Optimizing AI IDEs at Scale

Scaling AI development tools presents challenges, primarily due to the additional cost and complexity they introduce, often without proportional increases in productivity. A detailed analysis revealed that excessive cache reads were the primary cost driver, highlighting inefficiencies in context management, such as outdated rules and accumulated guidance. To address this, the team standardized AI development configurations, minimized always-on rules, and created purpose-built subagents, which improved tool reliability and reduced unnecessary context overhead. Additionally, they adopted a structured workflow of planning, executing, and compacting, with tests as a primary evaluation method, to streamline processes and reduce prompt drift. Post-refactor, they observed a significant reduction in output costs, achieving the same development results with fewer resources by focusing on removing systemic entropy rather than reducing AI usage. This approach emphasizes the importance of treating AI configurations like real code, centralizing and refactoring them, and ensuring tight, machine-checkable planning for better execution outcomes.