How to review agent-generated pull requests

How should I review agent-generated pull requests now

Review agent-generated pull requests the same way you would review a risky human change, then add extra scrutiny for hallucinated APIs, missing context, and accidental scope creep. Start with whether the PR solves the right problem, not whether the code looks polished. GitHub’s review guidance for AI-generated code puts functional checks first, and GitLab’s review guidance also centers clear descriptions, validation steps, and ownership checks.

First, read the PR description and compare it with the actual diff. The question is simple: does this branch do exactly what the task asked for, no more and no less. Agent output often drifts into extra refactors, new helpers, new packages, or unrelated cleanup because those changes look efficient in isolation. GitHub explicitly warns that large PRs are harder to review and that review quality drops as size grows.

Run the checks before you spend time on line-by-line review. Build the code, run tests, and run static analysis or security scanning if your stack supports it. GitHub’s guidance says to start with functional checks, then use tools such as CodeQL and Dependabot to catch vulnerabilities and dependency issues. If the code fails basic validation, do not try to reason your way into approval.

Inspect the intent, not just the syntax. Agent-generated code can be syntactically correct and still encode the wrong business rule, wrong edge case, or wrong architecture. Check whether the solution matches the project’s existing patterns, whether it uses the right abstractions, and whether it respects any local conventions in README files, recent pull requests, or team guidelines. GitHub’s AI review guide calls this out directly under verifying context and intent.

Read the diff for AI-specific failure modes. Watch for made-up APIs, invented framework methods, brittle assumptions, silent error swallowing, and code that looks complete but does not handle failure states. Review every new dependency closely. GitHub says to verify that suggested packages exist, are actively maintained, and fit your license requirements. That matters because agents can confidently recommend packages that are wrong, suspicious, or unnecessary.

Treat every external dependency as a review item, not a convenience. Check whether the package is real, whether it is already used in the repo, whether it duplicates an existing library, and whether the license is acceptable. If the agent added a package to save time, make the burden of proof higher, not lower. The easiest way to ship unwanted risk is to approve a dependency you did not inspect because the code around it looked clean.

Review changes in small slices when you can. GitHub recommends stacking AI-generated work into smaller dependent pull requests, because smaller diffs are easier to understand and safer to merge. That advice matters even more for agent output, because one early mistake propagates into everything built on top of it. If the branch is already large, ask for it to be split before you spend time reviewing details.

Do your own self-review before asking anyone else to look at it. That means reading the diff as if you were seeing it for the first time, checking tests, and verifying that the branch is self-contained. GitHub’s stacked-PR guidance says to review the generated change yourself before moving on. That is not a nicety, it is how you avoid handing teammates a PR that still contains obvious errors.

Use code owners, reviewers, and documented review rules when the repository already has them. GitHub supports automatic review requests from CODEOWNERS, and GitLab documents review instructions and approval routing for AI-generated work. If a change touches an area with special ownership, the review should include the person or team that owns that area. Agent-generated code does not get a lighter review because it was produced quickly.

Pay attention to the part people get wrong: they review only the final code, not the path that produced it. With agent-generated pull requests, the generation process matters because a good-looking diff can still come from a vague prompt, stale context, or hidden assumptions. If the PR description does not explain the source of truth, the tests that were run, and the risky decisions the agent made, request that information before approval. OpenAI’s guidance for working with agents also stresses explicit review feedback and human curation.

When you leave feedback, be specific about the failure, not just the symptom. Say what behavior is wrong, which input triggers it, what test is missing, or which convention is being violated. GitLab’s review guidance and OpenAI’s agent workflow material both point toward concrete validation and clear revision requests. That style matters more with agent-generated code because precise feedback makes the next iteration better instead of merely different.

A practical review order works well: confirm scope, run checks, inspect risky files, read dependencies, then verify edge cases and rollback risk. In a backend PR, that often means reading the migration first, then the API handler, then tests, then any new library. In a frontend PR, it can mean checking state management and error handling before style and formatting. Keep the order stable so review does not become a guessing game.

If the PR is too large to review safely, stop and split it. That is not blocking for its own sake, it is a quality control step. GitHub’s docs are explicit that review quality declines as PR size increases, and stacked pull requests exist to keep each layer reviewable. A single giant agent-generated branch is how subtle mistakes survive because nobody can hold the full change in working memory.

For teams, make the review standard visible in the repo. Put expectations in docs or review instructions, such as which checks must pass, which directories need owner review, and what counts as sufficient evidence for a generated change. GitLab documents review instructions for agent workflows, and GitHub recommends using organizational review criteria in Copilot review settings. The point is to make the next review cheaper without making it looser.

If you want a simple rule, use this one: approve only when you understand the change, trust the tests, and would be comfortable explaining the diff to the next person on call. Agent-generated code is useful when it saves drafting time, but review still owns correctness, maintainability, and risk. The code is not done when the agent stops, it is done when a human can defend it.

If you are setting up a workflow around this, keep the loop short. Ask the agent to make a smaller change, review the branch yourself, run the checks, and only then request teammate review. DevConnect at https://devconnectplatform.com is for finding real testers for app work, but for pull requests the same principle applies: keep the task narrow enough that a human can verify it quickly.

FAQ

Should I trust an agent-generated PR if the tests pass No. Passing tests reduce risk, but they do not prove the change matches intent, fits project conventions, or avoids a hidden dependency problem. Review the scope, the edge cases, and the diff around any new package or abstraction.

What if the agent added code I did not ask for Treat that as a review failure until the extra work is explained and justified. Extra refactors, cleanup, or convenience changes can hide bugs or widen the blast radius of the PR. Ask for a smaller branch or a clearer split if the scope is no longer easy to verify.

How do I review agent-generated dependencies Verify that each package exists, is maintained, and matches your licensing and security standards. Check whether the dependency is already present in the project, whether a built-in or existing library already solves the problem, and whether the agent invented the package name.

Who should approve an agent-generated PR The same people who should approve any risky PR: the code owner, the maintainer of the affected area, or the reviewer who can validate the behavior. If the change touches an owned path, use the repo’s ownership and review routing rules.

How small should an agent-generated PR be Small enough that one reviewer can understand the branch without guessing. GitHub’s guidance is to stack dependent changes into smaller pull requests because large ones are harder to review and more likely to stall. If the diff feels hard to hold in your head, it is already too large.

What is the most common mistake in reviewing agent output Reviewers accept polished code without checking whether it solves the right problem. The right review order starts with intent, then validation, then dependencies, then implementation details. Syntax is the last thing to worry about when the bigger question is whether the branch should exist at all.