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Review generated code in three layers: define which files the agent may change, verify the behavior and whether tests would catch a defect, then enforce repeatable rules with deterministic checks. A clean diff or green test run is evidence to examine—not a verdict. The human reviewer still decides whether the change is appropriate for the product and the task.
Why a plausible diff still needs review
Imagine asking for a small date-parser fix and receiving an eleven-file diff that also refactors unrelated code and adds caching. The tests pass, but that does not establish that the extra work was needed, that the original bug is fixed, or that the tests would catch a regression. The example illustrates a review problem; it is not a measured result.
Generated code can contain bugs, security issues, or subtle logic errors, Microsoft cautions in its VS Code guidance. Treat the output as a proposed change whose scope, behavior, and constraints must be checked.
The Tool Desk
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Write down the paths the task is allowed to change before the agent starts. Ask for the smallest change that satisfies the request, and state that any necessary work outside the list requires deliberate scope approval first. Specific paths give both the agent and reviewer a concrete boundary; a phrase such as “stay within the intended scope” leaves that boundary open to interpretation.
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This is steering, not enforcement. An agent may still make an out-of-scope edit, so compare the final changed paths against the approved list. For stronger protection, use a gate that blocks unauthorized paths, described below.
2. Check behavior and test strength
Run the motivating case
Exercise the changed code with the original input or scenario that prompted the request. For a date-parser fix, use the date string that previously failed and confirm the observed result is the intended one. A diff can look convincing while behaving differently at runtime; the relevant question is what the program actually does.
Ask whether the tests can detect a fault
A green suite shows that the current code passed the tests that ran. It does not show that those tests would fail if the change were wrong. Introduce a controlled fault—such as flipping a comparison, removing a guard, or deleting a branch—and confirm the relevant test fails. Revert the mutation afterward.
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Mutation-testing tools including mutmut, Cosmic Ray, and Stryker can automate this kind of test-sensitivity check. A surviving mutation is a signal to investigate whether the test suite misses a meaningful behavior; it is not, by itself, proof that the entire suite is inadequate.
Use independent review as input, not authority
A person or a fresh model that did not author the change can make a useful first pass for candidate issues. Check each finding against the relevant code and context before acting on it. OpenAI’s Codex review guidance likewise says to review generated findings against the code. A second reviewer may notice context-specific issues, but it can share model-wide blind spots and cannot decide whether the scope is right for your product.
3. Put objective rules behind deterministic gates
Automate checks with answers that can be evaluated consistently, and make the appropriate checks required for pull requests or merges. Tests, type checking, linting, secret scanning, branch protection, changed-path checks, and—where suitable—mutation checks can catch repeatable failures without asking a reviewer to remember every rule.
| Control | When it acts | What it can check | What still needs judgment |
|---|---|---|---|
| Explicit path instructions | Before editing | Steers the agent toward named files; a reviewer can compare the diff with the boundary | Whether the approved boundary is appropriate |
| In-loop path hook | Before a covered edit call | Blocks writes to paths outside an allow-list for the tools it intercepts | Whether code within an allowed file is correct |
| CI and merge gates | Pull-request or merge time | Runs configured tests, types, lint, security, and other objective checks | Whether the implementation solves the right product problem |
| Runtime and mutation checks | During review or testing | Exercises behavior and tests whether selected faults are detected | Whether the chosen cases and mutations represent important risks |
| Human review | During review | Evaluates scope, design, trade-offs, and product fit | Requires context; it is not a deterministic gate |
Example: block edits outside an allow-list
For Claude Code, a PreToolUse hook can inspect edit calls such as Write, Edit, and MultiEdit, compare the target path with a human-authored allow-list, and return exit code 2 to block a disallowed call and send a message to the model. See the Claude Code hooks documentation for hook configuration details.
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That example protects only the calls it checks. Shell writes—for example, using sed -i or shell redirection—can bypass a hook that only matches those edit tools unless shell operations are also guarded. And a path gate says where a change may land, not whether its contents are sound.
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Roll out a new guard in advisory mode first: record what it would block and refine the allow-list. Promote it to a hard block once the boundary is reliable. An overbroad rule can obstruct valid work, while an incomplete rule can leave a bypass.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What automation cannot decide
Checks can report whether tests passed, types are valid, lint rules were met, secrets were detected, or a path was permitted. They cannot determine whether adding a cache was worthwhile, whether a rename makes the codebase clearer, or whether the requested fix addresses the underlying product problem. Those decisions need a reviewer who understands the task and its context.
In a 2025 report, OpenAI said 36% of pull requests entirely generated by Codex cloud received Codex code-review comments, and 46% of those comments led authors to make a code change. The report’s broader deployed-review measure found that 52.7% of comments led to a change. These are figures from OpenAI’s own deployment, not general rates for review tools or teams; the report also warns that a clean review is not a guarantee of safety. See OpenAI’s report on verifying code at scale.
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Quick Recap
A repeatable review sequence
- Before editing: name the allowed paths and require the smallest change that meets the request; require approval before expanding scope.
- Inspect the diff: compare changed paths with the boundary, then examine unrelated refactors, new dependencies, caching, and other additions for a clear reason tied to the task.
- Exercise the reported case: run the changed code with the original failing input or scenario and inspect the actual result.
- Check the tests: run the relevant suite and, for important behavior, introduce a controlled mutation to verify that a test fails.
- Run deterministic gates: check required tests, types, lint, secrets, path constraints, and other configured merge rules.
- Evaluate the product decision: decide whether the implementation is the right fix and whether its scope and trade-offs make sense, regardless of whether automation is green.
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