Overview
This workshop is designed to show you why most agent memory stops at recall - you embed the transcript, retrieve the nearest chunks, and hope the model reconstructs what it already figured out last week. It usually doesn't, because the conversation is retrievable, but the knowledge was never structured in the first place. You will close that gap by working hands-on against a running Neo4j Agent Memory Service (NAMS) instance, building a memory layer that holds three distinct kinds of memory in one graph: short-term conversation history, long-term entities and relationships modeled with POLE+O, and reasoning memory that captures decision traces and tool-call provenance as first-class nodes.
