strong>terminal agents</strong>, <strong>PR agents</strong>, and <strong>cloud agents</strong>.</p>\\n</section><section><h2 id=\\\"exploring-the-four-workflow-types\\\">Exploring the Four Workflow Types<a href=\\\"#exploring-the-four-workflow-types\\\" title=\\\"Permanent link\\\"></a></h2>\\n<p>The four workflow types describe interaction modes and don’t always map cleanly to product categories. The same tool often spans multiple workflows. For example, <a href=\\\"https://realpython.com/ref/ai-coding-tools/claude-code/\\\">Claude Code</a> runs in your <a href=\\\"https://code.claude.com/docs/en/overview#terminal\\\">terminal</a>, in your <a href=\\\"https://code.claude.com/docs/en/overview#vs-code\\\">editor</a>, and in the cloud with <a href=\\\"https://code.claude.com/docs/en/claude-code-on-the-web\\\">Claude Code on the web</a>. It can also review pull requests with <a href=\\\"https://code.claude.com/docs/en/code-review\\\">Code Review</a>.</p>\\n<p>The goal is to match the workflow to the task. The diagram below summarizes the four types at a glance:</p>\\n<figure><a href=\\\"https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg\\\" target=\\\"_blank\\\"><img loading=\\\"lazy\\\" src=\\\"https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg\\\" width=\\\"2943\\\" height=\\\"1000\\\" srcset=\\\"https://realpython.com/cdn-cgi/image/width=735,format=auto/https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg 735w, https://realpython.com/cdn-cgi/image/width=981,format=auto/https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg 981w, https://realpython.com/cdn-cgi/image/width=1471,format=auto/https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg 1471w, https://realpython.com/cdn-cgi/image/width=2943,format=auto/https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg 2943w\\\" sizes=\\\"(min-width: 1200px) 690px, (min-width: 780px) calc(-5vw + 669px), (min-width: 580px) 510px, calc(100vw - 30px)\\\" alt=\\\"AI Agent Workflow Type Table\\\" data-asset=\\\"6908\\\"></a><figcaption>The Four Coding Agent Workflows</figcaption></figure>\\n\\n<p>This chart gives you a quick reference for comparing the four types. The sections below dig into each one.</p>\\n<section><h3 id=\\\"ide-agents\\\">IDE Agents<a href=\\\"#ide-agents\\\" title=\\\"Permanent link\\\"></a></h3>\\n<p><a href=\\\"https://realpython.com/ref/glossary/ide/\\\">IDE</a> agents live inside your code editor and work alongside you in real time. They suggest edits inline, show visual diffs, and let you accept or reject changes without leaving your editing environment.</p>\\n<p>This category has two common forms. <strong>AI-native IDEs</strong> such as <a href=\\\"https://realpython.com/courses/tips-using-ai-coding-editor-cursor/\\\">Cursor</a>, <a href=\\\"https://realpython.com/ref/ai-coding-tools/windsurf/\\\">Windsurf</a>, and <a href=\\\"https://realpython.com/ref/ai-coding-tools/kiro/\\\">Kiro</a> are built from the ground up around AI capabilities. Some AI-native IDEs, especially tools like Kiro, support a more <a href=\\\"https://kiro.dev/docs/specs/\\\">spec-driven workflow</a> where you describe the task upfront and let the agent work through it.</p>\\n<p><strong>IDE integrations</strong> like the <a href=\\\"https://realpython.com/github-copilot-python/\\\">GitHub Copilot</a> extension, the <a href=\\\"https://code.claude.com/docs/en/vs-code\\\">Claude Code in VS Code</a> extension, and <a href=\\\"https://developers.google.com/gemini-code-assist/docs/overview\\\">Gemini Code Assist</a> add agent features to editors you already use. Compared to AI-native IDEs, IDE integrations usually fit a more file-targeted workflow centered on interactive editing and <a href=\\\"https://realpython.com/ref/best-practices/refactoring/\\\">refactoring</a>. However, the choice is developer-specific, so try a few and see which style suits your workflow.</p>\\n<p>Keep in mind that cloud-backed IDE agents send your code to external servers for processing. Often, teams require approved tooling or local-only models for privacy reasons. Tools like <a href=\\\"https://docs.continue.dev/customize/models\\\">Continue</a> let you run models locally if your code can’t leave your machine.</p>\\n</section><section><h3 id=\\\"terminal-agents\\\">Terminal Agents<a href=\\\"#terminal-agents\\\" title=\\\"Permanent link\\\"></a></h3>\\n<p>Terminal agents run in your shell. You describe a task, and the agent reads files, proposes edits, and runs commands. You generally approve or reject each step before the agent moves on.</p>\\n<p>The <a href=\\\"https://realpython.com/terminal-commands/\\\">terminal</a> workflow works well for complex changes and navigating large codebases. You can point the agent at your whole project and let it trace through imports and related files, and propose coordinated changes across many modules. Terminal agents are also helpful when you’re jumping into a new codebase and need to get up to speed quickly. This category includes tools such as <a href=\\\"https://realpython.com/courses/getting-started-claude-code/\\\">Claude Code</a>, <a href=\\\"https://realpython.com/ref/ai-coding-tools/aider/\\\">Aider</a>, <a href=\\\"https://realpython.com/how-to-use-gemini-cli/\\\">Gemini CLI</a>, <a href=\\\"https://realpython.com/ref/ai-coding-tools/opencode/\\\">OpenCode</a>, and <a href=\\\"https://realpython.com/codex-cli/\\\">Codex CLI</a>.</p>\\n<p>For a direct comparison of two of the tools in this list, Real Python’s tutorial on <a href=\\\"https://realpython.com/gemini-cli-vs-claude-code/\\\">Gemini CLI vs Claude Code</a> walks through how each one handles common Python tasks.</p>\\n\\n<p>Because terminal agents operate in the shell, they integrate seamlessly with your existing development workflow. The most common way to interact with them is to launch them in your terminal and use their built-in interface. More advanced workflows include steps such as piping logs into them, chaining them with other <a href=\\\"https://realpython.com/ref/glossary/command-line-interface/\\\">CLI</a> tools, and running them inside automation scripts.</p>\\n<p>When you run the agent interactively, the step-by-step approval model gives you high control while still letting the agent handle the heavy lifting. If you want longer sessions without continuous approval prompts, you can explore <a href=\\\"https://code.claude.com/docs/en/permission-modes#eliminate-prompts-with-auto-mode\\\">auto mode</a> in Claude Code.</p>\\n<p>Some terminal agents can connect to local models through tools like <a href=\\\"https://realpython.com/ollama/\\\">Ollama</a>. If you’re unable to send code to external services due to company policies on proprietary code, a local model setup can be a good option to explore.</p>\\n</section><section><h3 id=\\\"pull-request-agents\\\">Pull Request Agents<a href=\\\"#pull-request-agents\\\" title=\\\"Permanent link\\\"></a></h3>\\n<p>Pull request (PR) agents are structurally different from the other three types. They’re <strong>asynchronous</strong>, meaning you don’t watch the agent work in real time. Instead, the agent often triggers automatically when a pull request is opened or updated. It runs on its own schedule, flags issues, suggests fixes, and leaves comments for you to review.</p>\\n<p>This workflow generally operates on shared branches visible to everyone on the team, not on your local workspace. The verification process involves human code review. The agent flags potential bugs, style violations, and logic issues, but a human reviewer makes the final call on whether to merge. In other words, PR agents usually act as a safety net before merging rather than as a tool you steer live while coding.</p>\\n<p>PR-agent workflows typically center on <a href=\\\"https://realpython.com/ref/best-practices/version-control/\\\">version control</a> platforms like GitHub, GitLab, and Bitbucket. Tools like <a href=\\\"https://docs.coderabbit.ai/\\\">CodeRabbit</a> and <a href=\\\"https://docs.github.com/en/copilot/concepts/agents/code-review\\\">GitHub Copilot code review</a> support this workflow. Even so, they don’t have to be triggered <em>only</em> from your version control platform. For example, GitHub Copilot code review can also be requested from places like <a href=\\\"https://docs.github.com/en/copilot/how-tos/use-copilot-agents/request-a-code-review/use-code-review?tool=vscode\\\">VS Code</a>, the <a href=\\\"https://docs.github.com/en/copilot/how-tos/copilot-cli/use-copilot-cli-agents/agentic-code-review\\\">GitHub Copilot CLI</a>, <a href=\\\"https://docs.github.com/en/copilot/how-tos/use-copilot-agents/request-a-code-review/use-code-review?tool=mobile\\\">a mobile device</a>, and more.</p>\\n<p>In practice, the workflow looks like this: You open a pull request, and after some time, the agent posts a review with comments about your code. It might catch an unhandled edge case, flag a missing test, or suggest a cleaner approach. You respond to its comments just as you would to a teammate’s review by accepting the suggestion, pushing a fix, or dismissing it.</p>\\n<p>Keep in mind that on many teams, repository-level AI tools are approved or blocked centrally, so privacy decisions often happen at the organizational level rather than the individual level.</p>\\n</section><section><h3 id=\\\"cloud-agents\\\">Cloud Agents<a href=\\\"#cloud-agents\\\" title=\\\"Permanent link\\\"></a></h3>\\n<p>Cloud agents generally offer the most autonomy. You describe a task, the agent works in a remote or managed environment, and later reports back with a branch, pull request, or prototype.</p>\\n<p>This makes cloud agents a good fit for <a href=\\\"https://en.wikipedia.org/wiki/Greenfield_project\\\">greenfield prototyping</a> or work that takes longer than you’d want to sit and supervise. This category includes tools such as <a href=\\\"https://realpython.com/ref/ai-coding-tools/devin/\\\">Devin</a>, <a href=\\\"https://code.claude.com/docs/en/claude-code-on-the-web\\\">Claude Code on the web</a>, <a href=\\\"https://developers.openai.com/codex/cloud\\\">Codex web</a>, and <a href=\\\"https://realpython.com/ref/ai-coding-tools/cursor/\\\">Cursor’s</a> <a href=\\\"https://cursor.com/docs/cloud-agent\\\">Cloud Agents</a>.</p>\\n<p>You can often access cloud agents through Slack, issue trackers, or a web browser. For example, you can mention Claude with <code>@Claude</code> in <a href=\\\"https://code.claude.com/docs/en/slack\\\">Slack</a> and ask it to complete a specific task for you, and it will spin up a Claude Code session on the web.</p>\\n<p>There’s a tradeoff, though. You get more autonomy but often give up real-time control, because your code runs on infrastructure outside your local machine. That remote execution makes cloud agents most useful when the task is clearly scoped and the output is easy to review, such as a branch, pull request, or prototype.</p>\\n<p>Not every cloud agent uses the same execution model. Claude Code on the web runs on Anthropic-managed cloud infrastructure. <a href=\\\"https://docs.github.com/en/copilot/concepts/agents/cloud-agent/about-cloud-agent\\\">GitHub’s Copilot cloud agent</a> runs in an ephemeral development environment powered by <a href=\\\"https://realpython.com/github-actions-python/\\\">GitHub Actions</a>. Cursor’s Cloud Agents can also run on machines you control through <a href=\\\"https://cursor.com/docs/cloud-agent/my-machines\\\">My Machines</a>. In short, where a cloud agent actually executes depends on the vendor, so factor that into your privacy compliance checks.</p>\\n<p>As with all AI-generated code, human review remains essential. That matters even more with cloud agents because they operate with a high level of autonomy. Every team has its own guidelines on working with AI-generated code, but a good rule of thumb is to never push or ship code that you haven’t laid eyes on.</p>\\n<p>Many cloud agents rely on vendor-managed infrastructure, and some organizations block them due to security or compliance requirements. Other cloud agents can run against machines you control. Either way, check your company’s policy before using them with proprietary code.</p>\\n</section></section><section><h2 id=\\\"navigating-category-overlap\\\">Navigating Category Overlap<a href=\\\"#navigating-category-overlap\\\" title=\\\"Permanent link\\\"></a></h2>\\n<p>Tool overlap is common. Three tools make that especially clear because each one shows up across all four workflow types.</p>\\n<ul>\\n<li>\\n<p><a href=\\\"https://code.claude.com/docs/en/overview\\\"><strong>Claude Code</strong></a> spans all four workflows. In the <a href=\\\"https://code.claude.com/docs/en/terminal-guide\\\">terminal</a>, it works as a shell-based agent. In an IDE, it has native integrations for editors like <a href=\\\"https://code.claude.com/docs/en/vs-code\\\">VS Code</a> and <a href=\\\"https://code.claude.com/docs/en/jetbrains\\\">JetBrains</a>. In the cloud, it runs as Claude Code on the web. For PR workflows, Anthropic offers <a href=\\\"https://code.claude.com/docs/en/code-review\\\">Code Review</a> as a managed service and <a href=\\\"https://code.claude.com/docs/en/github-actions\\\">Claude Code GitHub Actions</a> for teams running their own CI pipelines.</p>\\n</li>\\n<li>\\n<p><a href=\\\"https://realpython.com/courses/tips-using-ai-coding-editor-cursor/\\\"><strong>Cursor</strong></a> covers all four workflows, too. Its main editor experience is its <a href=\\\"https://cursor.com/docs/get-started/quickstart\\\">IDE</a>. <a href=\\\"https://cursor.com/docs/cli/overview\\\">Cursor CLI</a> handles terminal use, <a href=\\\"https://cursor.com/docs/cloud-agent\\\">Cloud Agents</a> manages cloud execution, and <a href=\\\"https://cursor.com/docs/bugbot\\\">Bugbot</a> automates pull request reviews.