Context Quality is the New Model Quality: An Open Memory Provider Standard with Zero-Downtime Compaction for LLM Agents

Context Quality is the New Model Quality: An Open Memory Provider Standard with Zero-Downtime Compaction for LLM Agents Short title: How We Eliminated 77% Entity Loss and Agent Freeze with an Open Memory Standard Author: L. Zamazal, GLG, a.s. Date: March 2026 Keywords: LLM memory, context compaction, agent memory, information loss, on-demand recall, UAML, structured memory, MCP, zero-downtime, open standard Abstract Large language models have no persistent memory. Every inference call receives the entire conversation context as input, and the model's response quality depends directly on the quality of that input. When context grows beyond the window limit, platforms perform compaction — summarizing the conversation to fit. We measured that standard compaction loses 77% of named entities (people, decisions, tools, dates) in production multi-agent deployments, directly degrading agent decision quality. We present a three-layer recall architecture that achieves 100% entity recovery while