GitHub Publishes the Playbook for Reviewing Agent-Generated PRs

Agent PRs look different from human PRs. GitHub's engineering blog published a hands-on guide addressing what to look for and where issues hide, and it's the most practical thing I've read on the topic.

Three patterns GitHub calls out that I've seen in my own agent-generated code:

Over-abstraction. Agents love creating abstractions. A function that's called once gets wrapped in a class with an interface and a factory. The code works, the tests pass, and now you have three files where you needed one. Review agent PRs specifically for unnecessary indirection.

Hallucinated dependencies. The agent generates an import for a package that doesn't exist, or imports the wrong version of a package that does exist. CI catches some of this, but not when the import is for an internal module the agent named slightly wrong. It compiles, it runs in test, and it fails in production when that module's actual API differs from what the agent assumed.

Subtle logic errors that pass CI. This is the scariest one. The agent writes code that's syntactically correct, passes type checking, passes unit tests, and implements the wrong business logic. The agent didn't understand WHY the original code worked that way. It just pattern-matched the WHAT.

This guide complements the PR surge story above. The volume problem is real, but the quality problem might be worse. Traditional code review assumes the author understood the codebase and made intentional decisions. Agent-authored code breaks both assumptions.

GitHub's earlier "trust layer" piece (May 8) focused on automated CI-level checking. This one targets the human reviewer. Read both. Update your team's review checklist. And if you don't have one, the fact that 17 million agent PRs shipped in the last six months should motivate you to create one.