Research Report 2.3: State Management Across Calls
The stateless paradox: how conversational AI maintains the illusion of memory on protocols designed to forget everything
Research into how LLM systems maintain conversational state across stateless HTTP API calls - covering the reconstructed illusion of memory, context window optimization strategies, token budget management, truncation approaches, caching mechanisms, and the behavioral analysis of how different state management strategies affect quality degradation in long-running conversations.
Also connected to
The distributed systems problem hiding inside every multi-agent AI system: how do agents share what they know without drowning in what they don't need?
The unified framework that production-grade agent platforms use to make context work at scale
How to architect context that scales across hundreds of agents without degradation
Reference specification for retrieval augmentation patterns in agentic systems, covering vector search, hybrid retrieval, and context window optimization.
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