Architecting Conversational Data Systems for Stateless LLM APIs: The Hydration Proxy Pattern

Abstract: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.

3 pages, 1 table. Presented at the SAO workshop at the 1st ACM Conference on AI and Agentic AI Systems (ACM CAIS 2026)

Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

D.2.11; H.2.8; I.2.7

arXiv:2609.01834 [cs.AI]

(or arXiv:2609.01834v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.01834 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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