Check the seams a multi-file change can break
AI edits are locally plausible and globally risky: they usually do what you asked in the files you were looking at, while quietly violating an invariant elsewhere. Review the seams, especially around data access and trust boundaries.
- Authorization regressions
A refactor of a query or endpoint can drop an ownership check. Re-verify access control after every edit to data-access code.
- Input trust
Confirm validation lives at the server boundary, not just the UI.
- Secret handling
Watch for keys hardcoded or moved into client-reachable code during a refactor.
- Reintroduced N+1 / unbounded queries
A cleanup can undo earlier performance work. Re-check data-fetching patterns.
Make review keep pace with the agent
Manual review cannot match agent-speed generation - that is the core tension. The fix is to automate the deterministic checks (access-control patterns, unsafe queries, exposed secrets, dependency sanity) so human attention goes to genuine judgment calls.
A per-commit automated review turns "I hope someone read this" into a scored, line-level report on every push, which is the only way the safety net keeps up with how fast Windsurf writes.
The pre-launch checklist
- Re-verify authorization after data-access edits
Ownership checks survive refactors.
- Confirm server-side input validation
At every boundary, not just the client.
- Scan for secrets moved into client code
Catch keys relocated during refactors.
- Check for reintroduced N+1 / unbounded queries
Cleanups can quietly undo performance work.
- Diff dependency changes for hallucinated packages
Confirm every added package is real and intended.
- Automate the deterministic checks
Reserve human review for judgment calls.
Run this checklist on your repo, automatically
PeakStack scores every commit for security, scalability, and cost - with the exact line and a fix.
Request accessFAQ
Does Windsurf-generated code need review?
Yes - arguably more than hand-written code, because the volume is higher and the agent has consistent blind spots around authorization, input trust, and data-access performance. Review the seams where a local change breaks a global invariant.
How do I review AI changes without slowing down?
Automate the deterministic checks and spend human attention only on judgment calls. PeakStack runs that automated review on every commit, flagging issues with the exact line and a fix.
What does an agent most often get subtly wrong?
Authorization regressions during refactors - a query or endpoint loses its ownership check while still appearing to work. It is invisible in testing and the most exploited bug class in production.