</p>\\n</li>\\n<li>\\n<p><a href=\\\"https://docs.github.com/en/copilot/get-started/quickstart\\\"><strong>GitHub Copilot</strong></a> also spans all four workflows. You can run it in your <a href=\\\"https://realpython.com/github-copilot-python/\\\">IDE</a>, take it to the terminal with <a href=\\\"https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-getting-started\\\">GitHub Copilot CLI</a>, request PR feedback with <a href=\\\"https://docs.github.com/en/copilot/how-tos/use-copilot-agents/request-a-code-review/use-code-review\\\">GitHub Copilot code review</a>, and hand off background work to a <a href=\\\"https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent\\\">GitHub Copilot cloud agent</a>.</p>\\n</li>\\n</ul>\\n\\n<p>The takeaway is that the taxonomy in this tutorial describes <strong>workflows</strong>, that is, how you’re working. The product might stay the same, but the interaction mode changes depending on where and how you use it. Most agentic coding tools now span more than one category, and that overlap will likely keep growing.</p>\\n</section><section><h2 id=\\\"avoiding-common-pitfalls\\\">Avoiding Common Pitfalls<a href=\\\"#avoiding-common-pitfalls\\\" title=\\\"Permanent link\\\"></a></h2>\\n<p>With agentic coding becoming increasingly powerful, it’s easier than ever to fall into common traps. To get the full benefit of coding agents without compromising quality, privacy, or control, watch out for these mistakes:</p>\\n<ul>\\n<li>\\n<p><strong>Assuming one agent type handles everything</strong>: IDE agents excel at interactive editing, terminal agents handle complex multi-file changes, PR agents catch issues asynchronously, and cloud agents tackle brand-new features and prototyping. Matching the workflow to the task matters more than picking a single tool.</p>\\n</li>\\n<li>\\n<p><strong>Ignoring privacy and compliance constraints</strong>: Many cloud agents run on remote infrastructure. PR agents operate in shared repositories. IDE and terminal agents with cloud backends send code to external <a href=\\\"https://realpython.com/ref/glossary/api/\\\">APIs</a> for inference. Before adopting any agent, check whether your code is allowed to leave your local machine and review your company’s policies on AI tool usage. Some teams can only use local models or self-managed environments.</p>\\n</li>\\n<li>\\n<p><strong>Over-automating without review</strong>: Assume AI-generated code contains mistakes. It may have subtle bugs, weak exception handling, or patterns that don’t match your team’s conventions. Review all generated code carefully before merging. The more autonomous the agent, the more important developer oversight becomes. A careful review is much cheaper than a production failure.</p>\\n</li>\\n</ul>\\n<p>Coding agents pay off when you treat them as collaborators, not replacements. Pick the right agent for each task, respect your team’s privacy boundaries, and keep a human reviewer in the loop.</p>\\n</section><section><h2 id=\\\"conclusion\\\">Conclusion<a href=\\\"#conclusion\\\" title=\\\"Permanent link\\\"></a></h2>\\n<p>Agentic coding is already reshaping how developers write, review, and ship code. Not all agents work the same way, though. The interaction mode matters as much as the tool itself.</p>\\n<p><strong>In this tutorial, you’ve learned how to:</strong></p>\\n<ul>\\n<li><strong>Identify</strong> the <strong>four coding agent workflow types</strong>: IDE, terminal, PR, and cloud                    </li>\\n<li><strong>Understand</strong> what makes <strong>each workflow distinct</strong>, from real-time inline editing to highly autonomous cloud tasks                                              </li>\\n<li><strong>Recognize</strong> which mode fits <strong>common development scenarios</strong>                                       </li>\\n<li><strong>Weigh</strong> the <strong>risks and tradeoffs</strong> associated with each workflow</li>\\n</ul>\\n<p>The boundaries between these categories will keep blurring as tools evolve. Rather than chasing the “best” tool, focus on the interaction mode that matches your current task. Use IDE agents for interactive editing, terminal agents for complex multi-file work, PR agents for automated review, and cloud agents for well-scoped tasks that can run in the background and be reviewed later.</p>\\n<p>To continue building your skills with AI-assisted development, explore Real Python’s <a href=\\\"https://realpython.com/learning-paths/coding-with-ai/\\\">Python Coding With AI</a> learning path and the <a href=\\\"https://realpython.com/ref/ai-coding-tools/\\\">AI Coding Tools</a> reference page for a comprehensive look at the tools available today.</p>\\n\\n</section><section><h2 id=\\\"frequently-asked-questions\\\">Frequently Asked Questions<a href=\\\"#frequently-asked-questions\\\" title=\\\"Permanent link\\\"></a></h2>\\n<p>Now that you have some experience with AI coding agents in Python, you can use the questions and answers below to check your understanding and recap what you’ve learned.</p>\\n<p>These FAQs are related to the most important concepts you’ve covered in this tutorial. Click the <em>Show/Hide</em> toggle beside each question to reveal the answer.</p>\\n<div id=\\\"collapse_cardb0f4bc\\\" data-parent=\\\"#collapse_cardb0f4bc\\\">\\n  <p>An AI coding agent is a tool that reads your code, reasons about changes, and acts on your behalf through a continuous loop of reading, reasoning, acting, and evaluating. That loop is what separates an agent from autocomplete or a one-shot chatbot exchange.</p>\\n</div>\\n\\n<div id=\\\"collapse_card2a90ff\\\" data-parent=\\\"#collapse_card2a90ff\\\">\\n  <p>The four workflow types are IDE agents, terminal agents, pull request (PR) agents, and cloud agents. They differ by where the agent runs, how you interact with it, and how much autonomy it has.</p>\\n</div>\\n\\n<div id=\\\"collapse_cardb26b3f\\\" data-parent=\\\"#collapse_cardb26b3f\\\">\\n  <p>Chatbots respond with one-off answers, while coding agents operate through a continuous execution loop and can edit files, run commands, and use external tools. Agents keep working until the task is done or they hand control back to you.</p>\\n</div>\\n\\n<div id=\\\"collapse_card05b2d9\\\" data-parent=\\\"#collapse_card05b2d9\\\">\\n  <p>Some agents can connect to local models through tools like Ollama or Continue, which keeps your code on your machine. This option matters when company policies prevent sending proprietary code to external services.</p>\\n</div>\\n\\n<div id=\\\"collapse_cardaff2bd\\\" data-parent=\\\"#collapse_cardaff2bd\\\">\\n  <p>Yes, human review remains essential for all AI-generated code. The more autonomous the agent, the more important careful review becomes, so a good rule of thumb is to never push or ship code that you haven’t laid eyes on.</p>\\n</div>\\n\\n<div>\\n\\n  <p><mark><strong> Take the Quiz:</strong></mark> Test your knowledge with our interactive “AI Coding Agents Guide: A Map of the Four Workflow Types” quiz. You’ll receive a score upon completion to help you track your learning progress:</p>\\n\\n  <hr>\\n\\n  <div>\\n\\n      <a href=\\\"https://realpython.com/quizzes/ai-coding-agents-guide/\\\" tabindex=\\\"-1\\\">\\n        <div>\\n\\n            <p><img alt=\\\"A person in overalls pointing at a four-piece puzzle map labeled IDE, Cloud, CLI, and PR/Repo, with a map info legend beside it and a Python logo.\\\" src=\\\"https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg\\\" width=\\\"1920\\\" height=\\\"1080\\\" srcset=\\\"https://realpython.com/cdn-cgi/image/width=480,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 480w, https://realpython.com/cdn-cgi/image/width=640,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 640w, https://realpython.com/cdn-cgi/image/width=960,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 960w, https://realpython.com/cdn-cgi/image/width=1920,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 1920w\\\" sizes=\\\"(min-width: 1200px) 142px, (min-width: 1000px) 122px, (min-width: 780px) 112px, (min-width: 580px) 139px, calc(100vw - 62px)\\\"></p>\\n        </div>\\n      </a>\\n\\n    </div>\\n\\n</div>\\n\\n</section>\\n\\n    \\n      \\n      \\n    \\n\\n    \\n  </div></div>\"\n}"
    }
  ],
  "details": {
    "url": "https://realpython.com/ai-coding-agents-guide/",
    "title": "AI Coding Agents Guide: A Map of the Four Workflow Types",
    "author": "Ben Batman",
    "summary": "AI coding agents come in four types: IDE, terminal, PR, and cloud. Learn how each workflow fits into modern Python development.",
    "content": "<div id=\"readability-page-1\" class=\"page\"><p><span>by <a href=\"#author\">Ben Batman</a>\n    \n    \n      <span><svg role=\"img\"><use href=\"/static/icons.ea74988d8a7e.svg#@date\"></use></svg><span>Publication date</span></span> Apr 29, 2026\n    \n    \n      <span><svg role=\"img\"><use href=\"/static/icons.ea74988d8a7e.svg#regular--book-open\"></use></svg><span>Reading time estimate</span></span> 15m\n    \n    \n      <a href=\"#reader-comments\"></a>\n    \n    \n\n    <span> \n      \n      \n        \n<a href=\"https://realpython.com/tutorials/intermediate/\" data-previewable=\"\">intermediate</a>\n\n      \n        \n<a href=\"https://realpython.com/tutorials/ai/\" data-previewable=\"\">ai</a>\n\n      \n    </span>\n\n    \n  </span></p><div>\n    \n      \n    \n\n    \n    \n    \n\n    \n\n    <p>AI coding agents can read your code, reason about changes, and act on your behalf. To choose the right one, it helps to understand the four common workflow types: integrated development environment (IDE), terminal, pull request (PR), and cloud.</p>\n<p><strong>In this tutorial, you’ll</strong>:</p>\n<ul>\n<li><strong>Identify</strong> the four common <strong>agent interaction modes</strong></li>\n<li><strong>Understand</strong> what makes <strong>each workflow distinct</strong></li>\n<li><strong>Recognize</strong> which mode fits <strong>common development scenarios</strong></li>\n<li><strong>Weigh</strong> the <strong>risks and tradeoffs</strong> of each workflow</li>\n</ul>\n<p>Before exploring the four workflow types, it’s worth looking at what makes a coding tool <a href=\"https://realpython.com/ref/ai-coding-glossary/agentic-coding/\">agentic</a> in the first place.</p>\n<div>\n\n  <p><mark><strong> Take the Quiz:</strong></mark> Test your knowledge with our interactive “AI Coding Agents Guide: A Map of the Four Workflow Types” quiz. You’ll receive a score upon completion to help you track your learning progress:</p>\n\n  <hr>\n\n  <div>\n\n      <a href=\"https://realpython.com/quizzes/ai-coding-agents-guide/\" tabindex=\"-1\">\n        <div>\n\n            <p><img alt=\"A person in overalls pointing at a four-piece puzzle map labeled IDE, Cloud, CLI, and PR/Repo, with a map info legend beside it and a Python logo.\" src=\"https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg\" width=\"1920\" height=\"1080\" srcset=\"https://realpython.com/cdn-cgi/image/width=480,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 480w, https://realpython.com/cdn-cgi/image/width=640,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 640w, https://realpython.com/cdn-cgi/image/width=960,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 960w, https://realpython.com/cdn-cgi/image/width=1920,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 1920w\" sizes=\"(min-width: 1200px) 142px, (min-width: 1000px) 122px, (min-width: 780px) 112px, (min-width: 580px) 139px, calc(100vw - 62px)\"></p>\n        </div>\n      </a>\n\n    </div>\n\n</div>\n\n\n<section><h2 id=\"understanding-ai-coding-agents\">Understanding AI Coding Agents<a href=\"#understanding-ai-coding-agents\" title=\"Permanent link\"></a></h2>\n<p>While standard chatbots provide one-off answers, coding agents are designed for autonomy, operating through a continuous execution loop to solve complex tasks. This loop typically follows four distinct steps:</p>\n<ol>\n<li><strong>Read</strong>: They read relevant files from your codebase to form their context.</li>\n<li><strong>Reason</strong>: They determine the logical steps needed to achieve your goal.</li>\n<li><strong>Act</strong>: They execute those steps by editing files, running terminal commands, or using external tools.</li>\n<li><strong>Evaluate</strong>: They check the results of their actions to see if more work is needed.</li>\n</ol>\n<p>This loop repeats until the task is completed or the agent hands control back to you. Unlike simple predictive text or one-off prompts, agents bridge the gap between suggestion and execution by autonomously navigating the development workflow.</p>\n<p>The core agent loop will generally stay the same, but where an agent runs will shape how you interact with it:</p>\n<ul>\n<li><strong>In an editor</strong>, it works alongside you.</li>\n<li><strong>In a terminal</strong>, you guide it step by step.</li>\n<li><strong>In pull requests</strong>, it reviews changes asynchronously.</li>\n<li><strong>In the cloud</strong>, it works in a managed environment and reports back later.</li>\n</ul>\n<p>These environments define four primary agent types, each enabling a distinct workflow: <strong>IDE agents</strong>, <strong>terminal agents</strong>, <strong>PR agents</strong>, and <strong>cloud agents</strong>.</p>\n</section><section><h2 id=\"exploring-the-four-workflow-types\">Exploring the Four Workflow Types<a href=\"#exploring-the-four-workflow-types\" title=\"Permanent link\"></a></h2>\n<p>The four workflow types describe interaction modes and don’t always map cleanly to product categories. The same tool often spans multiple workflows. For example, <a href=\"https://realpython.com/ref/ai-coding-tools/claude-code/\">Claude Code</a> runs in your <a href=\"https://code.claude.com/docs/en/overview#terminal\">terminal</a>, in your <a href=\"https://code.claude.com/docs/en/overview#vs-code\">editor</a>, and in the cloud with <a href=\"https://code.claude.com/docs/en/claude-code-on-the-web\">Claude Code on the web</a>. It can also review pull requests with <a href=\"https://code.claude.com/docs/en/code-review\">Code Review</a>.