AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
AgentLens is a production-assessed benchmark for interactive code agents that evaluates the entire trajectory — instruction following, tool usage, self-verification, error recovery, and communication — rather than just pass/fail. It pairs formal verification with LLM-written trajectory reviews and side-by-side comparisons to provide readable explanations of scores. Useful for model diagnosis, version comparison, and regression detection. Open-sourced on GitHub.
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[Submitted on 7 Jul 2026]
Title:AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
View a PDF of the paper titled AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation, by Andrey Podivilov and 6 other authors
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Abstract:We present AgentLens, a production-assessed benchmark for interactive code agents. Most code-agent benchmarks reduce a run to a single bit -- did the task pass? -- but the people who actually use these agents experience the entire trajectory: how the agent follows instructions, uses its tools, verifies its own work, recovers from mistakes, and talks to them along the way. AgentLens evaluates that whole trajectory. It pairs formal verification, where an objective check exists, with LLM-written trajectory reviews and side-by-side comparisons, so that each run yields a readable explanation of why the score is what it is. This makes AgentLens useful for more than ranking models: we use it to diagnose model behavior, compare successive versions of our own agent, and catch product regressions in a nightly evaluation pipeline. We release the benchmark as open source at this https URL.
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
Cite as: arXiv:2607.06624 [cs.AI]
(or arXiv:2607.06624v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.06624
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Vadim Lomshakov [view email] [v1] Tue, 7 Jul 2026 11:27:43 UTC (263 KB)
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