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Review an AI agent’s code by checking the requested outcome, the actual patch, the behavior it affects, and the evidence from tests and other checks—in that order. Treat the agent’s summary and review comments as claims to verify, not proof that the change is correct. A named human must own the decision to approve and merge it.
Start with the request, not the diff
First establish what change you are reviewing: confirm the repository, pull request title, author, and branch. Read the description to understand the intended outcome. The summary can help orient you, but it does not establish that the implementation meets the request.
Turn broad requests into specific questions that you can check against the code. For example: “How does this change affect sign-in?” or “Does the new error path release the database connection?” OpenAI’s Codex review guidance also suggests asking to see the code supporting a finding and comparing a revision with earlier review feedback to identify unresolved points. OpenAI’s pull-request review documentation describes these kinds of focused questions.
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Inspect the changed files and relevant lines directly. Then follow consequential edits into the surrounding code: a small change to a shared function, permission check, or error handler can affect behavior well beyond the line shown in the diff. Check whether each change serves the requested outcome and whether unrelated files or departures from project conventions have slipped in.
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Do not accept an AI-generated finding merely because it sounds specific. Locate the relevant code and verify the claim there. If the finding does not show its evidence, ask for the code that supports it; then inspect that code yourself.
Prioritize the parts where mistakes matter most
Set review depth according to the change’s risk and complexity. Spend particular attention on security-sensitive behavior, complex logic, and changes that affect multiple services or components. Use the project’s secure-coding standards and normal review process to assess what needs closer analysis.
- Authorization and access control: Check who can perform the affected action and whether existing boundaries still hold.
- Inputs and outputs: Look for validation, unsafe handling, or unintended exposure of data.
- Errors and cleanup: Trace failure paths as well as the successful path. Verify that resources are released and errors are handled consistently.
- Data and dependencies: Check whether the patch changes data handling or introduces dependency risks.
- Scope and conventions: Identify unrelated edits and compare the implementation with repository standards.
- Tests: Determine whether tests exercise meaningful behavior and relevant failure cases, rather than only confirming that a narrow happy path passes.
NIST’s July 2024 Secure Software Development Practices for Generative AI and Dual-Use Foundation Models recommends code review and/or code analysis against organizational secure-coding standards, with discovered issues triaged and addressed. It notes that automation can reduce the effort and resources needed to detect vulnerabilities, but does not quantify the effectiveness of this review workflow.
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Check execution evidence and the latest revision
Review the available tests and other checks, and look for unresolved merge conflicts. A green result is useful evidence, not a substitute for checking whether the tests cover the behavior that changed. If the agent proposes a fix during review, inspect the new diff and the results for the revised code before commenting, committing, or merging.
Make sure the evidence corresponds to the current revision. GitHub’s documentation notes that a new review may need to be requested after new commits unless the repository is configured to review new pushes. GitHub’s Copilot code review documentation also describes repository-level review instructions and different review effort settings. Feature availability and configuration can change; consult the current documentation for details.
Use automated review as a second pass
Automated review can surface leads, but its depth and focus depend on the tool and settings. GitHub describes “Lite” effort as targeted feedback on glaring issues such as bugs, security vulnerabilities, and style inconsistencies, and “Balanced” effort as deeper analysis for complex logic, security-sensitive code, and cross-service changes. Repository-specific instructions can provide further context.
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These options can help direct attention; they do not transfer responsibility. Verify each consequential finding against the current patch and surrounding code, and check the applicable tests and other review evidence yourself.
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Review actions separately when the agent can do more than propose code
If an agent can invoke tools or take actions, review the proposed action as well as the code. Check its target, action, tool arguments, identity, and approved scope. OpenAI’s guardrails guidance recommends denying out-of-scope hosts and attempts involving credential theft, persistence, data exfiltration, destructive changes, production access, or policy bypass. Pause for human approval when an action is ambiguous or high risk.
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Do not assume a review feature in one product applies to an application built with another framework: OpenAI states that applications built with the Responses API or Agents SDK do not inherit Codex Auto-review automatically.
Keep approval with a responsible human
Close the review only when a responsible developer has checked the evidence, addressed or explicitly triaged remaining issues, and made the approval decision through the project’s established workflow. OWASP’s Secure Coding with AI Cheat Sheet states, “AI tools do not accept responsibility for the code they generate.” It adds that responsibility rests with the developer who accepts and commits the code.
NIST’s DevSecOps reference model likewise says AI-generated corrective actions should not change software, configurations, or system state without review and approval through established processes. For the reviewer, the practical rule is straightforward: an agent can propose a change or a finding, but a human remains accountable for accepting it.
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