Turning Agent Memory Into Skills That Work — Will Lyon, Neo4j

This post introduces the Neo4j Agent Memory Service (NAMS) as a solution to the 'amnesiac loop' of AI agents. It posits that retrieval alone is insufficient for actionable knowledge and proposes a context graph model for agent memory, encompassing short-term, long-term, and reasoning memory. Key components include entity resolution, shared ontologies, and decision traces for inter-agent learning. The core contribution is the distillation of 'skills' from this memory, defining skills as typed execution graphs with rigorous checks for grounding, coverage, and coherence, and demonstrating this process with a healthcare patient-intake skill.