AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
📰 ArXiv cs.AI
Learn to evaluate coding agents with AgentLens, a production-assessed benchmark that reviews the entire trajectory of agent interactions, not just task success
Action Steps
- Implement AgentLens to evaluate coding agents
- Use formal verification to assess agent trajectories
- Analyze agent interactions, including instruction following and tool usage
- Evaluate agent recovery from mistakes and communication with users
- Compare agent performance using production-assessed benchmarks
Who Needs to Know This
DevOps and software engineering teams can benefit from AgentLens to assess and improve the performance of coding agents in real-world scenarios
Key Insight
💡 AgentLens evaluates the entire trajectory of coding agent interactions, providing a more comprehensive assessment of agent performance
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🤖 Evaluate coding agents with AgentLens, a new benchmark that assesses the entire trajectory of agent interactions 📊
Key Takeaways
Learn to evaluate coding agents with AgentLens, a production-assessed benchmark that reviews the entire trajectory of agent interactions, not just task success
Full Article
Title: AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
Abstract:
arXiv:2607.06624v1 Announce Type: new 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, wher
Abstract:
arXiv:2607.06624v1 Announce Type: new 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, wher
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