Your AI Remembers Everything Except the Thing You Keep Telling It

AI agents rely on system prompts for consistent responses, but the repeated computation of these prompts for every request, especially in high-volume applications, incurs significant costs. Prefix caching is a solution designed to reuse previously computed token sequences in AI models, reducing redundant computation. However, while this method is effective for static contexts like system prompts, it struggles with conversational workloads, where each interaction incrementally alters the token sequence, making reuse difficult. This limitation arises because conversations rarely align with fixed token block sizes used in caching, leading to cascading cache misses as the conversation evolves. Consequently, although prefix caching can significantly reduce costs for shared static contexts, it is less effective for dynamic, long-lived interactions. To manage the complexity of evolving conversational contexts, future infrastructure will need to focus on understanding and efficiently managing these dynamic interactions rather than treating data purely as static sequences.