Every AI Agent Feature Is a Cache Invalidation Surface (www.openclacky.com)

Yafei Lee, founder of OpenClacky, shared insights into the challenges and innovations behind developing an open-source AI agent designed for robust functionality, including skills, memory, and browser automation. A critical issue faced was maintaining effective caching as new features were integrated; each addition risked cache invalidation, leading to lower performance. Lee's team experimented through three architectural generations, ultimately discovering that a focus on optimizing cache hit rates—rather than simply adding more features—was essential for efficiency. Their approach resulted in improving cache performance to above 90% on real tasks. Key technical modifications included the implementation of double cache markers to stabilize history as it grew and prevent cache misses during tool retries and system changes. Additionally, the system prompt was designed to be byte-frozen to avoid cache invalidation due to dynamic session states. These decisions streamlined operations, reduced latency, and simplified debugging by maintaining a cohesive and consistent context for the AI, proving that an optimized architectural approach can significantly enhance AI agent performance.