</p>\n<p>The goal is to match the workflow to the task. The diagram below summarizes the four types at a glance:</p>\n<figure><a href=\"https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg\" target=\"_blank\"><img loading=\"lazy\" src=\"https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg\" width=\"2943\" height=\"1000\" srcset=\"https://realpython.com/cdn-cgi/image/width=735,format=auto/https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg 735w, https://realpython.com/cdn-cgi/image/width=981,format=auto/https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg 981w, https://realpython.com/cdn-cgi/image/width=1471,format=auto/https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg 1471w, https://realpython.com/cdn-cgi/image/width=2943,format=auto/https://files.realpython.com/media/Autonomous_Agent-2026-04-16-135819_2.0effa4a51d4b.jpeg 2943w\" sizes=\"(min-width: 1200px) 690px, (min-width: 780px) calc(-5vw + 669px), (min-width: 580px) 510px, calc(100vw - 30px)\" alt=\"AI Agent Workflow Type Table\" data-asset=\"6908\"></a><figcaption>The Four Coding Agent Workflows</figcaption></figure>\n\n<p>This chart gives you a quick reference for comparing the four types. The sections below dig into each one.</p>\n<section><h3 id=\"ide-agents\">IDE Agents<a href=\"#ide-agents\" title=\"Permanent link\"></a></h3>\n<p><a href=\"https://realpython.com/ref/glossary/ide/\">IDE</a> agents live inside your code editor and work alongside you in real time. They suggest edits inline, show visual diffs, and let you accept or reject changes without leaving your editing environment.</p>\n<p>This category has two common forms. <strong>AI-native IDEs</strong> such as <a href=\"https://realpython.com/courses/tips-using-ai-coding-editor-cursor/\">Cursor</a>, <a href=\"https://realpython.com/ref/ai-coding-tools/windsurf/\">Windsurf</a>, and <a href=\"https://realpython.com/ref/ai-coding-tools/kiro/\">Kiro</a> are built from the ground up around AI capabilities. Some AI-native IDEs, especially tools like Kiro, support a more <a href=\"https://kiro.dev/docs/specs/\">spec-driven workflow</a> where you describe the task upfront and let the agent work through it.</p>\n<p><strong>IDE integrations</strong> like the <a href=\"https://realpython.com/github-copilot-python/\">GitHub Copilot</a> extension, the <a href=\"https://code.claude.com/docs/en/vs-code\">Claude Code in VS Code</a> extension, and <a href=\"https://developers.google.com/gemini-code-assist/docs/overview\">Gemini Code Assist</a> add agent features to editors you already use. Compared to AI-native IDEs, IDE integrations usually fit a more file-targeted workflow centered on interactive editing and <a href=\"https://realpython.com/ref/best-practices/refactoring/\">refactoring</a>. However, the choice is developer-specific, so try a few and see which style suits your workflow.</p>\n<p>Keep in mind that cloud-backed IDE agents send your code to external servers for processing. Often, teams require approved tooling or local-only models for privacy reasons. Tools like <a href=\"https://docs.continue.dev/customize/models\">Continue</a> let you run models locally if your code can’t leave your machine.</p>\n</section><section><h3 id=\"terminal-agents\">Terminal Agents<a href=\"#terminal-agents\" title=\"Permanent link\"></a></h3>\n<p>Terminal agents run in your shell. You describe a task, and the agent reads files, proposes edits, and runs commands. You generally approve or reject each step before the agent moves on.</p>\n<p>The <a href=\"https://realpython.com/terminal-commands/\">terminal</a> workflow works well for complex changes and navigating large codebases. You can point the agent at your whole project and let it trace through imports and related files, and propose coordinated changes across many modules. Terminal agents are also helpful when you’re jumping into a new codebase and need to get up to speed quickly. This category includes tools such as <a href=\"https://realpython.com/courses/getting-started-claude-code/\">Claude Code</a>, <a href=\"https://realpython.com/ref/ai-coding-tools/aider/\">Aider</a>, <a href=\"https://realpython.com/how-to-use-gemini-cli/\">Gemini CLI</a>, <a href=\"https://realpython.com/ref/ai-coding-tools/opencode/\">OpenCode</a>, and <a href=\"https://realpython.com/codex-cli/\">Codex CLI</a>.</p>\n<p>For a direct comparison of two of the tools in this list, Real Python’s tutorial on <a href=\"https://realpython.com/gemini-cli-vs-claude-code/\">Gemini CLI vs Claude Code</a> walks through how each one handles common Python tasks.</p>\n\n<p>Because terminal agents operate in the shell, they integrate seamlessly with your existing development workflow. The most common way to interact with them is to launch them in your terminal and use their built-in interface. More advanced workflows include steps such as piping logs into them, chaining them with other <a href=\"https://realpython.com/ref/glossary/command-line-interface/\">CLI</a> tools, and running them inside automation scripts.</p>\n<p>When you run the agent interactively, the step-by-step approval model gives you high control while still letting the agent handle the heavy lifting. If you want longer sessions without continuous approval prompts, you can explore <a href=\"https://code.claude.com/docs/en/permission-modes#eliminate-prompts-with-auto-mode\">auto mode</a> in Claude Code.</p>\n<p>Some terminal agents can connect to local models through tools like <a href=\"https://realpython.com/ollama/\">Ollama</a>. If you’re unable to send code to external services due to company policies on proprietary code, a local model setup can be a good option to explore.</p>\n</section><section><h3 id=\"pull-request-agents\">Pull Request Agents<a href=\"#pull-request-agents\" title=\"Permanent link\"></a></h3>\n<p>Pull request (PR) agents are structurally different from the other three types. They’re <strong>asynchronous</strong>, meaning you don’t watch the agent work in real time. Instead, the agent often triggers automatically when a pull request is opened or updated. It runs on its own schedule, flags issues, suggests fixes, and leaves comments for you to review.</p>\n<p>This workflow generally operates on shared branches visible to everyone on the team, not on your local workspace. The verification process involves human code review. The agent flags potential bugs, style violations, and logic issues, but a human reviewer makes the final call on whether to merge. In other words, PR agents usually act as a safety net before merging rather than as a tool you steer live while coding.</p>\n<p>PR-agent workflows typically center on <a href=\"https://realpython.com/ref/best-practices/version-control/\">version control</a> platforms like GitHub, GitLab, and Bitbucket. Tools like <a href=\"https://docs.coderabbit.ai/\">CodeRabbit</a> and <a href=\"https://docs.github.com/en/copilot/concepts/agents/code-review\">GitHub Copilot code review</a> support this workflow. Even so, they don’t have to be triggered <em>only</em> from your version control platform. For example, GitHub Copilot code review can also be requested from places like <a href=\"https://docs.github.com/en/copilot/how-tos/use-copilot-agents/request-a-code-review/use-code-review?tool=vscode\">VS Code</a>, the <a href=\"https://docs.github.com/en/copilot/how-tos/copilot-cli/use-copilot-cli-agents/agentic-code-review\">GitHub Copilot CLI</a>, <a href=\"https://docs.github.com/en/copilot/how-tos/use-copilot-agents/request-a-code-review/use-code-review?tool=mobile\">a mobile device</a>, and more.</p>\n<p>In practice, the workflow looks like this: You open a pull request, and after some time, the agent posts a review with comments about your code. It might catch an unhandled edge case, flag a missing test, or suggest a cleaner approach. You respond to its comments just as you would to a teammate’s review by accepting the suggestion, pushing a fix, or dismissing it.</p>\n<p>Keep in mind that on many teams, repository-level AI tools are approved or blocked centrally, so privacy decisions often happen at the organizational level rather than the individual level.</p>\n</section><section><h3 id=\"cloud-agents\">Cloud Agents<a href=\"#cloud-agents\" title=\"Permanent link\"></a></h3>\n<p>Cloud agents generally offer the most autonomy. You describe a task, the agent works in a remote or managed environment, and later reports back with a branch, pull request, or prototype.</p>\n<p>This makes cloud agents a good fit for <a href=\"https://en.wikipedia.org/wiki/Greenfield_project\">greenfield prototyping</a> or work that takes longer than you’d want to sit and supervise. This category includes tools such as <a href=\"https://realpython.com/ref/ai-coding-tools/devin/\">Devin</a>, <a href=\"https://code.claude.com/docs/en/claude-code-on-the-web\">Claude Code on the web</a>, <a href=\"https://developers.openai.com/codex/cloud\">Codex web</a>, and <a href=\"https://realpython.com/ref/ai-coding-tools/cursor/\">Cursor’s</a> <a href=\"https://cursor.com/docs/cloud-agent\">Cloud Agents</a>.</p>\n<p>You can often access cloud agents through Slack, issue trackers, or a web browser. For example, you can mention Claude with <code>@Claude</code> in <a href=\"https://code.claude.com/docs/en/slack\">Slack</a> and ask it to complete a specific task for you, and it will spin up a Claude Code session on the web.</p>\n<p>There’s a tradeoff, though. You get more autonomy but often give up real-time control, because your code runs on infrastructure outside your local machine. That remote execution makes cloud agents most useful when the task is clearly scoped and the output is easy to review, such as a branch, pull request, or prototype.</p>\n<p>Not every cloud agent uses the same execution model. Claude Code on the web runs on Anthropic-managed cloud infrastructure. <a href=\"https://docs.github.com/en/copilot/concepts/agents/cloud-agent/about-cloud-agent\">GitHub’s Copilot cloud agent</a> runs in an ephemeral development environment powered by <a href=\"https://realpython.com/github-actions-python/\">GitHub Actions</a>. Cursor’s Cloud Agents can also run on machines you control through <a href=\"https://cursor.com/docs/cloud-agent/my-machines\">My Machines</a>. In short, where a cloud agent actually executes depends on the vendor, so factor that into your privacy compliance checks.</p>\n<p>As with all AI-generated code, human review remains essential. That matters even more with cloud agents because they operate with a high level of autonomy. Every team has its own guidelines on working with AI-generated code, but a good rule of thumb is to never push or ship code that you haven’t laid eyes on.</p>\n<p>Many cloud agents rely on vendor-managed infrastructure, and some organizations block them due to security or compliance requirements. Other cloud agents can run against machines you control. Either way, check your company’s policy before using them with proprietary code.</p>\n</section></section><section><h2 id=\"navigating-category-overlap\">Navigating Category Overlap<a href=\"#navigating-category-overlap\" title=\"Permanent link\"></a></h2>\n<p>Tool overlap is common. Three tools make that especially clear because each one shows up across all four workflow types.</p>\n<ul>\n<li>\n<p><a href=\"https://code.claude.com/docs/en/overview\"><strong>Claude Code</strong></a> spans all four workflows. In the <a href=\"https://code.claude.com/docs/en/terminal-guide\">terminal</a>, it works as a shell-based agent. In an IDE, it has native integrations for editors like <a href=\"https://code.claude.com/docs/en/vs-code\">VS Code</a> and <a href=\"https://code.claude.com/docs/en/jetbrains\">JetBrains</a>. In the cloud, it runs as Claude Code on the web. For PR workflows, Anthropic offers <a href=\"https://code.claude.com/docs/en/code-review\">Code Review</a> as a managed service and <a href=\"https://code.claude.com/docs/en/github-actions\">Claude Code GitHub Actions</a> for teams running their own CI pipelines.</p>\n</li>\n<li>\n<p><a href=\"https://realpython.com/courses/tips-using-ai-coding-editor-cursor/\"><strong>Cursor</strong></a> covers all four workflows, too. Its main editor experience is its <a href=\"https://cursor.com/docs/get-started/quickstart\">IDE</a>. <a href=\"https://cursor.com/docs/cli/overview\">Cursor CLI</a> handles terminal use, <a href=\"https://cursor.com/docs/cloud-agent\">Cloud Agents</a> manages cloud execution, and <a href=\"https://cursor.com/docs/bugbot\">Bugbot</a> automates pull request reviews.</p>\n</li>\n<li>\n<p><a href=\"https://docs.github.com/en/copilot/get-started/quickstart\"><strong>GitHub Copilot</strong></a> also spans all four workflows. You can run it in your <a href=\"https://realpython.com/github-copilot-python/\">IDE</a>, take it to the terminal with <a href=\"https://docs.github.com/en/copilot/how-tos/copilot-cli/cli-getting-started\">GitHub Copilot CLI</a>, request PR feedback with <a href=\"https://docs.github.com/en/copilot/how-tos/use-copilot-agents/request-a-code-review/use-code-review\">GitHub Copilot code review</a>, and hand off background work to a <a href=\"https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent\">GitHub Copilot cloud agent</a>.</p>\n</li>\n</ul>\n\n<p>The takeaway is that the taxonomy in this tutorial describes <strong>workflows</strong>, that is, how you’re working. The product might stay the same, but the interaction mode changes depending on where and how you use it. Most agentic coding tools now span more than one category, and that overlap will likely keep growing.