AgentLens Evaluates Coding Agent Trajectories
Andrey Podivilov · hf · 2026-07-10
AgentLens is a production-grade benchmark for coding agents that looks beyond task pass rates. It evaluates the agent's performance across its entire execution trajectory: instruction adherence, tool usage, self-verification, error correction, and user interaction.
The benchmark combines formal verification with LLM trajectory review and pairwise comparisons, ensuring every run yields a readable score explanation. The authors note that this methodology can be used not only for model rankings but also for diagnosing agent behaviors, comparing version differences, and catching product regressions in nightly evaluation pipelines. The project is open-source.
More from coding & agent
- HeyGen adds a media-sourcing skill for coding agents with 75k images and 10k tracks — HeyGen · 2026-07-22
- Agent search bottlenecks are now about variance, not raw latency — rohanpaul_ai · 2026-07-22
- LangSmith adds tracing for Pipecat, LiveKit, OpenAI Realtime, and Gemini Live — LangChain · 2026-07-22
- An MCP server signs every AI agent tool call into a verifiable Merkle chain — Funky_Chicken_22 · 2026-07-22
- Annotated transcript of a Claude Code team interview is now available — trq212 · 2026-07-22
- Claude Code skill uses 10 Markdown rules to make outputs ADHD-friendly — alex_verem · 2026-07-22