Artificial intelligence agent evaluation tests both final outcomes and the process used to reach them. It can measure task success, tool selection, policy compliance, error recovery, cost, latency, and reliability across repeated trials.
Evaluation is easier in domains where experts already have clear ways to judge outputs. Strong evaluation uses representative tasks, explicit criteria, and retained evidence rather than relying on a persuasive-looking answer.
