How to Audit an Agent Pull Request for Unrelated Changes

Once you have the list, give each file exactly one label: in-scope, adjacent, or unrelated. This single step does more to keep an agent PR reviewable than any amount of careful reading, because it tells you how much attention each file deserves.

The category that matters most is unrelated. Agents often edit files the task never mentioned, and each of those should be treated as an out-of-scope edit until someone gives a reason. The OWASP secure coding with AI cheat sheet calls missing these edits the most common review failure mode in agentic coding quoted in the SpecStory checklist. Thoughtbot's reviewers make the same observation: "AI will often change other parts of the app to get something working," and those parts may have been that way for a reason Thoughtbot.

Adjacent files are where you should slow down in a specific way. An agent can make a suite pass by editing the tests, so read test files before application code. Watch for a deleted or skipped test, an assertion loosened from toHaveLength(2) to toBeDefined(), a disable comment, or a relaxed lint rule SpecStory. Google's own review guidance is blunt on this point: tests should be added in the same change as the production code, and a human must confirm the tests actually fail when the code breaks Google's What to look for in a code review. That is a different check from "does the test pass."

Docs and config deserve a lighter pass, with one exception: config changes can alter runtime behavior for every user, so treat a changed environment file, feature flag default or CI job as in-scope until proven adjacent.

The hard cases are not the obviously unrelated files. They are the unrelated edits that arrive bundled inside a file that also contains the real fix. A single hunk that reformats a function you did not ask anyone to touch is easy to skim past, and it is exactly where a signature change or a behavior change can hide.

Separate the cosmetic from the substantive with a whitespace-blind diff:

git diff -w origin/main...HEAD -- src/lib/session.ts

The -w flag ignores whitespace when comparing lines, which is the standard way to strip formatting noise from a diff a Stack Overflow answer on ignoring formatting changes. If the output is empty but the file appears in your list, the change is formatting-only. If the output is short but the raw diff was long, most of the churn was cosmetic and you can focus on the remaining lines.

Formatting-only changes still deserve a verdict, just not a line-by-line one. The reason is review honesty: a reformatted file makes blame harder to read later and can bury a real edit in future diffs. Google's guidance is that on matters of style, the project's style guide is the authority, and any style point not in the guide is a matter of personal preference Google's Standard of Code Review. So the question is not "do I like this formatting," it is "does this match the guide, and was it asked for."

Refactors are the more dangerous cousin, because they look tidy and change behavior. Use rename and move detection to see whether a file shrank or moved rather than changed:

git diff -M --stat origin/main...HEAD
git log --oneline origin/main..HEAD

A refactor that renames a symbol or changes a function signature can ripple into call sites the agent also edited, which is why an unrelated refactor is rarely really unrelated. The reviewer's job is to ask whether that change is load-bearing for the fix. If it is not, ask for it to be split out into its own pull request.

Here is a quick decision rule for a hunk you did not expect:

1. Is the hunk inside an in-scope file but unrelated to the task? Flag it, and ask why it is there.
2. Is it formatting-only (empty under -w)? Decide whether it belongs to a separate formatting commit.
3. Is it a rename, moved code or changed signature? Check the call sites, then ask for a split.
4. Does it change any default, flag, permission or config key? Treat it as behavior, not style.

Once the scope is clean, the review itself still has to catch the failure modes that make agent code look polished and behave badly. These classic checks are unchanged by the scope audit, so run them on the in-scope and adjacent files: logic and correctness, security, performance, maintainability and production readiness a DEV Community review checklist.

The most consistent gap is the happy path. A model will write a perfect function for the data it expects and skip the null check, the empty array or the error path, because in its training examples the data was always present Haseeb Sohail on evaluating LLM code quality. That matters more than it sounds: an evaluation of snippets from five models found almost half contained bugs that were often impactful and exploitable CSET's report on cybersecurity risks of AI-generated code. A separate study cited in the Cloud Security Alliance writeup found 62% of AI-generated code solutions contained design flaws or known vulnerabilities CSA on AI code security risks.

Security checks belong in the same pass, not a later one. Look for string-concatenated queries, missing input validation on endpoints, overly permissive role checks and secrets in the diff, since these are the recurring patterns analysts find in generated code Endor Labs on security vulnerabilities in AI code. Also verify every new dependency name exists in its registry and matches the lockfile, because models can hallucinate package names and attackers can register them GitHub's guide to reviewing AI-generated code.

None of this changes the scope decision. A security flaw in an unrelated file is still a reason to stop and split, not to review the file line by line inside this PR.

A routine only survives contact with a busy day if it is written down. Put it in the pull request template so it travels with every agent change, which is the pattern the SpecStory checklist recommends SpecStory. The version below assumes a bug fix task and a three-file, 150-line budget.

Scope audit
- [ ] Task restated in one sentence: ______
- [ ] Diff budget set before reading: <= 3 files, <= 150 lines
- [ ] File list checked with --name-only --no-renames
- [ ] Each file labeled in-scope / adjacent / unrelated
- [ ] Every unrelated file has a stated reason or is split out
- [ ] -w diff run on any file with heavy churn
Tests and gates
- [ ] No test deleted, skipped or weakened; assertions still assert
- [ ] New packages exist, are needed, and pass an audit
- [ ] No secrets in code, tests, fixtures or docs
Owner
- [ ] The named owner can explain every changed file

The last item is not ceremony. The OWASP cheat sheet's requirement that AI-generated code have a human owner means the person who accepts the change should be able to say why each file moved SpecStory. If the owner cannot explain an unrelated file, the correct outcome is not a careful review, it is a split or a revert. Google's review standard is a useful tiebreaker here: approve when the change definitely improves code health, and remember there is no such thing as perfect code, only better code Google's Standard of Code Review.

One more practical note: do not stop at the first unrelated edit you find. If file three of twelve is out of scope, the odds are good that file seven is too. The audit is cheap when it runs over the whole list, so finish the classification pass before you comment, and send one message covering every out-of-scope file rather than trickling them out across a long review thread.

If your team runs several agents and several models at once, keeping the same review prompt and the same scope checklist across all of them is where a single, consistent interface pays off. Quanta Inference puts text, image and embedding models behind one OpenAI-compatible API key, so the review instructions and diff conventions stay constant no matter which model drafted the patch: https://quantaintelligence.ai/inference.

Suppose the task was "fix the null dereference when a user has no display name." The agent's PR shows nine changed files and 340 lines. That is already over budget on both counts, so you stop and classify before reading.

The raw size said 340 lines. The useful number is three files, because the other six are noise you can decide about in minutes: one under -w, two to revert, one dependency to verify, one gate to restore. Your line-by-line attention, which is the scarce resource, goes to profile.ts and the assertions in profile.test.ts. That reallocation is the entire point of the audit, and it is what makes reviewing agent output sustainable rather than exhausting.

For teams that would rather not hand-run this on every PR, the same judgment can be delegated with clear instructions. Quanta Experts are set up to read your repository context, do the classification work and hand back a finished deliverable you can check against this checklist: https://quantaintelligence.ai/agents.