The Most Common Questions About Agent Evals, Answered

Agent evaluations address the difficulty of testing AI agents, whose outputs and decision paths can vary even when requests are identical, by assessing not only final responses but also tool usage, intermediate steps, conversations, and resulting environmental states. A practical process begins with reviewing execution traces, creating a small dataset of representative behaviors and failures, establishing a baseline experiment, and continuously adding newly discovered production issues to create an improvement loop. Offline evaluations use fixed datasets to compare versions before release and catch regressions, while online evaluations assess sampled live traffic to monitor real-world quality and supply new test cases. Common evaluator types include deterministic code checks, LLM-as-a-judge assessments, human review, and pairwise comparisons, with LLM judges made more reliable through narrow questions, clear scales, and calibration against human ratings. For complex multi-step agents, benchmarks can use isolated containerized environments and test scripts to verify the state an agent leaves behind, supported by tools such as Harbor and LangSmith. The approach also covers multi-turn conversations, production-cost management through sampling and lower-cost evaluators, reusable templates and automatically generated evals, framework-agnostic tracing, migration from existing testing stacks, and dashboards for tracking quality, latency, cost, and release-to-release performance.

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