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Agent Evaluation Checklist: How to Build, Run, and Ship Agent Evals

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Build effective agent evaluation systems by starting with simple, high-signal end-to-end evals and iteratively increasing complexity. Use observability tools like LangSmith to analyze real agent traces, define clear success criteria, and separate capability vs regression evals. Focus heavily on failure analysis by categorizing issues (prompt design, tool interfaces, model limits, or data gaps) before automating evaluation. Leverage evaluation levels—single-step (run), full-turn (trace), and multi-turn (thread)—with trace-level evals as the most practical starting point. Ensure infrastructure issues are ruled out, assign ownership to a domain expert, and validate not just outputs but real-world state changes. This approach improves agent reliability, debugging, and continuous performance optimization.

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