AI agent code review
AI Agent Code Review Checklist Before Merge
AI coding agents can produce useful patches quickly. They can also quietly touch risky areas that deserve a slower human review.
The dangerous failure mode is rarely “the generated code is obviously broken.” More often, the patch looks reasonable but includes a dependency change, auth/config edit, missing tests, or a secret-like literal buried inside a larger diff.
Use this checklist before merging patches from Cursor, Claude Code, Codex, Copilot agents, or any other AI coding workflow.
The 6-point checklist
- Dependencies: Did package files, lockfiles, or build manifests change?
- Sensitive paths: Did auth, payment, security, token, session, or config files change?
- Tests: Did source files change without matching test changes?
- Generated files: Did a bundled or generated file change in a way that hides review context?
- Secrets: Are there API keys, tokens, passwords, webhook secrets, or suspicious literals in the diff?
- Evidence: Is every “tests passed” claim backed by real command output?
Turn it into a local gate
You can start manually with the checklist. The next step is making it part of the review process:
git diff > change.patch
python src/agent_change_risk_auditor.py audit --diff change.patchThe AI Agent Change Risk Audit Kit packages this as a local Python CLI, JSON output, CI gate, and review workflow.
When to block a merge
- Secret-like literals appear in the patch.
- Auth/payment/security files changed without tests.
- Dependencies changed without lockfile/review explanation.
- Generated files dominate the diff.
When to slow down
- The patch touches Docker, CI, deploy, or config.
- The AI agent claims tests passed but no output is attached.
- Large refactors mix behavior changes and formatting changes.
Get the local kit
Local-first workflow. No uploading private diffs to a third-party service.
Also read the public DEV.to write-up: I built a local risk gate for AI-agent code changes.