AI code review on GitHub: what the platform decides for you
Search for how to add an AI reviewer to GitHub and you land on a pile of tool READMEs, a Reddit thread asking whether any of them are worth it, and GitHub's own Copilot documentation. Each one describes the product. None of them describe the layer underneath, which is where the behavior of the reviewer actually gets decided. A reviewer installed on GitHub can read certain files, write certain…
The article discusses the AI code review feature on GitHub, specifically focusing on how the decision-making process behind the reviewer operates. There are two main ways an AI reviewer can be integrated with GitHub: through a hosted app installed on the repository or by setting up a workflow within your own repository. The hosted app receives a scoped token from GitHub and runs on the vendor's infrastructure, while the self-hosted workflow operates within your pipeline and posts comments or summaries using the permissions configured by the user.
The article emphasizes the importance of understanding the underlying mechanisms that determine the behavior of the AI reviewer, as failure in one setup does not necessarily result in an error but may lead to unnoticed context being omitted from the review.
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