Code Review Was Never Mostly About Bugs. With Agents, It's How You Learn Your Own Code
Book: System Design Pocket Guide: Interviews Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub Picture the pull request. An agent wrote it: 600 lines across 11 files, a new retry path for a payment webhook,…
The article discusses the limitations of code review and the role of AI agents in software development. It cites research from Microsoft and Google which found that code review primarily serves to help the reviewer understand the code, rather than solely for finding bugs. Microsoft's study showed that the top reasons for reviewing code were for improvements in readability and understanding, with only 14% of comments focused on defects.
The article highlights the risk of AI agents writing code changes without the reviewer gaining a deep understanding of the code. As AI agents generate more diffs and larger changes, the reviewer may struggle to comprehend the entire codebase. This creates a "comprehension debt" where engineers may approve code they do not fully understand, leading to implicit endorsement of the code.
To address this, the article suggests reading AI-generated code in the order it was written, as the agent generates changes turn by turn. It recommends asking the AI questions during the review process to better understand its reasoning and approach. Keeping PRs small and manageable is also advised, in line with Google's process of reviewing small changes with single reviewers.
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