</p>\n</section><section><h2 id=\"avoiding-common-pitfalls\">Avoiding Common Pitfalls<a href=\"#avoiding-common-pitfalls\" title=\"Permanent link\"></a></h2>\n<p>With agentic coding becoming increasingly powerful, it’s easier than ever to fall into common traps. To get the full benefit of coding agents without compromising quality, privacy, or control, watch out for these mistakes:</p>\n<ul>\n<li>\n<p><strong>Assuming one agent type handles everything</strong>: IDE agents excel at interactive editing, terminal agents handle complex multi-file changes, PR agents catch issues asynchronously, and cloud agents tackle brand-new features and prototyping. Matching the workflow to the task matters more than picking a single tool.</p>\n</li>\n<li>\n<p><strong>Ignoring privacy and compliance constraints</strong>: Many cloud agents run on remote infrastructure. PR agents operate in shared repositories. IDE and terminal agents with cloud backends send code to external <a href=\"https://realpython.com/ref/glossary/api/\">APIs</a> for inference. Before adopting any agent, check whether your code is allowed to leave your local machine and review your company’s policies on AI tool usage. Some teams can only use local models or self-managed environments.</p>\n</li>\n<li>\n<p><strong>Over-automating without review</strong>: Assume AI-generated code contains mistakes. It may have subtle bugs, weak exception handling, or patterns that don’t match your team’s conventions. Review all generated code carefully before merging. The more autonomous the agent, the more important developer oversight becomes. A careful review is much cheaper than a production failure.</p>\n</li>\n</ul>\n<p>Coding agents pay off when you treat them as collaborators, not replacements. Pick the right agent for each task, respect your team’s privacy boundaries, and keep a human reviewer in the loop.</p>\n</section><section><h2 id=\"conclusion\">Conclusion<a href=\"#conclusion\" title=\"Permanent link\"></a></h2>\n<p>Agentic coding is already reshaping how developers write, review, and ship code. Not all agents work the same way, though. The interaction mode matters as much as the tool itself.</p>\n<p><strong>In this tutorial, you’ve learned how to:</strong></p>\n<ul>\n<li><strong>Identify</strong> the <strong>four coding agent workflow types</strong>: IDE, terminal, PR, and cloud                    </li>\n<li><strong>Understand</strong> what makes <strong>each workflow distinct</strong>, from real-time inline editing to highly autonomous cloud tasks                                              </li>\n<li><strong>Recognize</strong> which mode fits <strong>common development scenarios</strong>                                       </li>\n<li><strong>Weigh</strong> the <strong>risks and tradeoffs</strong> associated with each workflow</li>\n</ul>\n<p>The boundaries between these categories will keep blurring as tools evolve. Rather than chasing the “best” tool, focus on the interaction mode that matches your current task. Use IDE agents for interactive editing, terminal agents for complex multi-file work, PR agents for automated review, and cloud agents for well-scoped tasks that can run in the background and be reviewed later.</p>\n<p>To continue building your skills with AI-assisted development, explore Real Python’s <a href=\"https://realpython.com/learning-paths/coding-with-ai/\">Python Coding With AI</a> learning path and the <a href=\"https://realpython.com/ref/ai-coding-tools/\">AI Coding Tools</a> reference page for a comprehensive look at the tools available today.</p>\n\n</section><section><h2 id=\"frequently-asked-questions\">Frequently Asked Questions<a href=\"#frequently-asked-questions\" title=\"Permanent link\"></a></h2>\n<p>Now that you have some experience with AI coding agents in Python, you can use the questions and answers below to check your understanding and recap what you’ve learned.</p>\n<p>These FAQs are related to the most important concepts you’ve covered in this tutorial. Click the <em>Show/Hide</em> toggle beside each question to reveal the answer.</p>\n<div id=\"collapse_cardb0f4bc\" data-parent=\"#collapse_cardb0f4bc\">\n  <p>An AI coding agent is a tool that reads your code, reasons about changes, and acts on your behalf through a continuous loop of reading, reasoning, acting, and evaluating. That loop is what separates an agent from autocomplete or a one-shot chatbot exchange.</p>\n</div>\n\n<div id=\"collapse_card2a90ff\" data-parent=\"#collapse_card2a90ff\">\n  <p>The four workflow types are IDE agents, terminal agents, pull request (PR) agents, and cloud agents. They differ by where the agent runs, how you interact with it, and how much autonomy it has.</p>\n</div>\n\n<div id=\"collapse_cardb26b3f\" data-parent=\"#collapse_cardb26b3f\">\n  <p>Chatbots respond with one-off answers, while coding agents operate through a continuous execution loop and can edit files, run commands, and use external tools. Agents keep working until the task is done or they hand control back to you.</p>\n</div>\n\n<div id=\"collapse_card05b2d9\" data-parent=\"#collapse_card05b2d9\">\n  <p>Some agents can connect to local models through tools like Ollama or Continue, which keeps your code on your machine. This option matters when company policies prevent sending proprietary code to external services.</p>\n</div>\n\n<div id=\"collapse_cardaff2bd\" data-parent=\"#collapse_cardaff2bd\">\n  <p>Yes, human review remains essential for all AI-generated code. The more autonomous the agent, the more important careful review becomes, so a good rule of thumb is to never push or ship code that you haven’t laid eyes on.</p>\n</div>\n\n<div>\n\n  <p><mark><strong> Take the Quiz:</strong></mark> Test your knowledge with our interactive “AI Coding Agents Guide: A Map of the Four Workflow Types” quiz. You’ll receive a score upon completion to help you track your learning progress:</p>\n\n  <hr>\n\n  <div>\n\n      <a href=\"https://realpython.com/quizzes/ai-coding-agents-guide/\" tabindex=\"-1\">\n        <div>\n\n            <p><img alt=\"A person in overalls pointing at a four-piece puzzle map labeled IDE, Cloud, CLI, and PR/Repo, with a map info legend beside it and a Python logo.\" src=\"https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg\" width=\"1920\" height=\"1080\" srcset=\"https://realpython.com/cdn-cgi/image/width=480,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 480w, https://realpython.com/cdn-cgi/image/width=640,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 640w, https://realpython.com/cdn-cgi/image/width=960,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 960w, https://realpython.com/cdn-cgi/image/width=1920,format=auto/https://files.realpython.com/media/A-Practical-Map-of-Types-of-AI-Coding-Agents_Watermarked.7955cfd5f864.jpg 1920w\" sizes=\"(min-width: 1200px) 142px, (min-width: 1000px) 122px, (min-width: 780px) 112px, (min-width: 580px) 139px, calc(100vw - 62px)\"></p>\n        </div>\n      </a>\n\n    </div>\n\n</div>\n\n</section>\n\n    \n      \n      \n    \n\n    \n  </div></div>"
  }
}

{
  "content": [
    {
      "type": "text",
      "text": "{\n  \"results\": [\n    {\n      \"title\": \"Best AI Agents in March 2026 | Blaxel Blog\",\n      \"url\": \"https://blaxel.ai/blog/best-ai-agents\",\n      \"description\": \"Spin up your first sandbox in under a second. Best AI agents in March 2026. 9 AI agents across coding, business, and IT ops compared. Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025. That trajectory means engineering teams choosing agents today are making infrastructure decisions that will define their stack for years. Each entry includes an honest assessment of what works, where limitations exist, and which teams get the most value. ## **What are AI agents?**. Claude Code runs on Anthropic's Opus and Sonnet models with large context windows, which helps it keep more of a repository in-scope during a session. OpenAI's coding agent spans multiple surfaces: a cloud-based agent in ChatGPT, a terminal CLI, an IDE extension for VS Code and forks, and a standalone desktop app. It shares infrastructure with Gemini Code Assist, so developers get the same models in both their terminal and VS Code.\"\n    },\n    {\n      \"title\": \"Best AI Coding Agents in 2026, Ranked - MightyBot\",\n      \"url\": \"https://mightybot.ai/blog/coding-ai-agents-for-accelerating-engineering-workflows/\",\n      \"description\": \"# Best AI Coding Agents in 2026, Ranked — MightyBot. _MightyBot applies this same agentic architecture beyond coding —[learn how policy-driven AI agents](https://mightybot.ai/blog/what-is-policy-driven-ai/) automate mission-critical workflows in financial services with [99%+ accuracy](https://mightybot.ai/blog/shipping-ai-that-works-built-x-mightybot-draw-agent/)._. Companies like [MightyBot](https://mightybot.ai/blog/mightybot-2025-the-year-agentic-ai-became-real/) are applying this same agentic approach beyond coding — using [policy-driven AI agents](https://mightybot.ai/blog/what-is-policy-driven-ai/) to automate mission-critical workflows in [financial services with 99%+ accuracy](https://mightybot.ai/blog/shipping-ai-that-works-built-x-mightybot-draw-agent/). [Economics**AI Agent ROI & TCO Calculator**Model engineering headcount, token spend, timeline delay, maintenance, and 3-year TCO.](https://mightybot.ai/tools/ai-agent-roi-calculator/)[Decision guide**Build vs Buy AI Agent Platform**Compare internal build risk, token efficiency, governance, and production deployment cost.](https://mightybot.ai/why-mightybot/build-vs-buy/)[Platform**AI Agent Platform**The full Decision Execution Stack for regulated workflows.](https://mightybot.ai/platform/)[Runtime**Compiled Agent Execution**Parallel execution plans for regulated workflows instead of sequential prompt chains.](https://mightybot.ai/platform/agent-execution/)[Compare**AI Agent Platform Comparisons**See how MightyBot compares with Palantir AIP, OpenAI, Google, UiPath, Workato, and more.](https://mightybot.ai/compare/)[Category guide**Best AI Agent Platforms for Regulated Industries**Evaluate AI agent platforms for policy-driven regulated workflows, compliance, and audit trails.](https://mightybot.ai/compare/best-ai-agent-platforms-regulated-industries/). The top AI coding agents in 2026 are OpenAI Codex with GPT-5.5 for the best overall coding-agent workflow, Claude Code with Opus 4.7 for Claude-native terminal development, OpenCode for provider-agnostic open-source flexibility, Gemini CLI for free access to frontier models with 1M token context, and Cursor for the best AI-native IDE experience.\"\n    },\n    {\n      \"title\": \"Best AI Coding Agents in 2026: Ranked and Compared - Codegen\",\n      \"url\": \"https://codegen.com/blog/best-ai-coding-agents/\",\n      \"description\": \"# Best AI Coding Agents in 2026: Ranked and Compared - The Codegen Blog. [Skip to main content](https://codegen.com/blog/best-ai-coding-agents/#main-content)[Skip to content](https://codegen.com/blog/best-ai-coding-agents/#content). [](https://codegen.com/blog/best-ai-coding-agents/#ekit_modal-popup-39e71d8). [](https://codegen.com/blog/best-ai-coding-agents/#ekit_modal-popup-e0dabe0). [Codegen Blog](https://codegen.com/blog/)/[Developer Tools & Workflows](https://codegen.com/blog/category/dev-tools-and-workflows/)/Best AI Coding Agents in 2026: Ranked and Compared. | Cursor | Individual developers and teams wanting IDE-first AI | Code authoring and editing | High (parallel agents) | Yes | $16/month |. The [AI code review](https://docs.codegen.com/capabilities/pr-review) agent delivers line-by-line PR feedback that maintains quality standards across both human and AI contributions. **Best for:** Teams new to AI coding tools, developers whose work centers on inline editing, and organizations already deep in the GitHub Enterprise ecosystem. If you are ready to move beyond individual tool evaluation and start running agents in a governed, production-ready environment, [get started free](https://clickup.com/brain/agents/codegen) at codegen.com, or [request a demo](https://clickup.com/brain/agents/codegen) to see the full orchestration layer in action. 1.   [How to Pick the Right Agent for Your Team](https://codegen.com/blog/best-ai-coding-agents/#h-how-to-pick-the-right-agent-for-your-team). 2.   [How We Evaluated These Tools](https://codegen.com/blog/best-ai-coding-agents/#h-how-we-evaluated-these-tools). 3.   [The Best AI Coding Agents, Ranked](https://codegen.com/blog/best-ai-coding-agents/#h-the-best-ai-coding-agents-ranked). 4.   [Frequently Asked Questions](https://codegen.com/blog/best-ai-coding-agents/#h-frequently-asked-questions).