Agent Evaluation

Evaluating agents is hard because the output is a trajectory, not a single token. A correct final answer that wasted 20 tool calls is worse than a slightly less accurate answer reached efficiently. Agent eval frameworks like AgentBench, WebArena, and custom harnesses measure task success rate, step efficiency, hallucination rate in tool arguments, and regression across versions.