Hydration Proxy Pattern manages stateless LLM APIs for conversational data

A new arXiv paper (2609.01834) proposes the Hydration Proxy Pattern to manage stateless LLM APIs for conversational data, cutting per-turn token spend by 30–50% while preserving data sovereignty. The pattern offloads conversational memory to a dedicated layer, enabling multi-turn agents to scale without rewriting orchestrators or leaking session data.

arXiv https://arxiv.org/abs/2609.01834 Hydration Proxy Pattern manages stateless LLM APIs for conversational data Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated. LLMs are stateless, forcing your agents to handle all conversational memory—adding latency, cost, and failure risk. The Hydration Proxy Pattern offloads this to a dedicated layer, cutting per-turn token spend by 30–50% while keeping data sovereignty. If you’re shipping multi-turn agents, this lets you scale without rewriting your orchestrator or leaking session data. Because LLM APIs are stateless, your app owns all conversational state and semantic memory — and the naive fix of injecting sovereign context yourself silently breaks KV cache reuse, driving up latency and token cost. The proposed hydration proxy decouples session persistence from the reasoning engine so you keep data sovereignty, and the "context stabilization" rule is the part that matters: keep the prefix stable across turns so caching still hits instead of paying full prompt cost every message.