\"\n    },\n    {\n      \"title\": \"AI Code Tools: Complete Guide for Developers in 2026 - CodeSubmit\",\n      \"url\": \"https://www.codesubmit.io/blog/ai-code-tools/\",\n      \"description\": \"All categories 105 Code Assistant 10 Development Tool 10 Open-weight Model 8 Video Model 8 Hugging Face Model 7 Image Model 7 AI Model 6 AI Agent 5 Big Model 5 IDE 5 AI Platform 4 Code Generator 4 Text-to-Speech 4 Development Platform 3 IDE Extension 3 Code Analysis 2 Code Review 2 Humanizer 2 Terminal 2 Copywriter 1 Development Suite 1 Image Upscaler 1 Inference Server 1 Music Model 1 OCR 1 Speech-to-Text 1 Utility 1. ![Image 8: ChatGPT and OpenAI GPT-5.5 icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fopenai.c84d9c67.png&w=48&q=75). ![Image 9: Claude Opus 4.7 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fclaude.f9d91d4d.png&w=48&q=75). ![Image 10: Gemini CLI icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fbard.2ad9bdd6.png&w=48&q=75). ![Image 11: CodeRabbit icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcoderabbit.1b3772d9.png&w=48&q=75). ![Image 12: Cursor icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcursor.0e17228c.png&w=48&q=75). ![Image 14: Playwright icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fplaywright.03da4a20.png&w=48&q=75). ![Image 15: Serena icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fserena.68fe00d8.png&w=48&q=75). ![Image 16: AlphaCode icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Falphacode.4aaebba0.png&w=48&q=75). ![Image 17: Amazon Q Developer icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Faws-bedrock.3087bd57.png&w=48&q=75). ![Image 19: Bolt icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fbolt.c0542d98.png&w=48&q=75). ![Image 20: ChatGPT and OpenAI GPT-5.5 icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fopenai.c84d9c67.png&w=48&q=75). ![Image 21: Claude Opus 4.7 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fclaude.f9d91d4d.png&w=48&q=75). ![Image 22: Claude Opus 4.7 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fclaude.f9d91d4d.png&w=48&q=75). ![Image 23: Claude Opus 4.7 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fclaude.f9d91d4d.png&w=48&q=75). ![Image 24: Cline icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcline.dca1aa6c.png&w=48&q=75). ![Image 25: Code Llama icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcode-llama.047c0463.png&w=48&q=75). ![Image 26: Codestral (Mistral) icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fmistral.bb2ccb79.png&w=48&q=75). ![Image 27: Continue icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcontinue.73880dd5.png&w=48&q=75). ![Image 28: DeepSeek R1 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fdeepseek.308a3f47.png&w=48&q=75). ![Image 30: DeepSeek R1 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fdeepseek.308a3f47.png&w=48&q=75). ![Image 32: Devin icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fdevin.ca317ef3.png&w=48&q=75). ![Image 33: Factory icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ffactory.efead20a.png&w=48&q=75).\"\n    },\n    {\n      \"title\": \"Coding Agents Comparison: Cursor, Claude Code, GitHub Copilot, and more\",\n      \"url\": \"https://artificialanalysis.ai/agents/coding\",\n      \"description\": \"| [GitHub Copilot Coding Agent GitHub](https://github.com/features/copilot) | IDE ExtensionCloud | FreeSubscription [$0-39/mo](https://github.com/features/copilot/plans) | May 2025 | Coding assistant in IDE and GitHub. Provides completions and chat over your repo; turns issues into PR-ready changes with summaries and reviewable diffs. | [Windsurf Cognition AI](https://windsurf.com/) | Standalone IDE | FreeSubscription [$0-200/mo](https://windsurf.com/pricing) | Nov 2024 | Desktop coding IDE with an agent workflow for multi-file changes. | [Codex OpenAI](https://openai.com/codex/) | IDE ExtensionLocal/CLICloud | FreeSubscriptionEnterpriseUsage-based [$0–200/mo](https://openai.com/chatgpt/pricing/) | Apr 2025 | Agentic coding tool accessible via VS Code extension, CLI, or cloud. | [Amp Sourcegraph](https://ampcode.com/) | IDE ExtensionLocal/CLI | FreeSubscriptionUsage-based [Free + API costs](https://ampcode.com/) | May 2025 | Agent-first coding tool for turning a task into concrete repo changes. The table organizes agents into four categories: IDE extensions that add AI capabilities to existing editors (e.g. GitHub Copilot, Cline, Continue), dedicated IDEs built around AI (e.g. Cursor, Windsurf, Zed), CLI tools for terminal-based workflows (e.g. Claude Code, Aider, Gemini CLI), and cloud platforms that run tasks asynchronously in remote environments (e.g. Devin, OpenHands, Jules).\"\n    },\n    {\n      \"title\": \"Best AI Coding Tools 2026: Complete Ranking by Real ... - NxCode\",\n      \"url\": \"https://www.nxcode.io/resources/news/best-ai-for-coding-2026-complete-ranking\",\n      \"description\": \"![Image 2: Best AI Coding Tools 2026: Complete Ranking by Real-World Performance](https://www.nxcode.io/images/news/best-ai-for-coding-2026.svg). See our roundup of the [best free AI coding tools](https://www.nxcode.io/resources/news/best-free-ai-coding-tools-2026) for more options. For a broader look at AI tools beyond coding, see our [best AI tools 2026 ranking](https://www.nxcode.io/resources/news/best-ai-tools-2026-complete-ranking-guide). *   [OpenAI Codex vs Cursor vs Claude Code: Which AI Coding Tool Should You Use in 2026?](https://www.nxcode.io/resources/news/openai-codex-vs-cursor-vs-claude-code-ai-coding-tools-2026). *   [Best OpenCode Alternatives in 2026: 8 Superior AI Coding Tools Reviewed](https://www.nxcode.io/resources/news/opencode-alternative-2026). 2026-03-29 Read more →](https://www.nxcode.io/resources/news/best-ai-tools-2026-complete-ranking-guide)[![Image 4: What Is Vibe Coding? 2026-03-29 Read more →](https://www.nxcode.io/resources/news/what-is-vibe-coding-complete-guide-ai-development-2026)[![Image 5: Agentic Engineering: The Complete Guide to AI-First Software Development Beyond Vibe Coding (2026)](https://www.nxcode.io/_next/image?url=%2Fassets%2Fblog%2Fai-trends%2Fagentic-engineering.webp&w=3840&q=75) ### Agentic Engineering: The Complete Guide to AI-First Software Development Beyond Vibe Coding (2026) Agentic engineering is the 2026 evolution beyond vibe coding — where engineers orchestrate AI agents that plan, write, test, and ship code under structured human oversight. 2026-03-03 Read more →](https://www.nxcode.io/resources/news/agentic-engineering-complete-guide-vibe-coding-ai-agents-2026)[![Image 6: Building Apps with Gemini 3.1 Pro: Developer Guide to API, Coding & Vibe Coding (2026)](https://www.nxcode.io/images/news/gemini-3-1-pro-developer-guide.svg) ### Building Apps with Gemini 3.1 Pro: Developer Guide to API, Coding & Vibe Coding (2026) Developer guide to building apps with Gemini 3.1 Pro API. 2026-02-19 Read more →](https://www.nxcode.io/resources/news/gemini-3-1-pro-developer-guide-api-coding-vibe-coding-2026).\"\n    },\n    {\n      \"title\": \"Best AI Code Editors in 2026: Cursor, Windsurf, Copilot, and More\",\n      \"url\": \"https://www.mindstudio.ai/blog/best-ai-code-editors/\",\n      \"description\": \"# Best AI Code Editors in 2026: Cursor, Windsurf, Copilot, and More. A practical comparison of the best AI code editors in 2025, covering context handling, autocomplete quality, pricing, and developer experience. Two years ago, the AI code editor landscape was simple: you used GitHub Copilot or you didn’t. Today there are half a dozen serious contenders, each with a different philosophy about what AI-assisted coding should look like. The choices matter more than they used to because the tools have diverged significantly — in approach, in capability, and in cost. This guide covers the best **AI code editors** in 2025 — Cursor, Windsurf, GitHub Copilot, Claude Code, and a few others — with a practical focus on what actually differentiates them. Cursor is a VS Code fork that rebuilds the editor around AI from the ground up. It’s not a plugin — it’s a whole editor, which means it has full control over how AI integrates with the UI, the codebase index, and the editing workflow.\"\n    },\n    {\n      \"title\": \"8 Best AI Coding Assistants [Updated April 2026]\",\n      \"url\": \"https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases\",\n      \"description\": \"[Skip to content](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#main-content). ![Image 3: 8 Best AI Coding Assistants [Updated April 2026]](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2F65912c7bcc79cb4033ce883a7640531e8c40539b-1024x1024.png&w=2160&q=75). Augment Code launched Intent for [multi-agent orchestration](https://www.augmentcode.com/guides/multi-agent-ai-system-code-development) with living specs. **What changed in 2026:** Augment Code launched [Intent](https://www.augmentcode.com/product/intent), a standalone macOS workspace for multi-agent orchestration. ![Image 6: Cursor homepage with tagline \\\"Built to make you extraordinarily productive, Cursor is the best way to code with AI.\\\"](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2Fcbf67d49e64cfd90b974fa2706eb6c02955b395e-1920x1080.png%3Fw%3D1600%26fm%3Dwebp%26q%3D90%26dpr%3D2&w=2160&q=75). ![Image 7: GitHub Copilot homepage showing \\\"Command your craft\\\" with VS Code interface demo and chat panel creating test files](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2Fbb8f71f3337bc186e029a1837a61465c5bc61e16-1547x995.png%3Fw%3D1600%26fm%3Dwebp%26q%3D90%26dpr%3D2&w=2160&q=75). ![Image 8: Amazon Q Developer product page showing AI assistant for software development with chat interface demo](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2F02abc47dfdce6ec0f963dfd763f87cbf9c6175f0-1559x778.png%3Fw%3D1600%26fm%3Dwebp%26q%3D90%26dpr%3D2&w=2160&q=75). ![Image 10: Tabnine homepage showing \\\"An AI Coding Platform for Enterprises That Can't Afford Mistakes\\\" with demo video and enterprise customer logos](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2Fe63052c5d5f7e2d8fb0f58b62cbd5614fca50778-1397x905.png%3Fw%3D1600%26fm%3Dwebp%26q%3D90%26dpr%3D2&w=2160&q=75). *   [Augment Code vs JetBrains AI](https://www.augmentcode.com/tools/augment-code-vs-jetbrains-ai-which-understands-your-codebase). *   [Continue vs Aider vs Cline](https://www.augmentcode.com/tools/continue-vs-aider-vs-cline-private-ai-coding-assistants-for-regulated-teams). [01 TL;DR](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#tldr)[02 What Changed in the AI Coding Landscape Since 2025](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#what-changed-in-the-ai-coding-landscape-since-2025)[03 How I Tested: Real Codebases, Not Clean Demos](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#how-i-tested-real-codebases-not-clean-demos)[04 Scored Rankings: 8 AI Coding Assistants Tested](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#scored-rankings-8-ai-coding-assistants-tested)[05 1. Augment Code](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#1-augment-code)Show 14 more.\"\n    },\n    {\n      \"title\": \"Terminal vs IDE: Why CLI is better for programming with AI - YouTube\",\n      \"url\": \"https://www.youtube.com/watch?v=69UonSdioCI\",\n      \"description\": \"Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=YFjfBk8HI5o Thank you for listening ❤ Check out our sponsors:\"\n    },\n    {\n      \"title\": \"A map of AI coding tools: IDEs, agents, all-in-on app builders and ...\",\n      \"url\": \"https://annaarteeva.medium.com/a-map-of-ai-coding-tools-ides-agents-all-in-on-app-builders-and-foundation-models-60a9b5f2ed80\",\n      \"description\": \"A practical guide to understanding the AI coding ecosystem and picking the right tool for different use cases and workflows.\"\n    }\n  ]\n}"
    }
  ],
  "details": {
    "results": [
      {
        "title": "Best AI Agents in March 2026 | Blaxel Blog",
        "url": "https://blaxel.ai/blog/best-ai-agents",
        "description": "Spin up your first sandbox in under a second. Best AI agents in March 2026. 9 AI agents across coding, business, and IT ops compared. Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025. That trajectory means engineering teams choosing agents today are making infrastructure decisions that will define their stack for years. Each entry includes an honest assessment of what works, where limitations exist, and which teams get the most value. ## **What are AI agents?**. Claude Code runs on Anthropic's Opus and Sonnet models with large context windows, which helps it keep more of a repository in-scope during a session. OpenAI's coding agent spans multiple surfaces: a cloud-based agent in ChatGPT, a terminal CLI, an IDE extension for VS Code and forks, and a standalone desktop app. It shares infrastructure with Gemini Code Assist, so developers get the same models in both their terminal and VS Code."
      },
      {
        "title": "Best AI Coding Agents in 2026, Ranked - MightyBot",
        "url": "https://mightybot.ai/blog/coding-ai-agents-for-accelerating-engineering-workflows/",
        "description": "# Best AI Coding Agents in 2026, Ranked — MightyBot. _MightyBot applies this same agentic architecture beyond coding —[learn how policy-driven AI agents](https://mightybot.ai/blog/what-is-policy-driven-ai/) automate mission-critical workflows in financial services with [99%+ accuracy](https://mightybot.ai/blog/shipping-ai-that-works-built-x-mightybot-draw-agent/)._. Companies like [MightyBot](https://mightybot.ai/blog/mightybot-2025-the-year-agentic-ai-became-real/) are applying this same agentic approach beyond coding — using [policy-driven AI agents](https://mightybot.ai/blog/what-is-policy-driven-ai/) to automate mission-critical workflows in [financial services with 99%+ accuracy](https://mightybot.ai/blog/shipping-ai-that-works-built-x-mightybot-draw-agent/). [Economics**AI Agent ROI & TCO Calculator**Model engineering headcount, token spend, timeline delay, maintenance, and 3-year TCO.](https://mightybot.ai/tools/ai-agent-roi-calculator/)[Decision guide**Build vs Buy AI Agent Platform**Compare internal build risk, token efficiency, governance, and production deployment cost.](https://mightybot.ai/why-mightybot/build-vs-buy/)[Platform**AI Agent Platform**The full Decision Execution Stack for regulated workflows.](https://mightybot.ai/platform/)[Runtime**Compiled Agent Execution**Parallel execution plans for regulated workflows instead of sequential prompt chains.](https://mightybot.ai/platform/agent-execution/)[Compare**AI Agent Platform Comparisons**See how MightyBot compares with Palantir AIP, OpenAI, Google, UiPath, Workato, and more.](https://mightybot.ai/compare/)[Category guide**Best AI Agent Platforms for Regulated Industries**Evaluate AI agent platforms for policy-driven regulated workflows, compliance, and audit trails.](https://mightybot.ai/compare/best-ai-agent-platforms-regulated-industries/). The top AI coding agents in 2026 are OpenAI Codex with GPT-5.5 for the best overall coding-agent workflow, Claude Code with Opus 4.7 for Claude-native terminal development, OpenCode for provider-agnostic open-source flexibility, Gemini CLI for free access to frontier models with 1M token context, and Cursor for the best AI-native IDE experience."
