AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
Quick Answer
AgentLens introduces a comprehensive benchmark for evaluating interactive code agents by assessing their entire operational trajectory rather than just pass/fail metrics.
Quick Take
It combines formal verification with -generated reviews to provide insights into agent behavior, making it valuable for diagnosing issues and tracking model improvements. The benchmark is available as open source.
Key Points
- AgentLens evaluates the entire trajectory of code agents, not just pass/fail outcomes.
- It combines formal verification with LLM-generated explanations for better insights.
- The benchmark aids in diagnosing model behavior and tracking product regressions.
- AgentLens is available as open source for broader community use.
- It enhances the evaluation process for interactive code agents significantly.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.