Strategies for keeping context and documentation in-repo, maintaining worklogs, updating docs alongside commits, static sites for agent navigation
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Developers are increasingly treating in-repo documentation as an essential external memory for AI agents, utilizing worklogs, Gherkin features, and even specialized static sites to help LLMs navigate complex codebases without exceeding context limits. While some skeptics argue that agents should simply read the code to avoid the trap of stale documentation, proponents emphasize that "active" updates—rather than mere appending—are vital for teaching the AI the "why" behind past decisions and preventing it from rehashing failed approaches. This strategy often evolves into a sophisticated ecosystem of automated validation scripts and contract-first APIs that prioritize reproducibility, effectively turning the repository into a high-fidelity environment tailored for agent navigation. Ultimately, while this approach may transform repos into a series of prompts and logs, it raises intriguing concerns about whether massive documentation folders might eventually create cognitive blindspots that limit an agent's ability to think outside established patterns.
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