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Emergence WebVoyager: Toward Consistent and Transparent Evaluation of (Web) Agents in The Wild

arXiv AI Archived Apr 01, 2026 ✓ Full text saved

arXiv:2603.29020v1 Announce Type: new Abstract: Reliable evaluation of AI agents operating in complex, real-world environments requires methodologies that are robust, transparent, and contextually aligned with the tasks agents are intended to perform. This study identifies persistent shortcomings in existing AI agent evaluation practices that are particularly acute in web agent evaluation, as exemplified by our audit of WebVoyager, including task-framing ambiguity and operational variability tha

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    Computer Science > Artificial Intelligence [Submitted on 30 Mar 2026] Emergence WebVoyager: Toward Consistent and Transparent Evaluation of (Web) Agents in The Wild Deepak Akkil, Mowafak Allaham, Amal Raj, Tamer Abuelsaad, Ravi Kokku Reliable evaluation of AI agents operating in complex, real-world environments requires methodologies that are robust, transparent, and contextually aligned with the tasks agents are intended to perform. This study identifies persistent shortcomings in existing AI agent evaluation practices that are particularly acute in web agent evaluation, as exemplified by our audit of WebVoyager, including task-framing ambiguity and operational variability that hinder meaningful and reproducible performance comparisons. To address these challenges, we introduce Emergence WebVoyager, an enhanced version of the WebVoyager benchmark that standardizes evaluation methodology through clear guidelines for task instantiation, failure handling, annotation, and reporting. Emergence WebVoyager achieves an inter-annotator agreement of 95.9\%, indicating improved clarity and reliability in both task formulation and evaluation. Applying this framework to evaluate OpenAI Operator reveals substantial performance variation across domains and task types, with an overall success rate of 68.6\%, substantially lower than the 87\% previously reported by OpenAI, demonstrating the utility of our approach for more rigorous and comparable web agent evaluation. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.29020 [cs.AI]   (or arXiv:2603.29020v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.29020 Focus to learn more Submission history From: Mowafak Allaham [view email] [v1] Mon, 30 Mar 2026 21:27:28 UTC (377 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
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    ◬ AI & Machine Learning
    Published
    Apr 01, 2026
    Archived
    Apr 01, 2026
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