arXiv:2606.15673v1 Announce Type: new Abstract: Web agents act through long interaction sequences, yet existing benchmarks evaluate only terminal success, discarding all process information and offering little guidance on improvement. In this work, we conduct a process-level analysis of web agents. We introduce WebStep, a benchmark of 1,800 task instances with controlled difficulty and automatic semantic state tracking. Each website exposes a deterministic semantic MDP alongside the GUI: the agent operates on the interface, while the environment records high-level states and transitions in the
The rapid advancement and deployment of AI agents necessitate more sophisticated evaluation methods beyond simple task completion, leading to a focus on process-level analysis.
This development addresses a critical limitation in AI agent evaluation, directly impacting the speed and reliability of developing advanced autonomous systems.
The focus shifts from merely evaluating terminal success of web agents to understanding and optimizing their internal decision-making processes and intermediate steps.
- · AI Agent developers
- · AI evaluation companies
- · Companies relying on AI for workflow automation
- · Developers of brittle or non-interpretable AI agents
- · Traditional, outcome-only AI benchmark methodologies
Improved debugging and development cycles for autonomous web agents due to detailed process insights.
Faster and more reliable deployment of AI agents across various industries, leading to increased automation efficiency.
Enhanced trust and adoption of AI agents in complex, high-stakes environments where process transparency is critical.
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Read at arXiv cs.AI