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This article explains how current agent memory systems store and retrieve facts but fail to recognize patterns, and introduces Zep's Observations feature which analyzes knowledge graphs to derive structural insights.
Explains why production AI agents require a three-tier persistent memory stack—session logs (Zep), personalization (Mem0), and governed context (ContextNest)—to avoid retrieval of stale or conflicting facts.
Explains how to fix agent memory by defining an ontology using Pydantic schemas, enabling structured extraction into knowledge graphs for multi-hop reasoning, with an open-source solution (Zep).