An AI evaluation produces a judgment: an answer passed, a tool action was acceptable, or a task succeeded. Before that judgment controls a software release, ask a second question: how do we know the check is trustworthy?
A November 4 AWS post describes connecting decision models to agent evaluations through code-based evaluators, including validating a check before using it to gate a release. That is a useful supporting example—not proof that any evaluator is ready to make release decisions.
The following is provider-neutral engineering guidance, not experimental findings from that post.
Start with a question you can actually judge
Avoid beginning with a broad instruction such as “decide whether this agent is good.” Choose a specific requirement instead: did the answer include the required disclosure, did the calculation match the reference result, or did the agent stop when a necessary input was missing?
Use a deterministic check—a rule that produces the same result for the same input—where the requirement permits it. Reserve model-based judgment for cases that need interpretation, and define what counts as acceptable before selecting the evaluator.
Build examples that can expose mistakes
Create a small, human-reviewed set containing acceptable outputs, unacceptable outputs and difficult boundary cases. Document the reasoning behind each expected judgment. Where reviewers disagree, clarify the requirement rather than hiding the disagreement inside a score.
Keep some examples separate from those used to tune the check. Otherwise, repeated adjustments may make the evaluator look convincing on familiar cases without showing whether it handles new ones.
Measure two kinds of error
A false pass accepts an output that should fail. A false failure rejects an output that should pass. Count them separately and retain the number of examples behind each result.
Their consequences differ. In a hypothetical support workflow, accepting an unsupported refund promise and rejecting a harmless wording variation should not automatically carry equal weight. Set acceptance criteria according to the workflow’s risks, not a universal accuracy target.
Also distinguish a negative judgment from an evaluator timeout or parsing error. A check that did not complete has not established that the answer passed—or failed.
Observe before enforcing
Consider first running the evaluator in observation mode: record its verdict without allowing it to block releases. Compare those verdicts with reviewed outcomes and investigate disagreements. This is a recommendation for evaluating software, not a requirement for additional manual authorization in an automatically configured publishing campaign.
Record the evaluator version, relevant input references, verdict and error status. Limit sensitive data in logs and restrict access. More detailed records can help diagnosis, but they also create storage and privacy obligations.
Keep the gate explainable
Before enforcement, define what happens when the evaluator is unavailable or uncertain. For consequential workflows, consider pausing the affected operation rather than treating a missing verdict as permission. Choose the behavior deliberately and document the tradeoff.
Recheck the evaluator when its rules, model or task distribution changes. A passing test set is evidence about those examples, not a guarantee about every future case.
A useful first step is modest: select one consequential requirement, collect reviewed examples, and inspect the check’s mistakes. The goal is not simply to add a score. It is to understand what that score can—and cannot—justify.