Treat an AI coding agent’s diagnosis as a hypothesis, not a verdict. Check it against the intended behavior, repository context, relevant code and a focused reproduction or test. If the evidence contradicts the finding, show the agent that evidence and ask for a narrow reassessment—then review the resulting diff yourself before merging.
Why an agent’s diagnosis needs verification
An AI code-review finding can sound specific while still misunderstanding the code, describing a problem that is not present, or recommending a fix that violates the project’s requirements. GitHub’s responsible-use guidance includes nonexistent problems and misunderstandings of code among examples of hallucinations in review. A plausible explanation is a reason to investigate, not proof that the bug exists.
Start by establishing what the software is supposed to do. A technically plausible change may still solve the wrong problem if it conflicts with the request, documented behavior or established project conventions. GitHub’s review guidance recommends checking whether generated code meets the task and follows project patterns.
How to verify the diagnosis
- Restate the expected behavior. Compare the agent’s claim with the original request, README, project documentation, relevant conventions and recent changes. Identify the specific behavior that should occur and the behavior the agent says is broken.
- Turn the finding into a testable claim. Ask what code path, input or condition causes the alleged failure. Inspect the relevant files and lines yourself; OpenAI’s Codex review guidance suggests asking, “Show me the code that supports this finding.”
- Try to reproduce the failure. Use a focused test or, where practical, exercise the relevant user-facing route—such as an HTTP endpoint, CLI command, message handler or file interface. OpenAI’s validation guidance recommends concrete criteria and bounded checks. When feasible, runtime behavior or test output is stronger evidence than code interpretation alone. If you cannot reproduce the issue, record what you tried and what remains unverified; an inconclusive check is not proof that the agent is wrong.
- Inspect the proposed change and its tests. Check whether the change addresses the stated behavior and fits the codebase. Look for hallucinated APIs or dependencies, ignored constraints, incorrect logic, and tests that were deleted, skipped or weakened instead of fixed. GitHub’s review guide specifically calls out these risks.
- Share counter-evidence and request a narrow reassessment. Give the agent the relevant code or documentation, reproduction steps and test output. Ask which assumption led to its conclusion and whether the finding still holds in light of that evidence. Keep the requested follow-up bounded to the disputed behavior rather than inviting an unrelated rewrite.
- Review again before merging. Examine the updated diff, test and check results, unresolved comments and conflicts. OpenAI’s guidance says to review findings against the relevant code and review the result before submitting comments, committing or merging. Do not approve a change on the strength of an agent’s summary alone.
Choose the right level of checking
The best check depends on how directly you can observe the behavior, how much code the claim touches and what happens if the diagnosis is wrong. These considerations help decide whether a quick local check is enough or whether the issue needs broader review.
#1 Best Overall
| Situation | Useful check | Why it matters |
|---|---|---|
| The claim concerns a bounded code path and has a clear expected result | Run a focused test or a realistic reproduction through the relevant interface | Direct behavioral evidence can confirm or challenge the claimed failure. |
| The issue is hard to reproduce, but the relevant code and project intent are clear | Trace the path through the code and documentation; state what remains unverified | Inspection can expose a faulty assumption, but should not be presented as runtime proof. |
| The proposed fix changes tests or touches a broad area | Review the full diff, test changes and related checks | A passing result may be misleading if the test was removed, skipped or weakened. |
| The disagreement involves security, sensitive data, business rules or an external interface | Get a teammate or domain expert to review the evidence and change | These cases can require human judgment about impact or intended design beyond what a code explanation establishes. |
This is a decision aid, not a guarantee that any single test settles a disagreement. OpenAI’s validation guidance and GitHub’s review recommendations both emphasize grounding checks in concrete criteria and reviewing consequential changes carefully.
What to say when asking the agent to reassess
Be precise about the claim and the evidence. For example:
“You said this branch drops requests when the input is empty. The handler at
src/handlernormalizes empty input before this call, and this reproduction returns the expected response. Please reassess only this finding against that path. If you still believe it fails, identify the condition and a reproduction that demonstrates it.”
Replace the example paths and behavior with details from your own project. The useful pattern is to supply verifiable context, ask for the assumption behind the diagnosis and define a limited scope for the follow-up. Do not treat a revised explanation as confirmation; check the evidence it cites.
When to involve another developer
Bring in a teammate or domain expert when the dispute could affect security, sensitive data, business rules or a public interface, or when the intended behavior cannot be established from the repository. A second reviewer can assess both the technical evidence and whether the proposed behavior is acceptable. GitHub recommends collaborative review and checking functionality, security and maintainability.
A 2026 arXiv preprint reports a dataset of 54,791 agent-generated code-review comments across 342 Python repositories; its abstract describes comments from five widely used agents. Incorrect suggestions were among the reasons some comments remained unresolved. These are counts from selected repositories, not an error rate and not an estimate of the chance that a particular diagnosis is wrong. The paper’s page is at arXiv:2607.21997; its publication status should be checked before treating it as peer reviewed.
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