Your codebase has a new primary reader. And it’s paying 7.5x more.

Short summary

AI agents as code readers require different architectural patterns than humans. Analysis of three production codebases shows agent-optimized architectures reduce token consumption by 7.5x and wall-clock time by 4.5x versus traditional Clean Architecture approaches. The key insight: every architectural hop humans appreciate for separation becomes a token cost for agents, introducing 're-derivation cost' as the primary metric for agent-era design.

• •Agent-optimized architectures use 7.5x fewer tokens than traditional Clean Architecture
• •Feature-focused directories with <300-line files outperform layered separation patterns
• •New metric: re-derivation cost replaces traditional elegance/DRY as the primary architectural concern

Generated with AI, which can make mistakes.