An Executable Benchmarking Suite for Tool-Using Agents

2026-07-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

An executable benchmarking suite addresses the conflation of workload, driver, and evidence in evaluating closed-loop tool-using agents. Developed by Stevens Institute of Technology, the suite introduces an explicit evidence-admission contract, connecting WebArena Verified, a SWE-Gym slice, and MiniWoB++ via shared adapters, manifests, and event schemas. In its canonical release, the suite admits 930 paper-facing rows while explicitly excluding 1,184 non-admitted rows, ensuring auditable claims. It reports systems-facing outputs such as model-call latency, invalid-action behavior, and patch-generation cost. A separate WebArena Verified controller study demonstrated that clean-baseline and medium live-stressed operating settings can select different fixed controller variants, reversing ordering across shipped rollout backends and every directly comparable backend–seed–budget cell in the tested grid, highlighting the decision-relevance of the evidence gate.

Key takeaway

For AI Scientists or MLOps Engineers evaluating tool-using agents, recognize that benchmark results are highly sensitive to evaluation settings and evidence admission. Relying solely on clean-baseline evaluations can lead to mis-ranking agent controllers, as demonstrated by the reversal of controller ordering under live-stressed conditions. You should adopt explicit evidence-admission contracts and test agents across diverse operating settings to ensure decision-relevant and robust conclusions, rather than just relying on workload names.

Key insights

An executable benchmarking suite provides an auditable evidence-admission contract for evaluating tool-using agents, distinguishing workloads, drivers, and settings.

Principles

• Explicitly separate workload, driver, and operating setting in evaluations.
• Implement an evidence-admission contract to validate paper-facing claims.
• Decision-relevant evidence gates are crucial for valid systems conclusions.

Method

The suite connects WebArena Verified, a SWE-Gym slice, and MiniWoB++ using shared workload adapters, task manifests, event schemas, replay classes, and an evidence-admission gate to filter paper-facing claims from diagnostic runs.

In practice

• Use the evidence gate to filter diagnostic runs from paper-facing claims.
• Evaluate agent controllers under both clean and live-stressed operating settings.
• Report systems-facing outputs like model-call latency and invalid-action rates.

Topics

• Tool-Using Agents
• Benchmarking Suites
• Evidence Admission
• WebArena Verified
• SWE-Gym
• LLM Evaluation

Code references

• openrlhf/openrlhf
• ServiceNow/AgentLab
• ServiceNow/webarena-verified
• verl-project/verl
• xlang-ai/OSWorld

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.