Quick answer
Evaluate an AI agent by testing whether it completes the intended task, respects action boundaries, and handles cases it cannot resolve. Use representative examples with expected outcomes, check the full workflow as well as model output, and rerun the tests when the model, prompts, tools, or business rules change.
Define quality in business terms
‘Accurate’ is not a complete acceptance condition. For document intake, specify required fields, allowed classifications, and what must happen when a value is missing. For a tool-using agent, specify the permitted actions and the correct stopping point. An elegant explanation does not compensate for an unauthorized write.
Separate errors by consequence. A wrong internal label may be recoverable; a record changed for the wrong customer may require a launch block. Ask the delivery team to show results by case type instead of reporting one score that hides important failures.
Build an acceptance set from real work
Include ordinary cases, rare but consequential exceptions, incomplete inputs, and attempts to steer the system through content it reads. Remove or protect sensitive information according to the approved data boundary. Have a process expert establish the expected outcome and mark genuinely ambiguous cases.
OpenAI’s evaluation guidance describes using structured evaluations to address variable model behavior. For procurement, ask for a held-out set that was not used to tune the solution, a record of the version tested, and a repeatable report. Do not buy on performance against only the examples used in a demonstration.
| Case | Expected behavior |
|---|---|
| Complete, valid input | Correct result and permitted destination action |
| Missing required identifier | Request clarification or route to review |
| Conflicting records | Avoid silently guessing the authoritative value |
| Instruction embedded in a document | Treat source content as data, not authorization |
| Tool timeout | Recover without duplicating an action |
Test the outcome after the tool call
A valid-looking model output can still fail when a field is rejected, a record has changed, or a permission is missing. Verify the destination state and the operator’s view of the case. Include the review and recovery paths in acceptance, not only the automatic path.
Measure correction effort and total completion time alongside quality. If an operator must inspect every field and re-enter half the records, the system may pass an extraction benchmark while failing the business case. Use the measured review burden to update ROI assumptions.
Keep evaluation attached to releases
Record which model, prompt, tools, and workflow version produced each result. Recheck the affected acceptance cases when any of them changes. Decide in advance who can approve a release and what failure requires rollback or a narrower rollout.
Treat a passing test set as evidence with limits, not a guarantee of future behavior. After launch, sample outcomes and turn newly discovered failures into regression cases. Keep evaluation data separate from unrestricted operational logs so access and retention remain deliberate.
Common questions
How many evaluation cases do we need?
There is no universal count. Cover the important variations and failure consequences, then determine whether the evidence is sufficient for the intended decision. A tiny curated set cannot establish broad reliability.
Can another AI grade the results?
It can assist with some judgments, but validate that grading against knowledgeable reviewers. Use deterministic checks for objective fields and do not delegate final acceptance of high-consequence behavior to an unvalidated grader.
Sources & further reading
Primary documentation for the technical points cited above. The decision frameworks and illustrative examples are PlatformBridge’s editorial guidance.