Definition. Production agent quality requires two loops: evaluations that score the system against representative tasks and observability that explains individual runtime behavior.
Evaluate the task, not just the text
A polished final answer can still hide a bad tool choice, unauthorized action, missed escalation or excessive spend. Build evaluations around the workflow's actual success criteria, including tool calls and side effects.
Trace the decision path
Record model selections, tool requests, retrieved context, policy decisions, latency, token or inference cost and external results. Avoid logging secrets or unnecessary personal data; the observability system should itself respect security boundaries.
Create a regression set
Start with real representative tasks and known failure modes. Add every important production incident as a test case. Run the set when prompts, models, tools, retrieval logic or policies change.
Set an autonomy budget
Increase autonomy only when the evaluation data supports it. Some actions can run automatically; others should draft, ask for approval, or hand off to a person. The interface between those states should be explicit.
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