Create evaluation sets that reflect real workflows, costly mistakes and user expectations. Use them to guide AI releases rather than relying on a successful demo.
Collect examples from the workflow
A useful evaluation set contains common tasks, ambiguous requests and failure cases. Include examples where the correct response is to ask a question or decline an unsupported action. Keep private data out of test material unless its use is authorized.
Label the expected outcome, the evidence required and the severity of a wrong result. This makes review more consistent than simply asking whether the answer sounds good.
Separate the layers of quality
For a knowledge assistant, inspect source selection, access control, factual support and response usefulness independently. For an agent, also check tool selection and resulting system state. A fluent answer can conceal a failed transaction.
Choose release criteria before comparing changes. For high-impact workflows, include a human review process instead of reducing everything to a single score.
Make evaluation part of operations
Re-run relevant cases when models, prompts, tools or sources change. Review new production failures and add them to the test library. Monitor costs and latency as supporting operational measures. Use security guidance such as OWASP’s agentic framework to identify additional test scenarios, without treating a passing test set as a guarantee of safety.
A practical next step
- Define acceptable outcomes for one workflow.
- Add ambiguous, unauthorized and incomplete requests.
- Set release gates before testing a change.
- Feed observed failures back into the evaluation set.
Primary reference
OWASP Agentic Applications 2026. Product features and availability can change; check the relevant documentation for your environment.
How Codeiz can help
Discuss the scope, integration constraints and delivery priorities with our ai & machine learning team. We start with your business problem and an agreed definition of success.
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