Designing memory systems for agentic AI is one of the most critical and underexplored challenges in the field today. As AI agents move from single-turn interactions to multi-step, multi-agent orchestration, managing what the agent remembers — and how — becomes the difference between a useful system and an unpredictable one.
In this post we explore four memory types: working memory (in-context), episodic logs (conversation history), semantic memory (vector store retrieval), and procedural memory (tool/skill libraries). We also cover hybrid retrieval strategies that combine keyword and semantic search, and evaluation metrics like memory precision, recall, and retrieval latency.