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.
The text is the source's own description of its publication. The content belongs to AI Engineer.
More from AI Engineer
Inside Arca's Long-Horizon Agents That Run the Business - Aman Singh, Arca
AI Engineer 23 Oct
Real AI Agent Attacks and What Would Have Stopped Them — Andrey Rybka, Bloomberg
AI Engineer 23 Oct
Outside of tech there has been no financial payoff to US companies for using AI, now viewed as a margin-hurting cost center, as VC-backed firms burn more tokens
Techmeme 4 h ago
Source: Replit recently moved most AI spend from Anthropic to OpenAI, which gave 30%+ off in a deal worth $100M+/year, after Anthropic discontinued its discount
Techmeme 6 h ago