Reviewing AI-Generated Patches: My Checklist Before Anything Hits Main

AI-generated patches are often good enough to look finished before they are actually safe. That is why my review process is stricter, not looser, when an agent produced the first draft. I am not trying to catch the model making obvious syntax mistakes. I am trying to catch the more expensive problems: vague scope, accidental side effects, and changes that pass locally without being truly understood.

First Question: Was the Task Small Enough?

The first thing I check is not the code. It is the size of the assignment. If the patch touches too many concerns at once, I already know review quality will drop. Large agent patches create a false sense of speed because they compress several decisions into one diff.

When the scope is too broad, I split the work before I even comment on style. A smaller patch is easier to test, easier to reason about, and much easier to roll back if something subtle breaks later.

A Small Diff Is Easier to Trust

If the bug is a quantity bug in a cart, I would rather review a change like this:

diff
- const total = items.reduce((sum, item) => sum + item.price, 0)
+ const total = items.reduce((sum, item) => sum + item.price * item.quantity, 0)

That kind of patch is easy to reason about. A reviewer can immediately ask the right questions: are quantities always present, do tests cover zero values, and is the same logic duplicated elsewhere?

Read the Diff for Intent, Not Just Correctness

A human reviewer has to reconstruct why each edit exists. I want every changed block to map back to a clear reason. If I cannot explain why a line moved, why a helper was introduced, or why a dependency changed, the patch is not ready.

This is where AI often reveals itself. It tends to add cleanup edits that look harmless but are unrelated to the problem. Those edits increase surface area without increasing value, so I cut them aggressively.

Confirm the Verification Is Real

I look for proof that the code was exercised in the way the task required. A passing build is not enough if the change was in rendering logic. A screenshot is not enough if the change affects data consistency. The verification method has to match the risk.

These are the commands I typically run before approving a patch that touched a user-facing workflow:

bash
git diff --stat
git diff -- app/cart
pnpm test cart-summary -- --runInBand
pnpm lint app/cart

My rule is simple: the more user-facing the change, the more concrete the evidence needs to be. That can mean tests, logs, screenshots, or step-by-step reproduction notes. What matters is that the reviewer does not need to guess whether the behavior actually changed.

Watch for Pattern Mismatch

One of the most common failure modes is the model following a plausible pattern that the repository no longer uses. It may introduce an older hook pattern, a deprecated utility, or a style that looks correct but conflicts with current conventions. That kind of mismatch is easy to miss if you only review the changed lines in isolation.

I compare the patch against nearby code that was written recently by the team. If the new code feels like it came from a different generation of the codebase, I slow down and inspect it much more carefully.

Make Rollback Easy

Even strong AI-assisted changes should be easy to revert. I prefer patches that keep responsibilities separated and avoid bundling refactors with behavior changes. If a patch is difficult to unwind, it is usually doing too much.

Rollback is not a pessimistic mindset. It is a quality signal. Clean reversibility usually means the change is well scoped and well understood.

The Checklist I Actually Use

Before anything reaches main, I check five things: the patch is tightly scoped, every edit has a clear reason, verification matches the risk, repository patterns are respected, and rollback would be straightforward. If any one of those is weak, the patch still needs work.

I often reduce that to a compact checklist while reviewing:

md
[ ] Only the necessary files changed
[ ] Every edit maps to the bug or requirement
[ ] Verification matches the user-facing risk
[ ] Current repository patterns are preserved
[ ] Rollback is straightforward

AI is useful because it can accelerate implementation. It is not useful as a substitute for engineering judgment. The teams that benefit most are the ones with a high bar for review, not a lower one.