      },
      {
        "title": "Best AI Coding Agents in 2026: Ranked and Compared - Codegen",
        "url": "https://codegen.com/blog/best-ai-coding-agents/",
        "description": "# Best AI Coding Agents in 2026: Ranked and Compared - The Codegen Blog. [Skip to main content](https://codegen.com/blog/best-ai-coding-agents/#main-content)[Skip to content](https://codegen.com/blog/best-ai-coding-agents/#content). [](https://codegen.com/blog/best-ai-coding-agents/#ekit_modal-popup-39e71d8). [](https://codegen.com/blog/best-ai-coding-agents/#ekit_modal-popup-e0dabe0). [Codegen Blog](https://codegen.com/blog/)/[Developer Tools & Workflows](https://codegen.com/blog/category/dev-tools-and-workflows/)/Best AI Coding Agents in 2026: Ranked and Compared. | Cursor | Individual developers and teams wanting IDE-first AI | Code authoring and editing | High (parallel agents) | Yes | $16/month |. The [AI code review](https://docs.codegen.com/capabilities/pr-review) agent delivers line-by-line PR feedback that maintains quality standards across both human and AI contributions. **Best for:** Teams new to AI coding tools, developers whose work centers on inline editing, and organizations already deep in the GitHub Enterprise ecosystem. If you are ready to move beyond individual tool evaluation and start running agents in a governed, production-ready environment, [get started free](https://clickup.com/brain/agents/codegen) at codegen.com, or [request a demo](https://clickup.com/brain/agents/codegen) to see the full orchestration layer in action. 1.   [How to Pick the Right Agent for Your Team](https://codegen.com/blog/best-ai-coding-agents/#h-how-to-pick-the-right-agent-for-your-team). 2.   [How We Evaluated These Tools](https://codegen.com/blog/best-ai-coding-agents/#h-how-we-evaluated-these-tools). 3.   [The Best AI Coding Agents, Ranked](https://codegen.com/blog/best-ai-coding-agents/#h-the-best-ai-coding-agents-ranked). 4.   [Frequently Asked Questions](https://codegen.com/blog/best-ai-coding-agents/#h-frequently-asked-questions)."
      },
      {
        "title": "AI Code Tools: Complete Guide for Developers in 2026 - CodeSubmit",
        "url": "https://www.codesubmit.io/blog/ai-code-tools/",
        "description": "All categories 105 Code Assistant 10 Development Tool 10 Open-weight Model 8 Video Model 8 Hugging Face Model 7 Image Model 7 AI Model 6 AI Agent 5 Big Model 5 IDE 5 AI Platform 4 Code Generator 4 Text-to-Speech 4 Development Platform 3 IDE Extension 3 Code Analysis 2 Code Review 2 Humanizer 2 Terminal 2 Copywriter 1 Development Suite 1 Image Upscaler 1 Inference Server 1 Music Model 1 OCR 1 Speech-to-Text 1 Utility 1. ![Image 8: ChatGPT and OpenAI GPT-5.5 icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fopenai.c84d9c67.png&w=48&q=75). ![Image 9: Claude Opus 4.7 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fclaude.f9d91d4d.png&w=48&q=75). ![Image 10: Gemini CLI icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fbard.2ad9bdd6.png&w=48&q=75). ![Image 11: CodeRabbit icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcoderabbit.1b3772d9.png&w=48&q=75). ![Image 12: Cursor icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcursor.0e17228c.png&w=48&q=75). ![Image 14: Playwright icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fplaywright.03da4a20.png&w=48&q=75). ![Image 15: Serena icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fserena.68fe00d8.png&w=48&q=75). ![Image 16: AlphaCode icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Falphacode.4aaebba0.png&w=48&q=75). ![Image 17: Amazon Q Developer icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Faws-bedrock.3087bd57.png&w=48&q=75). ![Image 19: Bolt icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fbolt.c0542d98.png&w=48&q=75). ![Image 20: ChatGPT and OpenAI GPT-5.5 icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fopenai.c84d9c67.png&w=48&q=75). ![Image 21: Claude Opus 4.7 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fclaude.f9d91d4d.png&w=48&q=75). ![Image 22: Claude Opus 4.7 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fclaude.f9d91d4d.png&w=48&q=75). ![Image 23: Claude Opus 4.7 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fclaude.f9d91d4d.png&w=48&q=75). ![Image 24: Cline icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcline.dca1aa6c.png&w=48&q=75). ![Image 25: Code Llama icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcode-llama.047c0463.png&w=48&q=75). ![Image 26: Codestral (Mistral) icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fmistral.bb2ccb79.png&w=48&q=75). ![Image 27: Continue icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fcontinue.73880dd5.png&w=48&q=75). ![Image 28: DeepSeek R1 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fdeepseek.308a3f47.png&w=48&q=75). ![Image 30: DeepSeek R1 on Replicate icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fdeepseek.308a3f47.png&w=48&q=75). ![Image 32: Devin icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Fdevin.ca317ef3.png&w=48&q=75). ![Image 33: Factory icon](https://www.codesubmit.io/_next/image?url=%2F_next%2Fstatic%2Fmedia%2Ffactory.efead20a.png&w=48&q=75)."
      },
      {
        "title": "Coding Agents Comparison: Cursor, Claude Code, GitHub Copilot, and more",
        "url": "https://artificialanalysis.ai/agents/coding",
        "description": "| [GitHub Copilot Coding Agent GitHub](https://github.com/features/copilot) | IDE ExtensionCloud | FreeSubscription [$0-39/mo](https://github.com/features/copilot/plans) | May 2025 | Coding assistant in IDE and GitHub. Provides completions and chat over your repo; turns issues into PR-ready changes with summaries and reviewable diffs. | [Windsurf Cognition AI](https://windsurf.com/) | Standalone IDE | FreeSubscription [$0-200/mo](https://windsurf.com/pricing) | Nov 2024 | Desktop coding IDE with an agent workflow for multi-file changes. | [Codex OpenAI](https://openai.com/codex/) | IDE ExtensionLocal/CLICloud | FreeSubscriptionEnterpriseUsage-based [$0–200/mo](https://openai.com/chatgpt/pricing/) | Apr 2025 | Agentic coding tool accessible via VS Code extension, CLI, or cloud. | [Amp Sourcegraph](https://ampcode.com/) | IDE ExtensionLocal/CLI | FreeSubscriptionUsage-based [Free + API costs](https://ampcode.com/) | May 2025 | Agent-first coding tool for turning a task into concrete repo changes. The table organizes agents into four categories: IDE extensions that add AI capabilities to existing editors (e.g. GitHub Copilot, Cline, Continue), dedicated IDEs built around AI (e.g. Cursor, Windsurf, Zed), CLI tools for terminal-based workflows (e.g. Claude Code, Aider, Gemini CLI), and cloud platforms that run tasks asynchronously in remote environments (e.g. Devin, OpenHands, Jules)."
      },
      {
        "title": "Best AI Coding Tools 2026: Complete Ranking by Real ... - NxCode",
        "url": "https://www.nxcode.io/resources/news/best-ai-for-coding-2026-complete-ranking",
        "description": "![Image 2: Best AI Coding Tools 2026: Complete Ranking by Real-World Performance](https://www.nxcode.io/images/news/best-ai-for-coding-2026.svg). See our roundup of the [best free AI coding tools](https://www.nxcode.io/resources/news/best-free-ai-coding-tools-2026) for more options. For a broader look at AI tools beyond coding, see our [best AI tools 2026 ranking](https://www.nxcode.io/resources/news/best-ai-tools-2026-complete-ranking-guide). *   [OpenAI Codex vs Cursor vs Claude Code: Which AI Coding Tool Should You Use in 2026?](https://www.nxcode.io/resources/news/openai-codex-vs-cursor-vs-claude-code-ai-coding-tools-2026). *   [Best OpenCode Alternatives in 2026: 8 Superior AI Coding Tools Reviewed](https://www.nxcode.io/resources/news/opencode-alternative-2026). 2026-03-29 Read more →](https://www.nxcode.io/resources/news/best-ai-tools-2026-complete-ranking-guide)[![Image 4: What Is Vibe Coding? 2026-03-29 Read more →](https://www.nxcode.io/resources/news/what-is-vibe-coding-complete-guide-ai-development-2026)[![Image 5: Agentic Engineering: The Complete Guide to AI-First Software Development Beyond Vibe Coding (2026)](https://www.nxcode.io/_next/image?url=%2Fassets%2Fblog%2Fai-trends%2Fagentic-engineering.webp&w=3840&q=75) ### Agentic Engineering: The Complete Guide to AI-First Software Development Beyond Vibe Coding (2026) Agentic engineering is the 2026 evolution beyond vibe coding — where engineers orchestrate AI agents that plan, write, test, and ship code under structured human oversight. 2026-03-03 Read more →](https://www.nxcode.io/resources/news/agentic-engineering-complete-guide-vibe-coding-ai-agents-2026)[![Image 6: Building Apps with Gemini 3.1 Pro: Developer Guide to API, Coding & Vibe Coding (2026)](https://www.nxcode.io/images/news/gemini-3-1-pro-developer-guide.svg) ### Building Apps with Gemini 3.1 Pro: Developer Guide to API, Coding & Vibe Coding (2026) Developer guide to building apps with Gemini 3.1 Pro API. 2026-02-19 Read more →](https://www.nxcode.io/resources/news/gemini-3-1-pro-developer-guide-api-coding-vibe-coding-2026)."
      },
      {
        "title": "Best AI Code Editors in 2026: Cursor, Windsurf, Copilot, and More",
        "url": "https://www.mindstudio.ai/blog/best-ai-code-editors/",
        "description": "# Best AI Code Editors in 2026: Cursor, Windsurf, Copilot, and More. A practical comparison of the best AI code editors in 2025, covering context handling, autocomplete quality, pricing, and developer experience. Two years ago, the AI code editor landscape was simple: you used GitHub Copilot or you didn’t. Today there are half a dozen serious contenders, each with a different philosophy about what AI-assisted coding should look like. The choices matter more than they used to because the tools have diverged significantly — in approach, in capability, and in cost. This guide covers the best **AI code editors** in 2025 — Cursor, Windsurf, GitHub Copilot, Claude Code, and a few others — with a practical focus on what actually differentiates them. Cursor is a VS Code fork that rebuilds the editor around AI from the ground up. It’s not a plugin — it’s a whole editor, which means it has full control over how AI integrates with the UI, the codebase index, and the editing workflow."
      },
      {
        "title": "8 Best AI Coding Assistants [Updated April 2026]",
        "url": "https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases",
        "description": "[Skip to content](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#main-content). ![Image 3: 8 Best AI Coding Assistants [Updated April 2026]](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2F65912c7bcc79cb4033ce883a7640531e8c40539b-1024x1024.png&w=2160&q=75). Augment Code launched Intent for [multi-agent orchestration](https://www.augmentcode.com/guides/multi-agent-ai-system-code-development) with living specs. **What changed in 2026:** Augment Code launched [Intent](https://www.augmentcode.com/product/intent), a standalone macOS workspace for multi-agent orchestration. ![Image 6: Cursor homepage with tagline \"Built to make you extraordinarily productive, Cursor is the best way to code with AI.\"](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2Fcbf67d49e64cfd90b974fa2706eb6c02955b395e-1920x1080.png%3Fw%3D1600%26fm%3Dwebp%26q%3D90%26dpr%3D2&w=2160&q=75). ![Image 7: GitHub Copilot homepage showing \"Command your craft\" with VS Code interface demo and chat panel creating test files](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2Fbb8f71f3337bc186e029a1837a61465c5bc61e16-1547x995.png%3Fw%3D1600%26fm%3Dwebp%26q%3D90%26dpr%3D2&w=2160&q=75). ![Image 8: Amazon Q Developer product page showing AI assistant for software development with chat interface demo](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2F02abc47dfdce6ec0f963dfd763f87cbf9c6175f0-1559x778.png%3Fw%3D1600%26fm%3Dwebp%26q%3D90%26dpr%3D2&w=2160&q=75). ![Image 10: Tabnine homepage showing \"An AI Coding Platform for Enterprises That Can't Afford Mistakes\" with demo video and enterprise customer logos](https://www.augmentcode.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Foraw2u2c%2Fproduction%2Fe63052c5d5f7e2d8fb0f58b62cbd5614fca50778-1397x905.png%3Fw%3D1600%26fm%3Dwebp%26q%3D90%26dpr%3D2&w=2160&q=75). *   [Augment Code vs JetBrains AI](https://www.augmentcode.com/tools/augment-code-vs-jetbrains-ai-which-understands-your-codebase). *   [Continue vs Aider vs Cline](https://www.augmentcode.com/tools/continue-vs-aider-vs-cline-private-ai-coding-assistants-for-regulated-teams). [01 TL;DR](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#tldr)[02 What Changed in the AI Coding Landscape Since 2025](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#what-changed-in-the-ai-coding-landscape-since-2025)[03 How I Tested: Real Codebases, Not Clean Demos](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#how-i-tested-real-codebases-not-clean-demos)[04 Scored Rankings: 8 AI Coding Assistants Tested](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#scored-rankings-8-ai-coding-assistants-tested)[05 1. Augment Code](https://www.augmentcode.com/tools/8-top-ai-coding-assistants-and-their-best-use-cases#1-augment-code)Show 14 more."
