Memory at Scale: How Production AI Agents Actually Remember

As AI agents move from prototyping to production, maintaining accurate, time-aware memory becomes critical—but vector databases alone cannot track temporal context or handle fact updates. Sistava's seven-layer architecture demonstrates why production-scale systems require separation between working memory, episodic knowledge, semantic facts, and procedural skills to prevent hallucination and maintain consistency across long-running operations.