Agent Evaluation

Agent evaluation measures whether an agent reliably achieves its goals across a representative set of tasks and edge cases. Unlike single-call evals, agent evals must account for multi-step behavior: intermediate action quality, tool use correctness, step efficiency, and final outcome accuracy all matter. Trajectory replay, sandbox execution, and LLM-as-judge scoring are common evaluation techniques.