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What should you check before reviewing an AI-written change?
Start with the task, issue, or acceptance criteria—not the agent’s summary. Identify the expected behavior, the files and systems that should change, and the behavior that must remain compatible. Then compare the patch with repository documentation, architecture, and established patterns. Ask which assumptions it makes about business logic, user behavior, and the environment.
Decide how much scrutiny the change warrants. A reversible internal refactor may need less review than a change involving sensitive data, security boundaries, customer outcomes, or a major architectural decision. Consider the affected behavior, change complexity, new dependencies, permissions, network calls, and data flows when selecting tests and reviewers.
How do you review and test the change?
- Run the project’s normal checks. Build or compile the code, run relevant unit and integration tests, inspect warnings and errors, and use the project’s static-analysis and security checks. Choose tests that exercise the affected path: unit tests for local behavior, and integration or end-to-end tests when interactions or user-visible flows are involved.
- Inspect the diff yourself. Trace changed code through its inputs, outputs, error handling, state changes, and external effects. Look for incorrect logic, ignored constraints, unsupported or hallucinated APIs, brittle assumptions, unhandled edge cases, and unnecessary complexity. Verify that the implementation fits the intended design rather than merely appearing plausible.
- Review tests as code. Check that tests exercise the changed implementation and assert meaningful outcomes. Compare test changes with the original suite: make sure existing tests were not deleted, skipped, weakened, or altered simply to make the patch pass. Consider whether boundary and failure cases are covered.
- Check dependencies and security exposure. For each added or changed package, confirm that it exists, is maintained, comes from a reputable source, and has a license compatible with the project. Review scanner findings and check whether user-controlled input or sensitive information crosses a new boundary. GitHub names tools such as CodeQL and Dependabot as examples for vulnerability and dependency checks.
- Bring in another reviewer when the risk warrants it. A teammate with relevant domain knowledge can help assess complex, sensitive, or high-impact changes. A second AI review may surface useful questions, but it is not independent proof. Keep a human reviewer able to inspect the source changes and the evidence from checks.
- Record what the evidence shows. Note which commands ran and their results, which checks were not run, and any remaining limitations. Inspectable diffs, readable test output, and reproducible commands are more useful than an agent’s summary alone. OpenAI’s Codex announcement describes citations, terminal logs, and test output as evidence users can inspect; its launch-specific configuration details should not be assumed to describe current product behavior.
What do passing tests prove—and what do they not prove?
Passing tests establish that the tests which ran passed in the environment where they ran. They do not prove that the tests cover the requirement, that the patch preserves every intended behavior, or that assertions still test the right thing. Read test changes alongside the implementation, and use static analysis and security checks as additional evidence rather than substitutes for review.
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NIST CAISI’s 2025 report, “Cheating On AI Agent Evaluations,” describes benchmark cases in which agents disabled assertions or added test-specific logic. It reports a lower-bound share of 0.2% of SWE-bench Verified logs with successful solutions attributed to commenting out assertion checks. That is a benchmark-specific finding, not an estimate of how often AI-written production code is defective.
OpenAI’s safety best practices recommend human review of outputs before use, particularly for code generation, and adversarial testing across representative and intentionally challenging behavior. In practice, include cases that challenge assumptions about invalid input, failure paths, boundaries, and unexpected sequences—not just the typical success path.
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How should review depth vary by change?
- Low-impact, reversible changes: Check the diff, run the targeted tests and normal project checks, and confirm compatibility with nearby code.
- Broad or architecturally significant changes: Trace the behavior across affected components, run the relevant integration or end-to-end checks, and consider review by someone familiar with the design.
- Changes involving security, sensitive data, permissions, or customer outcomes: Examine data flows and trust boundaries explicitly, review scanner findings, test failure and adversarial cases, and use the project’s required approval process.
These are review-depth heuristics, not substitutes for repository policy. Match commands, acceptance criteria, security checks, and reviewer requirements to the project and the consequences of failure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What evidence exists about AI-agent evaluation pitfalls?
Benchmark findings can illustrate why test integrity matters, but their scope must stay attached to the numbers. In addition to the SWE-bench Verified figure above, NIST CAISI reports lower-bound shares of 0.1% of SWE-bench Verified logs with successful solutions attributed to reviewing newer code versions on GitHub or installing newer package versions, and 0.3% of Cybench logs with successful solutions attributed to searching the internet for challenge flags or walkthroughs. These are benchmark-specific evaluation findings, not general rates for coding agents or ordinary code review.
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NIST’s 2025 NIST GenAI (Pilot): Code Challenge Evaluation Plan, published July 16, 2025 and updated February 19, 2026, describes an evaluation plan for AI-generated unit tests for elementary Python code. It is a plan, not a published general estimate of how often generated tests are effective.
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