A Checklist For The New Default Workflow
Reviewing an AI-generated PR is not the same as reviewing a human-generated one. The mistakes are different. The signals are different. The reviewer's mental model has to adjust.
This is the checklist we hand to engineers onboarding to a team that uses AI coding agents. Ten questions, in order, that distinguish a careful review from a rubber stamp.
1. Does The PR Description Tell A Coherent Story?
The first thing to read is the description. A good AI description names the bug or feature, identifies the root cause or design choice, and explains the approach. A bad description is generic boilerplate.
If the description is bad, you have a signal: the AI did not fully understand the change. Read the diff with extra suspicion. See PR description quality.
2. Is The Diff Scope What The Ticket Asked For?
Compare what the ticket asked for with what the diff actually changes. AI agents sometimes scope-creep, they fix a problem and also touch adjacent code that was not in scope. Sometimes this is helpful. Sometimes it is dangerous expansion that should have been a separate PR.
If the diff is bigger than the ticket warranted, ask why. Either contract the diff or split into multiple PRs.
3. Are All Callers Of The Changed Function Updated?
This is the single highest-yield check on AI PRs. If the AI changed the contract of a function (added a parameter, changed a return type, changed exception behavior), did it update every caller? Search the codebase for the function name. Compare against the diff.
If callers are missed, the PR breaks those call sites silently. AI agents miss this more often than humans do.
4. Are New Code Paths Tested?
If the diff adds branches, exception paths, or new return-with-error paths, look at the test diff. Are the new paths covered? If not, the AI added behavior that the test suite does not exercise.
A reviewer reflex: when in doubt, ask the AI to generate the missing test. If the test would not pass on the new code, the new code has a bug.
5. Does The Style Match The Surrounding Code?
AI agents sometimes deviate from the local style, use different naming conventions, different import patterns, different abstraction levels. The deviation is rarely a bug but is friction for future readers.
If the style is off, ask the AI to conform. The tooling supports per-repo style profiles; if the deviation is recurring, it is worth tuning. See onboarding AI to a 10-year-old codebase.
6. Are There Any Suspicious "Helpful" Additions?
Look for code the AI added that was not strictly needed: comments explaining what the code obviously does, defensive null checks where the input cannot be null, retry loops on operations that do not need them, expanded error handling for cases that cannot occur.
These additions look helpful but indicate the AI was not confident. If they are present, the AI's underlying understanding was probably weaker than its output suggests. Push back on the additions and look harder at the rest of the diff.
7. Does The Change Cross Layer Boundaries?
Business logic in the controller layer. Data access in the service layer. UI logic in the model. AI agents do not always respect architectural boundaries, especially in larger codebases.
If the change feels like it is in the wrong place, it probably is. Move it to the right layer or ask the AI to do so.
8. Are There Hardcoded Values That Should Be Configurable?
A new environment-dependent value (URL, timeout, retry count) hardcoded in the diff is a flag. The AI sometimes inlines values that should come from configuration. Ask whether the value should live in a config file, an environment variable, or a feature flag.
9. Could This Change Affect Performance In A Hot Path?
Read the diff with one specific question: is this in a code path that runs many times? If yes, look specifically for database queries inside loops, synchronous calls in async code, full-table scans, and missing indexes. AI agents introduce performance regressions that the local diff does not surface.
See AI code review heuristics.
10. Would I Approve This PR If A Junior Engineer Wrote It?
The final calibration question. If a junior engineer on your team had submitted this PR, would you approve it as-is, or would you ask for changes? Apply the same standard to the AI.
If you would not approve a junior engineer's version, do not approve the AI's version. The standard is the work, not the author.
What Not To Do
Three reviewer behaviors that produce bad outcomes:
Approving on tests-passing alone. Tests passing means tests passed. It does not mean the code is correct. Read the diff.
Treating the AI as authoritative. The AI is a competent peer, not a senior. Apply the same skepticism you would to any peer's PR.
Dismissing the AI's reasoning. The flip side: the AI sometimes has reasoning the reviewer initially misses. Read the description and any inline comments. If the AI's justification is right and your initial reaction was wrong, update.
How Long Should This Take
For a small PR (under 100 lines), the checklist should take 5-10 minutes. For a medium PR (100-300 lines), 15-25 minutes. For a large PR (over 300 lines), the answer is "split it first," not "spend two hours reviewing." See diff size limits.
If you find yourself spending an hour on an AI PR, the AI generated something too complex or the description was bad enough to require detective work. Either is a signal to send it back.
The Reviewer's Authority
One final note. The reviewer is the final decider on whether an AI PR merges. Not the AI's confidence score. Not the validator's findings. Not the manager's pressure to move faster.
The system is designed for the reviewer to be the last line. Take the authority seriously. Use the checklist. Push back when something is off. The team's quality is in your hands, not the AI's.
For how the team measures reviewer outcomes, see 12 metrics for AI coding agent success. For the broader playbook, see engineering manager rollout playbook.
Frequently asked questions
How do you review AI-generated pull requests?
Work a fixed checklist rather than reviewing on instinct, because AI mistakes differ from human ones. Start with whether the PR description tells a coherent story, then confirm the diff scope matches the ticket, verify every caller of a changed function is updated, check that new code paths are tested, and confirm style matches the surrounding code. Finish with the calibration question: would you approve this if a junior engineer wrote it? For heuristics that catch subtler issues, see AI code review heuristics that catch what humans miss.
What should you check first in an AI-generated PR?
The highest-yield check is caller coverage: if the AI changed a function's contract (a new parameter, a different return type, altered exception behavior), search the codebase for the function name and confirm every caller was updated. Agents miss this more often than humans do, and a missed caller breaks that call site silently. Also watch for scope creep beyond the ticket and 'helpful' defensive additions that signal the AI wasn't confident.
Is it safe to approve an AI PR if the tests pass?
No. Tests passing means the tests passed (not that the code is correct), so you still have to read the diff. Approving on tests alone, treating the AI as authoritative, and rubber-stamping are the three reviewer behaviors that produce bad outcomes. The AI is a competent peer, not a senior; apply the same skepticism you'd give any peer's PR. Pairing size discipline with review helps, see diff size limits.
How long should it take to review an AI-generated PR?
For a small PR under 100 lines, the ten-question checklist should take 5-10 minutes; a medium 100-300 line PR runs 15-25 minutes. For anything over 300 lines the right answer is 'split it first,' not spend two hours reviewing. If you find yourself spending an hour on an AI PR, the change is too complex or the description was bad enough to require detective work, either way, send it back.
What mistakes do AI coding agents make in pull requests?
Common ones: missing callers after a contract change, scope creep into adjacent code, style that deviates from the local dialect, crossing architectural layer boundaries, hardcoding environment-dependent values, and introducing performance regressions like database queries inside loops that the local diff doesn't surface. Agents also add 'helpful' code (needless null checks, retry loops, over-explained comments), that signals low underlying confidence. A generic or boilerplate PR description is itself a warning sign; see PR description quality as a leading indicator.
EnsureFix Engineering Team
The EnsureFix engineering team designs and operates the multi-agent pipeline that turns tickets into production-ready pull requests. They write about architecture, model routing, safety validation, and what actually ships in enterprise codebases.