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This paper addresses the architectural challenges posed by the stateless nature of LLM APIs in enterprise conversational systems, which necessitate client applications to handle conversational state and memory. By introducing the Hydration Proxy Pattern, it offers a solution that separates session persistence from the reasoning engine, thereby enhancing platform sovereignty over conversational data. The authors also propose the Context Stabilization Mandate to balance sovereign state management with key-value caching efficiency, providing a robust framework for conversational data systems.
The Hydration Proxy Pattern could revolutionize how enterprises manage conversational state, ensuring data sovereignty while enhancing scalability.
As enterprise platforms transition to conversational reasoning interfaces, the stateless nature of LLM APIs creates an architectural gap. While statelessness enables horizontal scalability for AI providers, it forces client applications to manage the entire burden of conversational state and semantic memory. The work identifies the Hydration Proxy Pattern, an architecture that decouples session persistence from the reasoning engine. The framework ensures platform sovereignty over conversational data while enabling secure, multi-stage semantic grounding. We further propose the Context Stabilization Mandate to resolve the tradeoff between sovereign state management and KV caching.