Developers Struggle to Keep AI-Generated Code Consistent With Project Conventions

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Engineering teams using AI to generate production features are encountering a growing problem: code quality and consistency degrade noticeably after the first few AI-generated features. While early outputs closely mirror existing codebase patterns, later ones introduce subtle inconsistencies in error handling, naming conventions, and test structure. Common mitigation tools like AGENTS.md files, linters, and code reviews offer only partial relief, as they either go stale, miss semantic drift, or slow down the speed benefits AI is meant to provide. One team is developing an opinionated SaaS framework called Kumiko to reduce the surface area for drift by constraining what the AI can generate. The broader developer community is actively debating solutions, including auto-generating convention documents from the codebase and building custom semantic linters.

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