How to know an AI agent is ready for production

On 24 July 2026, AWS published guidance for evaluating production AI agents. The lesson is not to adopt one tool: an agent is not ready because it replies fluently, but because it passes verifiable task criteria.

Three different layers

First come pre-deployment tests: known cases with expected outcomes. Then production observation: traces of calls, tools and real errors. Finally comes effect verification: confirming that an appointment was created, a query reached the right source or a change was applied in the destination system. No layer replaces the others.

AWS documentation explains that evaluations can score traces with built-in or custom evaluators. A score is a signal, not a verdict. Model-based evaluators can classify responses; critical facts also need deterministic checks, such as validating an identifier, permission or destination record.

A minimum cycle

Define one task and observable result. Collect normal and edge cases. Record each trace: goal, tool, input, output and reply. Review a sample of apparent successes and failures. Group repeated causes. Change one thing at a time —a prompt, tool description or validation— and compare the variant before promotion.

AgentCore on-demand evaluation illustrates the pattern: teams can select specific traces to investigate an incident or validate a fix. That prevents an acceptable average from hiding a particular task that still fails.

The right question

Before deployment, ask: what proves the agent completed the user goal? If the answer is only “there was no error”, a metric is missing. If it includes outcome evidence and a reviewable trace, a team can detect regressions and learn. That is the durable skill: turning a convincing demo into a service that can be checked.