arXiv:2605.11030v2 Announce Type: replace-cross Abstract: Closed-loop tool-using agents are increasingly evaluated in executable web, code, and micro-task environments, but benchmark reports often conflate workloads, action-generating drivers, and the evidence admitted for systems-facing claims. We present an executable benchmarking suite that makes these objects explicit under a shared evidence-admission contract. The suite connects WebArena Verified, a SWE-Gym slice with SWE-bench-compatible verification, and MiniWoB++ through common workload adapters, task manifests, event schemas, replay/f
The rapid development and deployment of AI agents necessitate robust and standardized evaluation methods to ensure their reliability and performance in real-world applications.
A strategic reader should care because standardized benchmarking for AI agents will accelerate their development, deployment, and adoption, influencing how businesses and industries leverage autonomous systems.
The explicit and shared evidence-admission contract provided by this benchmarking suite will allow for more rigorous and comparable evaluations of tool-using AI agents.
- · AI agent developers
- · Businesses adopting AI agents
- · AI research community
- · Software testing industry
- · AI companies with overhyped or underperforming agents
- · Proprietary, non-standardized evaluation methods
Improved and more reliable AI agents will become available for diverse applications.
Increased adoption of AI agents could lead to significant automation advancements across various industries.
Standardized performance metrics might catalyze a 'feature race' among AI agent developers, accelerating innovation and potentially leading to more sophisticated, autonomous systems.
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Read at arXiv cs.AI