LangSmith published a step-by-step agent eval checklist covering trace analysis, failure categorization, and CI/CD integration for production agent systems.
LangSmith released a detailed agent evaluation checklist as a companion to their earlier post on agent observability. The checklist walks teams through building, running, and shipping agent evals using LangSmith's traces, annotation queues, and experiment tooling. It distinguishes capability evals (what can the agent do?) from regression evals (does it still work?), and maps specific failure types — prompt issues, tool design flaws, knowledge gaps — to concrete fixes. The guide recommends spending 60–80% of eval effort on error analysis before building any automated infrastructure.
Most teams building agents skip structured evals because the setup feels expensive — this checklist removes that barrier. The capability vs. regression split is the most underused pattern in agent dev: capability evals give you a hill to climb, regression evals catch backsliding before it hits prod. The concrete failure taxonomy (prompt bug vs. tool interface bug vs. knowledge gap) directly maps to where in your stack you fix the problem — no more guessing whether to tweak the prompt or redesign the tool.
Set up a LangSmith annotation queue on your agent's 20 most recent production traces this week. Tag each failure by type (prompt, tool, knowledge) — if more than 40% cluster in one category, you have your first targeted eval to build.
Go to smith.langchain.com and open your active project's Traces view
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