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Why Developers Are Now Writing Code With AI Agents in Mind

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A software developer argues that most AI coding assistant errors stem not from a lack of programming knowledge but from missing project-specific context. Unlike human developers, AI agents begin every task by reconstructing a codebase from scratch, limited by context windows and reasoning budgets. The author now structures code with explicit metadata — such as file paths, usage references, module READMEs, and impact annotations — to help AI tools navigate repositories more accurately. Practices like noting which components are shared, which hooks have side effects, and what architectural rules must never be broken reduce the guesswork AI agents must perform. The core idea is not to make AI smarter, but to make codebases easier for both humans and AI agents to understand.

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