Supervised software engineering agents are increasingly capable of identifying necessary updates and making larger changes to a codebase. At the same time, we're seeing growing complacency with AI-generated code, and developers becoming reluctant to review large AI-made change sets. A common justification for this is that human-oriented code quality matters less since AI can handle future modifications; however, AI coding assistants also perform better with well-factored codebases, making AI-friendly code design crucial for maintainability.
Fortunately, good software design for humans also benefits AI. Expressive naming provides domain context and functionality; modularity and abstractions keep AI’s context manageable by limiting necessary changes; and the DRY (don’t repeat yourself) principle reduces duplicate code — making it easier for AI to keep the behavior consistent. So far, the best AI-friendly patterns align with established best practices. As AI evolves, expect more AI-specific patterns to emerge, so thinking about code design with this in mind will be extremely helpful.
