How do you prevent AI-generated code from drifting away from your conventions over time?

We've been generating production features with AI for a while now — auth flows, billing hooks, notification handlers. And we've hit a pattern we don't have a good answer to yet.

The first feature the AI generates looks great. It reads the codebase, picks up the patterns, and the output looks like something a senior dev wrote.

The tenth feature? Less so. Small inconsistencies creep in:

• A handler that doesn't follow the error-handling convention
• A schema field with a different naming pattern
• A test that checks existence instead of behavior

None of it is wrong. All of it is subtly inconsistent.

Fixes we've tried

AGENTS.md / CLAUDE.md — helps, but gets stale and doesn't scale with the codebase

Code review — catches it, but defeats some of the speed advantage

Linting + formatting — catches easy stuff, misses semantic drift

What we haven't solved: giving the AI a "living" representation of your conventions that stays current as the codebase evolves.

We're building Kumiko — an opinionated SaaS framework — partly as an answer to this. If the framework constrains what's possible, drift has less surface area. But I'm not convinced that fully solves it either.

Curious what's actually working for others

• Do you just review AI output carefully and accept some drift?
• Custom guards / linters that encode your conventions?
• Something that auto-generates AGENTS.md from the codebase?

What's your approach?