The Architecture of Agentic Memory
How AI systems will remember
TL;DR
AI agents need three types of memory to be truly useful: working memory (current context), episodic memory (past experiences), and semantic memory (world knowledge). This article explores how each type maps to technical implementations and why getting memory right is the key unlock for production agents.
Introduction
Memory is the missing piece of the AI agent puzzle. Current agents are brilliant in the moment but amnesiac across sessions. This deep dive explores the three memory architectures that will change that.
Working Memory: The Context Window
Working memory is the context window — the tokens an LLM can process in a single pass. Larger context windows enable longer conversations but don't solve the persistence problem.
Episodic Memory: Learning from Experience
Episodic memory stores specific past experiences — conversations, tool calls, outcomes. It enables agents to learn from what worked and what didn't without retraining.
Semantic Memory: Persistent Knowledge
Semantic memory is the agent's long-term knowledge base — facts, relationships, and domain expertise that persist across all interactions. RAG is one implementation, but not the only one.