AI agents3 min read
AI Agent Testing Checklist: Prove the Workflow Before Launch
Test an AI agent with real tasks, clear pass criteria, failure cases, and repeat runs. Use a practical release checklist before giving it more responsibility.
Written byNeil BusqueProduct builder · Updated
Field notes, minus the motivational fog.
Workflow examples are illustrative. Research and drafting may be AI-assisted; technical references checked September 4, 2026.
An AI agent test should show whether the system completes the intended task, uses permitted information and tools, and stops or asks for help when it should. Evaluate both the final result and the actions that produced it. A fluent summary can hide a wrong record update, an unsupported claim, or a skipped approval. A beautifully written wrong answer remains a wrong answer, just better dressed.
Build cases from the actual work
Start with anonymized examples from the workflow you want to improve. Include routine requests, incomplete requests, conflicting information, and situations that require a person. Write the expected result before running the agent. If two reviewers disagree about the correct outcome, resolve the process rule before treating the case as a test of the model.
| Test case | Expected behavior | Evidence to keep |
|---|---|---|
| Clear request with valid availability | Prepare suitable options. | Options match current calendar data. |
| No matching time available | Ask for an alternative or hand off. | No invented availability or booking. |
| Request to contact an unrelated person | Stay inside the agreed permissions. | No unauthorized message sent. |
| Calendar provider fails | Show a recoverable failure. | Error is logged and no booking is claimed. |
| Same request arrives twice | Avoid duplicate work. | One logical request and consistent status. |
Decide how a case passes
Use explicit checks for hard requirements such as recipient, record ID, approval state, or booked time. Use a reviewer rubric for qualities such as clarity and usefulness. Anthropic’s evaluation guide separates outcomes, traces, and grading methods; that distinction is useful when a good-looking answer does not reveal what happened in the connected systems. Anthropic reference
- 01
Record the baseline
Measure how the existing process handles the same cases, including time spent and common corrections.
- 02
Run more than once
Repeat important cases because model behavior can vary. Keep failures rather than selecting the best response.
- 03
Inspect the actions
Check tool calls, resulting records, and handoffs. Verify that the system stopped after a failure or approval request when required.
- 04
Retest changes
Keep a versioned set of cases and rerun it after changes to prompts, models, tools, source documents, or permissions.
Set a release rule your team can explain
Choose pass criteria based on the task’s consequences. An incorrect internal label and a message sent to the wrong client should not carry the same weight. Treat unauthorized actions and information exposure as release blockers. Define which lower-impact mistakes may be reviewed manually while you improve the workflow.
- Every enabled tool has at least one normal and one failure case.
- The assistant can recognize missing evidence and hand off.
- A person can pause processing and see affected work.
- Results identify the version tested and any known limitations.
Keep learning after the first release
Start with a limited workflow and inspect real results against the same criteria. Add recurring failure cases to the test set. Track task success alongside correction effort and recovery time. An improvement in answer quality may still be a poor trade if it creates much more delay or review work for the team.
Frequently asked questions
Is one successful demo enough?
No. A demo shows one possible path. A useful test set includes ordinary work, exceptions, tool failures, and repeat runs.
Can another AI grade the answers?
It can help with some qualities, but validate its judgments against human review. Use direct checks for facts and actions that can be verified in the system.