Toots from 2025-11-28#
Some themes I’m hearing from people having success AI coding agents are explicit context, documen…#
Some themes I’m hearing from people having success AI coding agents are explicit context, documentation through time, and automated feedback mechanisms.
Explicit context includes the practices and assumptions that had previously been shared via IC senior/junior mentorships. Explicitly articulating architectural patterns, constraints, domain boundaries, language-specific design patterns, the definition of done, refactoring signals, error handling policies, optimization opportunities, etc. What would have been incrementally, lazily, and only verbally surfaced during an apprenticeship relationship is promoted to one or more documents.
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Documentation over time is the history behind why code works a certain way: special cases, environment-specific behaviors, why some IPC was chosen over another. While this information is often available in commit history or cryptic comments, it’s less often made first class. Artifacts like ADRs capture the evolution of code. This historical documentation provides counterbalancing evidence against what is intentionally absent from the current codebase version.
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Automated feedback mechanisms include style, code organization, linters, compiler warnings as errors, semgrep policies, OWASP Web Testing Guidance, fitness functions for correctness, unit tests, fuzzers, and similar types of functional and non-functional feedback mechanisms. These are guardrails that operate at agent speed and provide actionable corrective signals to ensure alignment.
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These are also hallmarks of pre-AI high-performing engineering teams. They highlight the importance of shared understanding, history, and actionable feedback. In my experience these practices help most teams to be more effective, independent of how they are constituted.