Research Report 5.4: Shared Context & Memory
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?
Research into shared context and memory architectures for multi-agent LLM systems - covering centralized versus distributed memory models, context synchronization protocols, memory partitioning strategies, attention and relevance filtering, forgetting mechanisms, and the behavioral analysis of how shared memory affects coordination effectiveness and failure modes.
Also connected to
The stateless paradox: how conversational AI maintains the illusion of memory on protocols designed to forget everything
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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