      },
      {
        "title": "Terminal vs IDE: Why CLI is better for programming with AI - YouTube",
        "url": "https://www.youtube.com/watch?v=69UonSdioCI",
        "description": "Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=YFjfBk8HI5o Thank you for listening ❤ Check out our sponsors:"
      },
      {
        "title": "A map of AI coding tools: IDEs, agents, all-in-on app builders and ...",
        "url": "https://annaarteeva.medium.com/a-map-of-ai-coding-tools-ides-agents-all-in-on-app-builders-and-foundation-models-60a9b5f2ed80",
        "description": "A practical guide to understanding the AI coding ecosystem and picking the right tool for different use cases and workflows."
      }
    ]
  }
}

{
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      "type": "text",
      "text": "{\n  \"results\": [\n    {\n      \"title\": \"A Strategic Guide to Building Browser Agents | by Lawrence Emenike\",\n      \"url\": \"https://lawrence-emenike.medium.com/a-strategic-guide-to-building-browser-agents-aa6eaaa11a62\",\n      \"description\": \"Browser Agents are autonomous systems that process information and decide on actions to carry out across the web on our behalf. Browser agents\"\n    },\n    {\n      \"title\": \"Building a Browser Automation Agent - by Avi Chawla\",\n      \"url\": \"https://blog.dailydoseofds.com/p/building-a-browser-automation-agent\",\n      \"description\": \"## Building a Browser Automation Agent. GPT-5 is now the default model in **Factory**, which is found to be highly agentic, detail-oriented, and comprehensive, particularly when searching and planning. ### Building a Browser Automation Agent. Planner Agent creates an automation plan. The Browser Automation Agent executes it using the Stagehand tool. Automation LLM: Executes the plan using the Stagehand tool. #### Define Automation Planner Agent. The planner agent receives an automation task from the user and creates a structured layout for execution by the browser agent. #### Define Browser Automation Agent. Browser Automation Agent utilizes the aforementioned Stagehand tool for autonomous browser control and plan execution. Synthesis Agent acts as final quality control, refining output from the browser automation agent to generate a polished response. Here’s our multi-agent browser automation workflow in action, where we asked it to find the top contributor on the Stagehand GitHub repo:. Learn how to build Agentic systems in **a crash course with 14 parts**.\"\n    },\n    {\n      \"title\": \"Anyone building AI browser agents in 2025? : r/AI_Agents\",\n      \"url\": \"https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/anyone_building_ai_browser_agents_in_2025/\",\n      \"description\": \"[Skip to main content](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/anyone_building_ai_browser_agents_in_2025/#main-content)Anyone building AI browser agents in 2025? [![Image 1](https://styles.redditmedia.com/t5_8b5cvj/styles/communityIcon_izy2luu72nee1.png?width=96&height=96&frame=1&auto=webp&crop=96%3A96%2Csmart&s=20cf99757a0ac0f6735e20e69bab9bb46f409207) Go to AI_Agents](https://www.reddit.com/r/AI_Agents/). [r/AI_Agents](https://www.reddit.com/r/AI_Agents/)•6mo ago. [100xBot](https://www.reddit.com/user/100xBot/). [Discussion](https://www.reddit.com/r/AI_Agents/?f=flair_name%3A%22Discussion%22). [![Image 2: u/hyperagentapp avatar](https://styles.redditmedia.com/t5_h1e65y/styles/profileIcon_pd9pobyye6xg1.png?width=48&height=48&frame=1&auto=webp&crop=48%3A48%2Csmart&s=02489f673ecb092063d45ec265c6b6de5a8b426e)hyperagentapp](https://www.reddit.com/user/hyperagentapp/)•[Promoted](https://www.reddit.com/user/hyperagentapp/). [![Image 4: u/OpenAI avatar](https://styles.redditmedia.com/t5_bjljw0/styles/profileIcon_x9h0braqsqqf1.png?width=48&height=48&frame=1&auto=webp&crop=48%3A48%2Csmart&s=e2846c1c9fc9c34cff707cd34840d11651c18cbe)u/OpenAI](https://www.reddit.com/user/OpenAI/)•[Promoted](https://www.reddit.com/user/OpenAI/). [](https://www.reddit.com/user/Unfair-Goose4252/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nol0m4d/). [](https://www.reddit.com/user/SeaKoe11/). [SeaKoe11](https://www.reddit.com/user/SeaKoe11/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nolhljd/). [More replies](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nol0m4d/?force-legacy-sct=1). [](https://www.reddit.com/user/MoneyMediocre4791/). [MoneyMediocre4791](https://www.reddit.com/user/MoneyMediocre4791/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/norgq65/). [![Image 6: u/AutoModerator avatar](https://styles.redditmedia.com/t5_1yz875/styles/profileIcon_klqlly9fc4l41.png?width=64&height=64&frame=1&auto=webp&crop=64%3A64%2Csmart&s=4cd002de4de73dc33950158eb385a54026d627e1)](https://www.reddit.com/user/AutoModerator/). [AutoModerator](https://www.reddit.com/user/AutoModerator/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nofzlik/). Thank you for your submission, for any questions regarding AI, please check out our wiki at [https://www.reddit.com/r/ai_agents/wiki](https://www.reddit.com/r/ai_agents/wiki) (this is currently in test and we are actively adding to the wiki). Please_[_contact the moderators of this subreddit_](https://www.reddit.com/message/compose/?to=/r/AI_Agents)_if you have any questions or concerns._. [![Image 7: u/mouhcine_ziane avatar](https://styles.redditmedia.com/t5_fivtzc/styles/profileIcon_szywj544kqrf1.jpg?width=64&height=64&frame=1&auto=webp&crop=64%3A64%2Csmart&s=609084b9a3c1fecfbbbc09ebf217edba1a50936f)](https://www.reddit.com/user/mouhcine_ziane/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/noic750/). [](https://www.reddit.com/user/MoneyMediocre4791/). [MoneyMediocre4791](https://www.reddit.com/user/MoneyMediocre4791/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/norgfhg/). [More replies](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/noic750/?force-legacy-sct=1). [](https://www.reddit.com/user/robroyhobbs/). [robroyhobbs](https://www.reddit.com/user/robroyhobbs/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nol8mw6/). [](https://www.reddit.com/user/Double_Try1322/). [Double_Try1322](https://www.reddit.com/user/Double_Try1322/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nom2yjg/). [](https://www.reddit.com/user/Intelligent-Pen1848/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/np7t6vo/). [![Image 8: u/OpenAI avatar](https://styles.redditmedia.com/t5_bjljw0/styles/profileIcon_x9h0braqsqqf1.png?width=48&height=48&frame=1&auto=webp&crop=48%3A48%2Csmart&s=e2846c1c9fc9c34cff707cd34840d11651c18cbe)u/OpenAI](https://www.reddit.com/user/OpenAI/)•[Promoted](https://www.reddit.com/user/OpenAI/). [ProfessionalFun2573](https://www.reddit.com/user/ProfessionalFun2573/). •[5mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nqb5deq/). 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[Best AI web browsing agents](https://www.reddit.com/answers/26e24fee-f90d-4482-82f3-bc0cd0d44ccb/?q=Best+AI+web+browsing+agents&source=PDP). [Easiest way to build AI agents](https://www.reddit.com/answers/fedf2530-fb00-4904-844f-cf8791009fe2/?q=Easiest+way+to+build+AI+agents&source=PDP). [Innovative uses of AI agents in daily life](https://www.reddit.com/answers/2c071114-46a6-416a-aaf9-cdf06a6b0c4d/?q=Innovative+uses+of+AI+agents+in+daily+life&source=PDP).\"\n    },\n    {\n      \"title\": \"What Are AI Browser Agents and How to Build Them\",\n      \"url\": \"https://layerxsecurity.com/generative-ai/ai-browser-agents/\",\n      \"description\": \"# What Are AI Browser Agents and How to Build Them. This article will explore the architecture of AI browser agents, detail the different types of agents, and provide a guide on how to build them securely. ## The Architecture of AI Browser Agents. In the context of AI browsers, a learning agent could learn a user’s browsing habits and proactively fetch information or automate tasks it predicts the user will need. ## The Unseen Risks: Securing Your AI Browser Agents. While AI browser agents offer immense potential, they also introduce new and significant security risks. Traditional security solutions are often blind to the activities of AI browser agents, making it difficult to detect and prevent malicious behavior. By providing deep visibility into all browser activity, including the actions of AI browser agents, LayerX can provide the necessary visibility and control to secure these powerful tools. This browser-centric security model allows enterprises to safely adopt AI browsers and autonomous AI agents without exposing themselves to unnecessary risk.\"\n    },\n    {\n      \"title\": \"AI Web Agents: Complete Guide to Intelligent Browser Automation ...\",\n      \"url\": \"https://www.skyvern.com/blog/ai-web-agents-complete-guide-to-intelligent-browser-automation-november-2025/\",\n      \"description\": \"* AI web agents use LLMs and computer vision to automate browser tasks without breaking when websites change. This reasoning capability allows AI web agents to handle workflows across multiple websites without custom configuration for each one. Companies are adopting AI web agents to handle workflows that resist traditional automation: multi-vendor procurement, data extraction from systems lacking APIs, and cross-site research tasks. AI web agents handle business workflows that resist traditional automation. Procurement teams use AI web agents to automate ordering across multiple supplier websites. Putting AI web agents to use in your automation workflows isn't technically challenging but, adhering to a few strategies and best practices can help get you up and running more quickly:. AI web agents use computer vision and LLMs to understand what elements do based on context and appearance, while traditional tools rely on XPath selectors that break when websites change their layout. AI web agents bring reasoning to browser automation, handling variations in layout and structure without custom code for each site.\"\n    },\n    {\n      \"title\": \"[PDF] Building Browser Agents: Architecture, Security, and Practical ... - arXiv\",\n      \"url\": \"https://arxiv.org/pdf/2511.19477\",\n      \"description\": \"Building Browser Agents: Architecture, Security, and Practical Solutions Aram Vardanyan Founder, FillApp aram@fillapp.ai Browser agents enable autonomous web interaction but face critical reliability and security challenges in production. This paper presents findings from building and operating a production browser agent. The paper argues against develop-ing general browsing intelligence in favor of specialized tools with programmatic constraints, where safety boundaries are enforced through code instead of large language model (LLM) reasoning. Through hybrid context management combining accessibility tree snapshots with selective vision, comprehensive browser tooling matching human interaction capabilities, and intelligent prompt engineering, the agent achieved approximately 85% success rate on the WebGames benchmark across 53 diverse challenges (compared to approximately 50% reported for prior browser agents and 95.7% human baseline). Findings from building and operating a production browser agent reveal that LLM capability is not the limiting factor in widespread adoption.\"\n    },\n    {\n      \"title\": \"What are Agentic Browsers? Exploring AI-native Web Navigation | DigitalOcean\",\n      \"url\": \"https://www.digitalocean.com/resources/articles/agentic-browsers\",\n      \"description\": \"Agentic browsers use AI agents to autonomously navigate websites and complete tasks like booking flights, filling forms, and making purchases without manual clicking or human intervention. Agentic browsers use autonomous AI agents to navigate websites, interact with web elements, and complete tasks on behalf of users. Comet is built on the company’s existing AI search expertise to integrate autonomous agents that can navigate websites, complete forms, and execute multi-step workflows. The browser focuses on productivity use cases to enable AI agents to handle routine web tasks like data entry, research compilation, and workflow automation across multiple websites. Yes, beautiful visual designs will still be important for human users, but websites will need to integrate APIs that let AI agents access information and complete tasks more efficiently. Agentic browsers use AI agents that autonomously navigate websites, complete forms, and execute tasks based on user instructions. Leading options include Perplexity Comet (which integrates AI agents with web automation), Fellou browser (designed specifically for agentic workflows), and Dia browser (offering developer-focused customization tools).\"\n    },\n    {\n      \"title\": \"AI Agents: What They Are, How They Work, and Why Web Context Is ...\",\n      \"url\": \"https://www.firecrawl.dev/blog/ai-agents\",\n      \"description\": \"*   The web is the most comprehensive real-time data source, but feeding it to agents reliably requires scrapers that handle [HTML-to-Markdown conversion](https://www.firecrawl.dev/blog/scrape-a-website-to-markdown), extraction, and rate limiting. An AI agent is a system that uses an LLM to pursue a goal by picking [tools](https://www.firecrawl.dev/blog/agent-tools), observing results, and adapting its plan until the task is done or a boundary is hit. The concept of self-directed software agents predates LLMs by decades, but what changed between 2022 and 2024 was that chain-of-thought prompting, ReAct, and [function calling](https://www.firecrawl.dev/blog/api-for-ai-agents) made goal-directed tool use practical. People are building agents to manage their calendars, triage email, automate content production, conduct [deep research](https://www.firecrawl.dev/blog/deep-research-for-ai-agents), and handle tasks that would have taken hours of manual work. ![Image 4: AI agent architecture diagram showing LLM, memory, planning, tools, and retrieval components connected in a system](https://www.firecrawl.dev/images/blog/ai-agents/agent-architecture.webp). Without [web search for AI agents](https://www.firecrawl.dev/blog/best-ai-search-engines-agents), these agents are limited to training-time knowledge. ![Image 6: Decision flowchart for choosing between scraping, crawling, extraction, and web search when building AI agents](https://www.firecrawl.dev/images/blog/ai-agents/web-operations-flowchart.webp).\"\n    }\n  ]\n}"
    }
  ],
  "details": {
    "results": [
      {
        "title": "A Strategic Guide to Building Browser Agents | by Lawrence Emenike",
        "url": "https://lawrence-emenike.medium.com/a-strategic-guide-to-building-browser-agents-aa6eaaa11a62",
        "description": "Browser Agents are autonomous systems that process information and decide on actions to carry out across the web on our behalf. Browser agents"
      },
      {
        "title": "Building a Browser Automation Agent - by Avi Chawla",
        "url": "https://blog.dailydoseofds.com/p/building-a-browser-automation-agent",
        "description": "## Building a Browser Automation Agent. GPT-5 is now the default model in **Factory**, which is found to be highly agentic, detail-oriented, and comprehensive, particularly when searching and planning. ### Building a Browser Automation Agent. Planner Agent creates an automation plan. The Browser Automation Agent executes it using the Stagehand tool. Automation LLM: Executes the plan using the Stagehand tool. #### Define Automation Planner Agent. The planner agent receives an automation task from the user and creates a structured layout for execution by the browser agent. #### Define Browser Automation Agent. Browser Automation Agent utilizes the aforementioned Stagehand tool for autonomous browser control and plan execution. Synthesis Agent acts as final quality control, refining output from the browser automation agent to generate a polished response. Here’s our multi-agent browser automation workflow in action, where we asked it to find the top contributor on the Stagehand GitHub repo:. Learn how to build Agentic systems in **a crash course with 14 parts**."
