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Treat the agent’s diagnosis as a hypothesis, not a verdict. Check it against the intended behavior and repository context, look for evidence in the relevant code, and reproduce the alleged failure with a focused test or realistic workflow when possible. Only then decide whether the diagnosis—or a proposed fix—holds up.
Why an AI coding agent’s diagnosis needs verification
An agent can produce a convincing explanation while misunderstanding the code, overlooking a project constraint, or reporting a problem that is not present. GitHub’s responsible-use guidance includes feedback about nonexistent problems and misunderstandings of code within its description of hallucination in code review (GitHub Copilot responsible-use guidance). A plausible explanation is a lead to investigate, not proof.
There is also a second possibility: the diagnosis may identify a real issue but recommend a change that solves the wrong problem. A fix can be logically sound in isolation and still conflict with the feature request, documented behavior, or conventions the project relies on. Start by establishing what the software is supposed to do, not by editing the line the agent flagged.
How to verify an AI coding agent’s diagnosis
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Restate the intended behavior
Write down the expected behavior in concrete terms. Compare it with the request, README, project documentation, relevant conventions, and recent changes. GitHub recommends checking whether generated code solves the right problem and follows project patterns, and using project materials to give AI better context (GitHub’s review guide).
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Turn the diagnosis into testable claims
Separate a broad statement such as “this code mishandles retries” into claims you can check: which input triggers the behavior, which branch handles it, and what result is incorrect? Ask the agent, “Show me the code that supports this finding.” Then inspect the cited files and lines yourself rather than relying on the summary. OpenAI’s Codex review guidance likewise says to review generated findings against relevant code before relying on them (OpenAI Help Center).
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Reproduce the alleged problem
Use the narrowest meaningful check: a focused test, a small reproduction, or a realistic user-facing route such as the relevant HTTP request, CLI command, message, or file input. A passing test does not prove every behavior is correct, but a reproducible failure is stronger evidence than an unsupported explanation. OpenAI’s validation guidance prioritizes runtime and test evidence over code understanding alone when those checks are feasible (OpenAI validation guidance).
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If a check fails, record the command or scenario and the result. If it cannot be run or is inconclusive, say what remains unverified; do not treat “not reproduced” as proof that the issue is impossible.
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Inspect the proposed change and test changes
Review the diff, not just the agent’s description of it. Check whether it changes the intended behavior, fits the project’s design, and avoids invented APIs or dependencies, ignored constraints, and faulty logic. Review tests with the same care as production code: deleting, skipping, or weakening a failing test can conceal the problem instead of fixing it. GitHub explicitly recommends looking for these kinds of issues in AI-generated code (GitHub’s review guide).
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Give the agent counter-evidence and request a narrow reassessment
Provide the relevant code or documentation, your reproduction steps, and the test output. Ask which assumption led to the diagnosis and request a reassessment limited to the disputed behavior. For example: “This route returns the expected status for the input in question, as shown by this test. Reassess your claim using this route and the documented behavior; identify any remaining case that fails.” Specific evidence and scope give the agent a useful basis for reconsideration without asking it to defend its first answer.
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Review the updated result before merging
Check the revised diff, tests and other checks, unresolved review comments, and any conflicts. OpenAI’s guidance says to review results before submitting comments, committing changes, or merging. For a complex or sensitive disagreement, ask a teammate or domain expert to review it; GitHub recommends collaborative review with attention to functionality, security, and maintainability (OpenAI Help Center; GitHub’s review guide).
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How strong is the evidence?
Choose a check based on how directly it tests the claim, how much code it covers, and what could go wrong if the decision is mistaken. A focused test or realistic reproduction usually provides stronger evidence about a particular behavior than reading code alone; code inspection remains useful for understanding paths the test does not cover. OpenAI’s validation guidance and GitHub’s review guidance support this evidence-led approach, but do not provide a product ranking or a universal test for every diagnosis.
- Directness: Can you observe the alleged failure, or is the conclusion inferred from code alone?
- Scope: Does the check cover the touched code and relevant behavior, or only one narrow path?
- Consequence: Could the disputed change affect security, sensitive data, business rules, or an external interface? If so, seek stronger validation and human review.
What the available study does—and does not—show
A 2026 arXiv preprint reports a dataset of 54,791 agent-generated code-review comments across 342 Python repositories, with comments from five widely used agents. It documents incorrect suggestions among common reasons comments remain unresolved (arXiv preprint). These are dataset counts, not an error rate: the study does not establish the odds that a particular diagnosis—or all coding-agent diagnoses—is wrong. Its data is observational and drawn from selected Python repositories, so it should not be generalized to every language, repository, or agent.
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