Why AI Coding Agents Work Better With WET Code Than DRY Architecture

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For decades, the DRY (Don't Repeat Yourself) principle has guided software development by encouraging shared, reusable code functions. However, as agentic AI systems like Claude Code and Devin take on more coding tasks, this approach is showing limitations — these agents must load numerous scattered files into their context windows, consuming large amounts of tokens and increasing costs. WET (Write Everything Twice) architecture, which tolerates code duplication, keeps logic localized so AI agents can understand and modify a component without traversing complex dependency chains. Since AI eliminates the manual typing burden that made WET impractical for human developers, the traditional drawbacks of duplication largely disappear. Isolated components also reduce the risk of unintended side effects, allowing agents to make changes confidently without breaking unrelated parts of a codebase.

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