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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.
This paper identifies the retention-forgetting dilemma in verbal reinforcement learning for LLM agents operating in non-stationary environments, and proposes a three-layer architecture with a feedback-driven curation loop to govern insight extraction and application.