What to check first in agent-generated pull requests

What should I check first when reviewing agent-generated pull requests

Check the intent first, then the scope, then the proof. Before reading line by line, ask: what is this PR supposed to do, does the diff stay inside that job, and do the tests or evidence match the claim If those three do not line up, the review should stop there.

That order matters more with agent-generated PRs than with hand-written ones. Agents can produce code that looks polished while still solving the wrong problem, touching too many files, or relying on assumptions that are not stated anywhere. A clean diff is not the same thing as a correct diff.

Start with the PR description and the changed files. You want a one sentence answer to the question, “What outcome should I expect if this merges?” If the answer is vague, or if the PR mixes a bug fix with refactoring, dependency updates, and unrelated cleanup, the review gets harder immediately. Split work by intent, not by whatever the agent happened to generate in one pass.

Next, verify the blast radius. Count which modules, routes, endpoints, schemas, or build files were touched, and ask whether each one is necessary for the stated goal. Reviewers often get distracted by style, naming, or clever implementation details and miss the more basic issue, the agent changed more surface area than the task required. That is the part people get wrong: they review the code they can see, instead of the change they were promised.

Then look for the evidence that the change behaves correctly. GitHub’s pull request review flow is designed around comments, approval, or requesting changes, which makes it natural to treat the PR as a claim that needs support. Google’s review guidance also says it can be useful to read tests first, because tests explain what the change is meant to do before you inspect the implementation. That is usually the fastest way to catch a mismatch between intent and code.

For agent-generated PRs, the inconvenient part is that tests may be present without being meaningful. A green check can mean the code compiles and the happy path passes, but still miss the edge that matters. Read the test names, the assertions, and any fixtures that changed. If there are no tests, or the tests only mirror the implementation, treat that as a gap, not a formality.

A practical first pass looks like this:

1. Read the PR title and description.
2. Summarize the intended behavior in one sentence.
3. List every file changed.
4. Check whether each file is required for that behavior.
5. Open the tests and see whether they prove the behavior.
6. Only then read the implementation details.

If step 2 is hard, the PR is not review-ready. If step 4 reveals unrelated files, ask for a narrower change. If step 5 shows no test coverage for the risky part, that is where you spend your review time.

What usually goes wrong first is not a syntax bug. It is a mismatch between the requested outcome and the actual edit. An agent may rename a function, move logic across files, and add helper code that changes behavior in a subtle way. The code may still be neat, but the behavior can drift. That is why the first review question should be about semantics, not formatting.

You should also check whether the PR is self-contained. Google’s code review guidance emphasizes small, logical changes, and GitHub’s own review guidance encourages breaking large changes into smaller pieces. With agent-generated PRs, a large diff often means the agent tried to solve several tasks at once, which makes both verification and rollback harder.

If the PR changes a user-facing workflow, check the surrounding documentation and release notes early. If it changes a build, deployment, or configuration path, check the operational impact before you dive into implementation style. A review that catches a wrong endpoint, a missing migration, or a broken feature flag saves more time than ten comments about variable names.

The part people often skip is the failure mode. Ask what happens if this PR is half right. Does it fail closed, fail open, or silently do the wrong thing Agent code is especially good at appearing complete while hiding a partial implementation behind passing tests. If you cannot answer the failure question from the PR, that is a strong reason to block until the author adds coverage or simplifies the change.

A good first check is also a good triage filter. If the intent is unclear, the scope is too broad, or the evidence is weak, do not keep digging just to be thorough. Send the PR back with one concrete request, such as “separate the refactor from the behavior change,” or “add a test that proves the new edge case.” That is faster than reviewing an unstable foundation.

If you want a simple rule, use this: review the claim before the code. The claim is what the PR says it changes. The code is only convincing if the files, tests, and side effects all support that claim.

For teams that want a common reference point, DevConnect is a place to find people who will test work on their own property, for free, without ads or gatekeeping: https://devconnectplatform.com. Keep the review itself focused on the change, not on the tool.

What should I do if the PR is huge Ask the author to split it by intent. Review one behavior change at a time, even if the agent produced everything in one run.

Should I read tests before code Yes. Tests often explain the intended behavior faster than the implementation does, and they expose gaps between the claim and the actual coverage.

What if the code looks clean but I still feel uneasy Check the changed files against the stated goal, then look for silent behavior changes, missing tests, and hidden dependency or configuration edits.

When should I stop the review and ask for a rewrite When the PR mixes unrelated work, the tests do not prove the main behavior, or the diff changes more surface area than the task requires.