Best Practices for AI Coding Instructions

• Write in direct, imperative language that is easy for humans and models to parse.
• Focus on rules that materially change agent behavior, not generic advice.
• Use clear sections and headings so content can be chunked and cited easily.
• Link to canonical documents instead of duplicating long explanations.
• Avoid time-sensitive or one-off project notes in instruction files.

Instruction files should live in source control alongside the code they influence. Changes to AGENTS.md, CLAUDE.md, and GEMINI.md should go through normal review, with clear commit messages and reviewers who understand the impact on agent behavior.

Some teams maintain a separate "instruction standards" repository that holds authoritative templates. Others keep templates in a dedicated directory next to code. The important part is that there is a single, traceable history of how instructions evolve.

• Roll out changes in small batches with clear owners and success criteria.
• Start with less critical repositories to validate new instructions.
• Use pull requests for all instruction updates so changes are reviewable.
• Include before-and-after snippets in PR descriptions for clarity.

Systems like DirectiveOps can generate rollout PRs automatically, but the review and acceptance process should still follow normal engineering practice.

Monitoring drift means scanning repositories on a recurring schedule, comparing instruction files against the current standard, and tracking findings in a place where platform and security teams can act on them.

Metrics such as "repos compliant with AGENTS.md standard", "instruction files with conflicting directives", or "repos referencing deprecated flows" help leaders see whether AI coding instructions are moving in the right direction.