AI agent evaluations, often called evals, test whether an agent completes a task according to defined criteria. For a business workflow, the final response is only part of the result. The records changed, tools called and permissions respected also matter.
A demonstration can show that an agent succeeds once. A useful evaluation process helps a team understand how it behaves across ordinary requests, difficult cases and changes to the system.
Define Success Before Running The Test
Consider a hypothetical assistant preparing a project-status answer. Success might require retrieving the correct project, using current information and clearly identifying a missing delivery estimate. The assistant must also avoid reading a different customer's records or inventing a date.
Write down which actions are allowed and which conditions should trigger a stop or a request for help. Keep these criteria separate from the prompt being improved. Otherwise, it becomes easy to move the definition of success whenever a test fails.
Allow more than one valid route to the result. An agent that completes the task correctly should not fail solely because it used a different permitted tool sequence from the example.
Build Cases Around Actual Failure Consequences
Begin with a manageable set of representative requests. Include straightforward tasks, incomplete information and failures in connected tools. Add requests the agent must refuse or redirect because the necessary access or approval is absent.
For the status assistant, useful cases include an unknown project, an outdated progress note, two projects with similar names and a service that times out. Each case should specify the starting records and the observable outcome.
Keep some cases separate from those used during development. Repeatedly tuning against every available example makes it harder to tell whether the change generalises. Use synthetic or appropriately approved records, rather than copying private customer conversations into an unrestricted test collection.
Run Repeated Trials From A Known State
Reset the environment before each attempt. If an agent changes a record during one trial, the next trial should not inherit that change unless the test explicitly depends on it.
Repeat important cases because agent behaviour can vary. Record the model, instructions, tools and configuration used for each run. A comparison becomes difficult to interpret when several parts changed and the earlier settings are missing.
For workflows that send messages or change external systems, use isolated substitutes. An evaluation should not contact real customers or alter production records merely to check whether a tool would have worked.
Inspect The Outcome And The Execution
Use direct checks when the answer is definite. If a task requires saving a record, verify the saved state. If duplicate submission must be prevented, inspect the number of resulting records. A friendly confirmation message does not prove either condition.
Where judgement is needed, use a clear scoring guide. For example, a summary might need to preserve the decision, unresolved questions and relevant evidence without adding unsupported claims. Compare automated grading with human reviews before relying on it.
Keep enough execution evidence to investigate failures, with appropriate access and retention. Sensitive tool inputs should not become casually shared test reports.
Compare Changes And Preserve Important Behaviours
Track task completion, prohibited actions, corrections, time and cost. Report critical failures separately rather than hiding them inside an average score.
A model update that improves summaries but starts making unapproved changes needs investigation even if its overall completion score rises. Set release criteria around the consequences that matter for the workflow.
Run the relevant cases again when prompts, tools, retrieval or models change. Our guides to context engineering and agent memory describe two sources of behaviour that should be included in those comparisons.
Use Production Findings To Improve The Test Set
An eval describes the cases tested. Continue monitoring real use and reviewing important actions after release. Turn a confirmed failure into a regression case with a clear expected outcome.
For a first AI integration, agree on a small task, an acceptance record and an owner for failed cases. That creates a practical basis for deciding whether the system is ready for broader use.
