AI is transforming how developers handle documentation, making it more critical than ever to store docs alongside code in repositories. The traditional argument that "code is always up to date" no longer holds when AI agents can automatically maintain alignment between documentation and implementation. Version control, proximity to code, and formal approval processes make repositories the ideal home for technical documentation.

Beyond basic version control benefits, AI agents solve the stale documentation problem by automatically keeping docs synchronized with code changes. They also benefit from having higher-level context available in the repository, including architecture proposals, product specifications, and implementation plans. This context helps agents work more effectively and reduces token consumption during development. The trend shows engineers increasingly focusing on specifications and guidelines rather than implementation details.

As development workflows shift left and AI handles more implementation work, the time spent reviewing specifications, harnesses, and guidelines will dominate engineering efforts. Documentation becomes the primary artifact for human review, not just an afterthought. Tools like GitHub support rich features including mermaid schemas, and even non-engineers can access docs through internal websites. The Chinese proverb holds true: the palest ink is more reliable than the most powerful memory - especially when AI agents can leverage that documentation to work more efficiently.