How to Test AI Agents: A Practical Workflow
Design useful AI agent tests, combine active and passive testing, and investigate the evidence behind regressions.
AI agents need more than a health check. A useful test asks whether the agent chose the right path, used its tools safely, and gave an answer that matches the user's situation. The practical approach is to define those expectations before release, run selected scenarios actively, and then evaluate real production traces passively.
Start with the behavior you need to protect
Write the scenario as if you were explaining it to a teammate. Include the trigger, the expected action, and the acceptable answer. For example:
- Scenario: the user asks to cancel a subscription.
- Expected behavior: the agent explains the cancellation path and does not claim that cancellation already happened.
- Execution evidence: the billing tool receives the correct account identifier before the agent confirms an action.
This is more useful than a vague instruction such as “handle the request.” A clear scenario can decide when a passive test is relevant and give a reviewer enough context to understand a failure.
Combine semantic and procedural checks
Semantic checks evaluate meaning. They can check whether a response is grounded in policy, acknowledges an important limitation, or gives the customer a complete next step.
Procedural checks evaluate observable execution facts. Depending on the trace, that may include a tool call, an argument, an HTTP status, a span attribute, or another value your instrumentation emits.
Use both when behavior depends on a chain of decisions. A polished response is not enough if the underlying tool was skipped, and a correct tool call is not enough if the final answer invents an outcome.
Run active tests before release
Active testing is the deliberate loop:
- Choose a scenario that represents a customer goal or a known edge case.
- Send it to the staging or production agent connection.
- Inspect the response, trace, and semantic or procedural results.
- Fix the agent or its orchestration.
- Run the scenario again and keep the test as a regression guard.
Start with a small set of high-value scenarios. Include a normal request, an ambiguous request, and a case where an unsafe assumption would create a bad outcome. Expand the suite when a production trace reveals a new failure mode.
Continue with passive production evaluation
Synthetic scenarios cannot predict every way a person will use an agent. Passive testing closes that gap: your system sends the trace created by a real interaction, and the same active test cases evaluate it in the background.
Use relevance carefully. A test about cancellation should not fail on a conversation about opening hours. Mibo's passive behavior settings distinguish tests that always run, tests that run when relevant, and tests reserved for manual active runs. When a test does not apply, skipping it is better evidence than forcing a failure.
Learn more about production testing and the n8n integration if your production traces come from a workflow tool.
Investigate the evidence, not only the score
A score is a useful summary, not a diagnosis. When a test fails, ask:
- Did the agent route the request to the correct specialist?
- Did the tool call happen, and did it carry the expected values?
- Did the response claim more than the trace proves?
- Is the check missing instrumentation rather than detecting a behavior failure?
- Can the same scenario reproduce the regression in an active run?
This workflow turns a failed result into a concrete engineering task. It also keeps the test suite tied to product behavior rather than to a particular framework or prompt implementation.
Know the limits of agent tests
No test suite captures every possible interaction. Semantic evaluation depends on a clearly written expectation and an appropriately governed trace. Procedural evaluation depends on the attributes your system actually emits. Scores can help compare changes, but they should be interpreted alongside the scenario, evidence, and failure reason.
For the product workflow, see AI agent evaluation in Mibo and the Mibo documentation.