Evals Turn Agent Behavior Into Engineering Evidence
A trace tells you what happened. An eval tells you whether it was acceptable. A regression gate keeps the next change from quietly making it worse. Separate unit, integration, end-to-end, and behavioral evaluation layers. Build realistic cases with output, trajectory, final-state, safety, cost, and latency checks. Calibrate model-based graders against human judgments. Classify transport, protocol, model, tool, contract, and policy failures. Design traces that support reproduction without leaking sensitive data. Use regression thresholds and statistical comparison for non-deterministic systems. An order agent responds, "Your replacement has been shipped." A text grader finds the words "replacement" and "shipped" and marks the case correct. The trace shows no shipping tool call. The order database shows no replacement. The agent invented a successful action. The output grader passed. The application failed. AI evaluation must reach beyond prose. A production case can have several independent expectations: The answer states only verified facts. The correct tool was selected. No forbidden tool was selected. Tool arguments matched the authenticated user. The final external state changed as intended. An unsafe request caused no side effect. Latency and cost remained within budget. Treat these as separate checks. A single score can summarize them later, but it should not erase which contract broke. Do not use an LLM judge to test code that a unit test can prove. Unit…
Evals Turn Agent Behavior Into Engineering Evidence: A trace tells you what happened. An eval tells you whether it was acceptable. A regression gate keeps the…
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