急増するエージェント生成PRをどうレビューするか Agent pull requests are everywhere. Here’s how to review them.

As AI coding agents proliferate, a growing share of pull requests on GitHub are no longer authored by humans. GitHub's blog post addresses a practical question that many engineering teams now face daily: how should reviewers approach PRs opened by agents such as Copilot coding agent?

The central argument is that review practices themselves need to evolve. Agents can produce large volumes of code quickly, but they can also misinterpret requirements, over-edit unrelated files, omit meaningful tests, or introduce subtle security issues. Reviewers are encouraged to begin by validating intent — confirming that the PR addresses the actual problem stated in the issue or prompt, and that the scope of changes is proportionate to the task. Treating the agent's description as a hypothesis rather than ground truth is a useful framing.

On the technical side, the post highlights several focal points. Tests should be inspected not just for presence but for whether they meaningfully exercise the new behavior; agents are known to generate assertions that pass trivially. Dependency bumps, configuration changes, and any handling of secrets deserve extra scrutiny, as do deviations from the project's existing architecture and style. GitHub points to its own tooling, including Copilot code review and the coding agent, which are designed to support workflows where humans and agents collaborate on the same branch.

The broader industry context is worth noting. Autonomous PR-generating agents have become a crowded category, with offerings from Cursor, Cognition's Devin, GitLab Duo, and Anthropic's Claude Code, alongside open-source frameworks like SWE-agent and Aider. As volume grows, code review risks becoming the new bottleneck of software delivery, shifting the cognitive load from writing to evaluating. Teams are likely to respond by tightening CI gates, enforcing smaller PRs, and writing more explicit prompts and repository-level instructions so that agents produce reviewable, narrowly-scoped diffs.

There is also a governance dimension. Even when an agent authored the change, the human who approves and merges remains accountable for what ships. That principle is unlikely to change in the near term, and it suggests that organizations adopting agentic development should invest in reviewer training, clearer ownership rules, and audit trails that distinguish human edits from machine-generated ones. Used well, agent PRs can absorb tedious work and accelerate delivery; used carelessly, they can quietly erode code quality. GitHub's guidance is a reminder that the discipline of reading code carefully matters more, not less, in an agent-heavy workflow.