How do we build agent evaluation different from LLM evaluation?

Add trajectory-quality (were the intermediate steps sensible), task-success-rate (did the agent complete the goal), cost-to-solution (tokens + tool calls), guardrail-trigger rate, and human-override rate. Standard LLM benchmarks (MMLU, HumanEval) tell you nothing about agent quality — build your own harness from real production trajectories.