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What stdout can tell you
Stdout and stderr are useful, but only for specific jobs. They are good evidence of what happened during a run, and poor evidence of whether a behavior was verified.
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- Sequence and activity. Printed tool invocations, status lines, and intermediate messages show the order in which the agent acted.
- Errors for diagnosis. Tracebacks and error messages point to where a run broke, which is often the fastest way to start debugging.
- Run context. Output can identify which run, prompt, or configuration produced a given result, provided the run was labeled when it started.
Google Cloud’s logging documentation lists stdout and stderr as log sources that logging agents collect. That makes them operational records. Logging documentation does not treat them as a test pass condition, and nothing in the stream says that an expected behavior was checked.
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A completed run is not a passing check
Consider an agent that ends its output with “I updated the parser and all tests pass.” That sentence is text the agent produced. It is not evidence that a test command ran, which tests ran, or what they asserted. A real check has three parts: a command or evaluation that executed, an assertion that could fail, and a recorded result with a reference to the run that produced it.
The same gap appears when an agent completes a task but the outcome is wrong. Exit code 0 means the process finished. It says nothing about whether the answer met the requirement, whether a required tool was called, or whether the output stayed inside policy. Each of those needs its own criterion.
How to write the test plan
Write the plan before you read the output. Reading a long transcript first tends to lead you toward whatever the agent claims succeeded. The plan has seven parts.
1. Scope
Name the user-visible behavior or requirement the change is supposed to satisfy. “The agent handles refunds” is too broad. “A refund request over the stated limit is escalated to a human handoff rather than approved” is testable.
2. Scenarios
Cover the ordinary path, the important edge cases, known failure cases, and any tool or handoff paths the change touches. Microsoft’s evaluation guidance recommends realistic, single-intent prompts grounded in real data. A scenario that asks for three unrelated things at once cannot show which part failed.
3. Expected outcomes
State the observable result for each scenario before running it. Write it in terms a reviewer could check without rerunning the agent, such as “the refund tool is not called, and the response contains a handoff to the billing queue.” If you cannot describe the expected result, you do not yet know what the test is for.
4. Assertions
Keep each assertion atomic, binary, and verifiable. Microsoft’s guidance describes assertions in these terms. Assert important public behavior, such as which tool was called, what the final state is, or whether a required field is present. Avoid asserting incidental log wording, because a harmless rephrasing will then fail the test while a real regression that changes no wording will pass.
5. Execution boundary
Label each check by what it exercised. A check that uses a scripted or model double tests the application’s own logic inside that script. A check that needs a real provider, network path, sandbox, or integration environment tests the boundary itself. Mark which of your checks fall into each group, so a passing scripted run is never reported as proof of live model behavior.
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Record the exact command or evaluation run, the case set, the environment and version where they matter, the pass or fail result, and a reference to the trace or log. A transcript or stdout excerpt is not proof that a check ran. The evidence is the executed check and its recorded outcome.
7. Regression loop
When a case fails, keep it. Microsoft describes evaluation as a loop: make a change, run the test set, inspect what improved or regressed, and preserve user-reported failures as new cases. Rerun the same set after each change. Investigate any regression instead of relying on a general impression that the agent seems better.
Match the test to the boundary
The most common mistake is testing the wrong layer. Deterministic scripted tests are well suited to logic your application owns, such as tool execution, handoffs, guardrails, retries, session handling, and normalized streaming. They are not suited to showing how a real model, provider, network protocol, sandbox, or audio system behaves. OpenAI’s Agents SDK testing guide puts the line this way: “Use real provider adapters or integration environments for behavior owned by an external model, network protocol, sandbox provider, or audio system.” A mocked success establishes behavior only within the scripted boundary.
The table below compares the four common approaches on the axes that matter when you decide what a result means.
| Approach | Behavior boundary it exercises | Realism of model, provider, or environment | Repeatability across runs and versions | Evidence it returns |
|---|---|---|---|---|
| Scripted test doubles | Application-owned orchestration: tool execution, handoffs, guardrails, retries, sessions | Low for the model and provider, which the double replaces | High, because the script is fixed | Pass or fail on assertions about the orchestration logic |
| Integration tests against a real provider or environment | External model, network protocol, sandbox, or audio behavior | High, though it depends on the live service at the time of the run | Lower, because live outputs can vary between runs and as services change | Pass or fail on assertions about live behavior, plus logs and traces from that run |
| Traces | The sequence of model calls, tool calls, guardrails, and handoffs in one run | Reflects the run that actually happened | Each trace describes one run only | A record for diagnosis, not a pass or fail verdict on its own |
| Datasets and evaluation runs | Behavior across a fixed set of cases against defined criteria | Depends on the environment used for the run | High across versions when the case set and criteria stay fixed | Scores and case-level results tied to a named run |
Use traces to diagnose and datasets to compare
Traces and datasets answer different questions, and an article or report should not blur them.
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Traces for diagnosis
OpenAI’s guidance is to begin with traces when debugging a workflow. A trace shows the sequence of model calls, tool calls, guardrails, and handoffs, which helps locate where a multi-step run went wrong. A trace of one run supports a narrow conclusion about that run. It does not show how the agent behaves in general.
Datasets for repeatable comparison
Move to datasets and evaluation runs when you need repeatability, prompt comparisons, or evaluation at a larger scale. AWS’s evaluation documentation describes curating representative cases from traces and scoring them with evaluators. A fixed case set makes results comparable across versions. A single score across changing cases does not prove reliability, and a score should always be reported with the case set and criteria that produced it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reporting stdout honestly
A clear report can include a relevant stdout or stderr excerpt, labeled as context. The result itself should be reported separately. Include the following in every report:
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- The exact test command or evaluation run that executed.
- The assertion or criterion that decided pass or fail, and the case it applied to.
- The environment and version details that affect the result, including whether the check used a double or a real provider.
- The pass or fail result, recorded from the run.
- A reference to the trace or log for that specific run.
When an excerpt is quoted, state which run and environment produced it. Output without that context cannot be tied to a result, and it is easy to mistake for one.
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What this guidance does and does not establish
The points above come from current official documentation reviewed in October 2026: OpenAI’s Agents SDK testing and tracing guidance, Microsoft’s evaluation guidance, AWS’s evaluation documentation, and Google Cloud’s logging documentation. These materials may change, so check the current versions before relying on specific wording.
The seven-part plan is an editorial synthesis of that guidance, not a published standard. None of the documentation reviewed quantifies how often agent output misleads reviewers or how much a test plan improves an agent’s reliability, so this article makes no such claim. The guidance supports an engineering process. It does not identify a particular tool or product as the right choice.
Stdout is worth keeping. Keep it as a record of what happened, and keep the test as the record of what was checked.
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