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

Agents finish a task, then forget what they learned. Retrieval alone won't fix that.

Will Lyon, senior product manager at Neo4j, explains why agent memory built on embeddings and retrieval isn't enough: recall isn't actionable knowledge. Agents need knowledge that is connected, typed and traceable. He shows Neo4j's model of agent memory as a context graph with short-term, long-term and reasoning memory, covering entity extraction and resolution, shared ontologies, and decision traces that let many agents learn from each other. Then he turns memory into action: distilling grounded, governed skills from the graph, based on Neo4j research that treats skills as typed execution graphs, with checks for grounding, coverage and coherence, and alerts when a skill goes stale. He demos the Neo4j Agent Memory Service distilling a patient-intake skill from a healthcare conversation.

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