      },
      {
        "title": "Anyone building AI browser agents in 2025? : r/AI_Agents",
        "url": "https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/anyone_building_ai_browser_agents_in_2025/",
        "description": "[Skip to main content](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/anyone_building_ai_browser_agents_in_2025/#main-content)Anyone building AI browser agents in 2025? [![Image 1](https://styles.redditmedia.com/t5_8b5cvj/styles/communityIcon_izy2luu72nee1.png?width=96&height=96&frame=1&auto=webp&crop=96%3A96%2Csmart&s=20cf99757a0ac0f6735e20e69bab9bb46f409207) Go to AI_Agents](https://www.reddit.com/r/AI_Agents/). [r/AI_Agents](https://www.reddit.com/r/AI_Agents/)•6mo ago. [100xBot](https://www.reddit.com/user/100xBot/). [Discussion](https://www.reddit.com/r/AI_Agents/?f=flair_name%3A%22Discussion%22). [![Image 2: u/hyperagentapp avatar](https://styles.redditmedia.com/t5_h1e65y/styles/profileIcon_pd9pobyye6xg1.png?width=48&height=48&frame=1&auto=webp&crop=48%3A48%2Csmart&s=02489f673ecb092063d45ec265c6b6de5a8b426e)hyperagentapp](https://www.reddit.com/user/hyperagentapp/)•[Promoted](https://www.reddit.com/user/hyperagentapp/). [![Image 4: u/OpenAI avatar](https://styles.redditmedia.com/t5_bjljw0/styles/profileIcon_x9h0braqsqqf1.png?width=48&height=48&frame=1&auto=webp&crop=48%3A48%2Csmart&s=e2846c1c9fc9c34cff707cd34840d11651c18cbe)u/OpenAI](https://www.reddit.com/user/OpenAI/)•[Promoted](https://www.reddit.com/user/OpenAI/). [](https://www.reddit.com/user/Unfair-Goose4252/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nol0m4d/). [](https://www.reddit.com/user/SeaKoe11/). [SeaKoe11](https://www.reddit.com/user/SeaKoe11/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nolhljd/). [More replies](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nol0m4d/?force-legacy-sct=1). [](https://www.reddit.com/user/MoneyMediocre4791/). [MoneyMediocre4791](https://www.reddit.com/user/MoneyMediocre4791/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/norgq65/). [![Image 6: u/AutoModerator avatar](https://styles.redditmedia.com/t5_1yz875/styles/profileIcon_klqlly9fc4l41.png?width=64&height=64&frame=1&auto=webp&crop=64%3A64%2Csmart&s=4cd002de4de73dc33950158eb385a54026d627e1)](https://www.reddit.com/user/AutoModerator/). [AutoModerator](https://www.reddit.com/user/AutoModerator/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nofzlik/). Thank you for your submission, for any questions regarding AI, please check out our wiki at [https://www.reddit.com/r/ai_agents/wiki](https://www.reddit.com/r/ai_agents/wiki) (this is currently in test and we are actively adding to the wiki). Please_[_contact the moderators of this subreddit_](https://www.reddit.com/message/compose/?to=/r/AI_Agents)_if you have any questions or concerns._. [![Image 7: u/mouhcine_ziane avatar](https://styles.redditmedia.com/t5_fivtzc/styles/profileIcon_szywj544kqrf1.jpg?width=64&height=64&frame=1&auto=webp&crop=64%3A64%2Csmart&s=609084b9a3c1fecfbbbc09ebf217edba1a50936f)](https://www.reddit.com/user/mouhcine_ziane/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/noic750/). [](https://www.reddit.com/user/MoneyMediocre4791/). [MoneyMediocre4791](https://www.reddit.com/user/MoneyMediocre4791/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/norgfhg/). [More replies](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/noic750/?force-legacy-sct=1). [](https://www.reddit.com/user/robroyhobbs/). [robroyhobbs](https://www.reddit.com/user/robroyhobbs/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nol8mw6/). [](https://www.reddit.com/user/Double_Try1322/). [Double_Try1322](https://www.reddit.com/user/Double_Try1322/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nom2yjg/). [](https://www.reddit.com/user/Intelligent-Pen1848/). •[6mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/np7t6vo/). [![Image 8: u/OpenAI avatar](https://styles.redditmedia.com/t5_bjljw0/styles/profileIcon_x9h0braqsqqf1.png?width=48&height=48&frame=1&auto=webp&crop=48%3A48%2Csmart&s=e2846c1c9fc9c34cff707cd34840d11651c18cbe)u/OpenAI](https://www.reddit.com/user/OpenAI/)•[Promoted](https://www.reddit.com/user/OpenAI/). [ProfessionalFun2573](https://www.reddit.com/user/ProfessionalFun2573/). •[5mo ago](https://www.reddit.com/r/AI_Agents/comments/1ov3j7a/comment/nqb5deq/). 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[Best AI web browsing agents](https://www.reddit.com/answers/26e24fee-f90d-4482-82f3-bc0cd0d44ccb/?q=Best+AI+web+browsing+agents&source=PDP). [Easiest way to build AI agents](https://www.reddit.com/answers/fedf2530-fb00-4904-844f-cf8791009fe2/?q=Easiest+way+to+build+AI+agents&source=PDP). [Innovative uses of AI agents in daily life](https://www.reddit.com/answers/2c071114-46a6-416a-aaf9-cdf06a6b0c4d/?q=Innovative+uses+of+AI+agents+in+daily+life&source=PDP)."
      },
      {
        "title": "What Are AI Browser Agents and How to Build Them",
        "url": "https://layerxsecurity.com/generative-ai/ai-browser-agents/",
        "description": "# What Are AI Browser Agents and How to Build Them. This article will explore the architecture of AI browser agents, detail the different types of agents, and provide a guide on how to build them securely. ## The Architecture of AI Browser Agents. In the context of AI browsers, a learning agent could learn a user’s browsing habits and proactively fetch information or automate tasks it predicts the user will need. ## The Unseen Risks: Securing Your AI Browser Agents. While AI browser agents offer immense potential, they also introduce new and significant security risks. Traditional security solutions are often blind to the activities of AI browser agents, making it difficult to detect and prevent malicious behavior. By providing deep visibility into all browser activity, including the actions of AI browser agents, LayerX can provide the necessary visibility and control to secure these powerful tools. This browser-centric security model allows enterprises to safely adopt AI browsers and autonomous AI agents without exposing themselves to unnecessary risk."
      },
      {
        "title": "AI Web Agents: Complete Guide to Intelligent Browser Automation ...",
        "url": "https://www.skyvern.com/blog/ai-web-agents-complete-guide-to-intelligent-browser-automation-november-2025/",
        "description": "* AI web agents use LLMs and computer vision to automate browser tasks without breaking when websites change. This reasoning capability allows AI web agents to handle workflows across multiple websites without custom configuration for each one. Companies are adopting AI web agents to handle workflows that resist traditional automation: multi-vendor procurement, data extraction from systems lacking APIs, and cross-site research tasks. AI web agents handle business workflows that resist traditional automation. Procurement teams use AI web agents to automate ordering across multiple supplier websites. Putting AI web agents to use in your automation workflows isn't technically challenging but, adhering to a few strategies and best practices can help get you up and running more quickly:. AI web agents use computer vision and LLMs to understand what elements do based on context and appearance, while traditional tools rely on XPath selectors that break when websites change their layout. AI web agents bring reasoning to browser automation, handling variations in layout and structure without custom code for each site."
      },
      {
        "title": "[PDF] Building Browser Agents: Architecture, Security, and Practical ... - arXiv",
        "url": "https://arxiv.org/pdf/2511.19477",
        "description": "Building Browser Agents: Architecture, Security, and Practical Solutions Aram Vardanyan Founder, FillApp aram@fillapp.ai Browser agents enable autonomous web interaction but face critical reliability and security challenges in production. This paper presents findings from building and operating a production browser agent. The paper argues against develop-ing general browsing intelligence in favor of specialized tools with programmatic constraints, where safety boundaries are enforced through code instead of large language model (LLM) reasoning. Through hybrid context management combining accessibility tree snapshots with selective vision, comprehensive browser tooling matching human interaction capabilities, and intelligent prompt engineering, the agent achieved approximately 85% success rate on the WebGames benchmark across 53 diverse challenges (compared to approximately 50% reported for prior browser agents and 95.7% human baseline). Findings from building and operating a production browser agent reveal that LLM capability is not the limiting factor in widespread adoption."
      },
      {
        "title": "What are Agentic Browsers? Exploring AI-native Web Navigation | DigitalOcean",
        "url": "https://www.digitalocean.com/resources/articles/agentic-browsers",
        "description": "Agentic browsers use AI agents to autonomously navigate websites and complete tasks like booking flights, filling forms, and making purchases without manual clicking or human intervention. Agentic browsers use autonomous AI agents to navigate websites, interact with web elements, and complete tasks on behalf of users. Comet is built on the company’s existing AI search expertise to integrate autonomous agents that can navigate websites, complete forms, and execute multi-step workflows. The browser focuses on productivity use cases to enable AI agents to handle routine web tasks like data entry, research compilation, and workflow automation across multiple websites. Yes, beautiful visual designs will still be important for human users, but websites will need to integrate APIs that let AI agents access information and complete tasks more efficiently. Agentic browsers use AI agents that autonomously navigate websites, complete forms, and execute tasks based on user instructions. Leading options include Perplexity Comet (which integrates AI agents with web automation), Fellou browser (designed specifically for agentic workflows), and Dia browser (offering developer-focused customization tools)."
      },
      {
        "title": "AI Agents: What They Are, How They Work, and Why Web Context Is ...",
        "url": "https://www.firecrawl.dev/blog/ai-agents",
        "description": "*   The web is the most comprehensive real-time data source, but feeding it to agents reliably requires scrapers that handle [HTML-to-Markdown conversion](https://www.firecrawl.dev/blog/scrape-a-website-to-markdown), extraction, and rate limiting. An AI agent is a system that uses an LLM to pursue a goal by picking [tools](https://www.firecrawl.dev/blog/agent-tools), observing results, and adapting its plan until the task is done or a boundary is hit. The concept of self-directed software agents predates LLMs by decades, but what changed between 2022 and 2024 was that chain-of-thought prompting, ReAct, and [function calling](https://www.firecrawl.dev/blog/api-for-ai-agents) made goal-directed tool use practical. People are building agents to manage their calendars, triage email, automate content production, conduct [deep research](https://www.firecrawl.dev/blog/deep-research-for-ai-agents), and handle tasks that would have taken hours of manual work. ![Image 4: AI agent architecture diagram showing LLM, memory, planning, tools, and retrieval components connected in a system](https://www.firecrawl.dev/images/blog/ai-agents/agent-architecture.webp). Without [web search for AI agents](https://www.firecrawl.dev/blog/best-ai-search-engines-agents), these agents are limited to training-time knowledge. ![Image 6: Decision flowchart for choosing between scraping, crawling, extraction, and web search when building AI agents](https://www.firecrawl.dev/images/blog/ai-agents/web-operations-flowchart.webp)."
      }
    ]
  }
}