Context Management System Architecture
How to architect context that scales across hundreds of agents without degradation
A multi-tier context architecture for agentic AI systems - covering the four-layer hierarchy from global to session context, automatic context preparation for new projects, compression strategies that reduce tokens by 59% while preserving meaning, cross-project sharing, and Git-like versioning for context state.
Also connected to
The stateless paradox: how conversational AI maintains the illusion of memory on protocols designed to forget everything
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
Reference specification for retrieval augmentation patterns in agentic systems, covering vector search, hybrid retrieval, and context window optimization.
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