When a team evaluates an AI model, it may start with benchmark scores or the percentage of questions answered correctly. These measures have a place, but an autonomous agent is a participant in a workflow rather than a standalone answer engine. It observes partial context, selects tools, decides whether to ask for more information, executes actions and must recognize when it should stop. Success therefore depends on the whole system's behavior under realistic constraints.
A useful evaluation corpus begins with business tasks and their consequences. Take a refund: which customer requested it, whether the original payment was settled, the policy applicable at that time, the upper amount, the correct merchant account and whether an earlier attempt already succeeded. The outcome is not simply a sentence saying the refund is eligible. It is a sequence of authorized decisions, a correctly recorded transaction or a justified refusal. The evaluation should score each stage, especially boundaries that must never be crossed.
Measure more than pass rate. Track unauthorized action attempts, wrong-tenant leakage, repeated side effects, escalation quality, false certainty, recovery from tool failures, human review burden and cost per accepted result. Tests should include ambiguous instructions, stale permissions, incomplete data and intentionally hostile input from external documents. A system that politely fails safely on uncertainty may be more useful than one that always returns a confident answer.
Evaluation is not a launch ceremony. Policies change, provider behavior changes and the data distribution shifts as customers adopt new patterns. Preserve representative traces, isolate sensitive data and rerun outcome-based suites after changes to prompts, models, tools, semantics or authorization. The point of evaluation is not to convince ourselves the model is smart. It is to demonstrate that the business can trust the workflow.