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Review AI-generated code by setting a human-owned boundary before editing, exercising the change against the case that prompted it, checking whether tests can catch a defect, and enforcing repeatable rules with deterministic gates. A green test run or a second model’s approval is useful evidence—not a verdict. The reviewer still has to decide whether the change belongs in the product at all.
Why a plausible diff is not enough
Imagine asking an agent to fix a date parser and receiving an eleven-file diff. The parser change looks reasonable, the tests pass, and the patch also adds caching and refactors unrelated code. That example illustrates the review problem: a change can appear technically polished while exceeding the request or making a product decision nobody asked for.
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Review therefore needs more than a quick scan for syntax errors. You need to ask whether the change stayed within its intended scope, whether it behaves correctly in the motivating case, whether the tests would expose a relevant failure, and whether the change is appropriate for the product.
Set the allowed scope before the agent edits
Write down which paths the task is allowed to change, then instruct the agent to make the smallest change that satisfies the request and to stay within those paths. For example, if the task concerns date parsing, specify the parser implementation and the relevant test file rather than saying only “avoid unrelated changes.”
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If the agent discovers that another file must change, treat that as a scope decision: review the reason and explicitly widen the allowed set before the additional edit. This makes an unexpected path visible as a concrete boundary crossing instead of asking a reviewer to infer what “the intended scope” meant.
Instructions steer a probabilistic agent; they do not enforce the boundary. Use a deterministic check or gate if out-of-scope edits must be blocked.
Review each control for what it can actually establish
| Control | When it acts | What it can check | What still needs judgment |
|---|---|---|---|
| Explicit path instructions | Before editing | Steers the agent toward a human-defined set of files | Whether the proposed scope is appropriate; instructions alone cannot guarantee compliance |
| Runtime checks and tests | During review | Whether exercised behavior and test assertions produce expected results | Whether the scenarios adequately represent the product need |
| Independent review | During review | Can surface candidate issues or missed context in the diff | Whether a finding is real, important, or in scope |
| CI and in-loop gates | Before or during merge, or before a tool write | Repeatable conditions such as tests, types, lint, secrets, and allowed paths | Whether correct code is useful and right for the product |
Exercise the behavior that motivated the change
Run the changed code against the original case that led to the request. For a date-parser fix, use the troublesome date input and check the returned value or error—not just whether the diff seems plausible. A passing suite does not prove the specific behavior is correct if the relevant case is absent or weakly asserted.
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Also consider relevant edge cases and security implications. Microsoft’s VS Code guidance on reviewing AI-generated code likewise recommends reviewing output, running tests, and checking edge cases and security rather than treating generated code as finished work.
Check whether the tests can detect a defect
A green suite tells you that the current code passed the checks that ran. It does not tell you whether those checks would fail if the implementation were wrong. Probe the suite with a controlled mutation: flip a comparison, remove a guard, or delete a branch that should matter. Confirm that an appropriate test fails, then restore the original code.
If the mutation leaves the suite green, inspect the relevant assertions and add or strengthen a test before relying on the suite for that behavior. Mutation-testing tools such as mutmut, Cosmic Ray, and Stryker can automate the introduction of mutations; they do not replace deciding which behavior deserves coverage.
Use independent review as a source of findings, not approval
A person or a fresh model that did not author the change can make a useful first pass over the diff. Ask it to identify concrete risks, unexpected scope, and missing tests. Then verify each finding against the code and the task before acting on it.
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Independence can reduce blind spots tied to the author’s context, but it does not eliminate shared blind spots or decide whether a change is worthwhile. OpenAI’s pull-request review guidance advises reviewing generated findings against the relevant code before relying on them. Treat a clean review as one input, not a guarantee of safety.
Move repeatable checks into deterministic gates
Checks with objective answers should not depend on a reviewer remembering to run them. Make appropriate tests, type checking, linting, secret scanning, branch protection, and scope checks required parts of the pull-request or merge workflow. Keep product questions—such as whether a new cache is justified—with the human reviewer.
Gate agent edits with an allow-list when needed
An in-loop Claude Code PreToolUse hook can inspect Write, Edit, or MultiEdit calls and block a target path outside a human-authored allow-list. In the described example, exit code 2 blocks the call and returns a message to the model.
That control is only as broad as the operations it covers. A hook matching those file-edit tools does not automatically stop shell writes such as sed -i or shell redirection. Shell operations need their own protections if they are within the threat model. A path gate also checks where a change lands, not whether the code inside an allowed file is correct.
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Start a new guard in advisory mode and observe what it would block. Once the allow-list is reliable, promote it to a hard block. An overly broad or incomplete rule can obstruct valid work, while a narrow rule may leave bypasses untouched.
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Keep the product decision with the reviewer
Automation can report test status, type errors, out-of-scope paths, or possible secrets. It cannot determine whether the caching layer was worth adding, whether a rename improves the codebase, or whether the requested fix addresses the underlying product problem. Those choices require context about the product and task.
OpenAI reported that, in its 2025 deployment, 36% of pull requests entirely generated by its cloud coding agent received code-review comments, and 46% of those comments led the author to make a code change. In a broader deployed-review measure, it reported that 52.7% of comments led to a code change. These are figures from one organization’s deployment, not a general measure of review-tool accuracy or a guarantee that reviewed code is safe. OpenAI itself warns against over-reliance on review.
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