At HumanX, Ryan is joined by Philip Rathle, CTO at Neo4j to discuss what knowledge context means for AI agents, how limitations like stale training data make the model-only approach to agents a bad fit for enterprise environments, and how Graph RAG raises the bar for accuracy and reduces context rot by combining vectors with a knowledge graph so agents are more targeted and connected.Episode notes:<a href="https://neo4j.com/" rel="noopener noreferrer" target="_blank">Neo4j</a> is a native graph database management system designed to handle complex, highly-connected data by focusing on relationships rather than tables. You can try it out for free on <a href="https://neo4j.com/product/auradb/" rel="noopener noreferrer" target="_blank">Aura</a> and learn more at their <a href="https://graphacademy.neo4j.com/categories/workshops/" rel="noopener noreferrer" target="_blank">Graph Academy</a>.&nbsp;Connect with Philip on <a href="https://www.linkedin.com/in/prathle/" rel="noopener noreferrer" target="_blank">LinkedIn</a>.&nbsp;See Privacy Policy at <a href="https://art19.com/privacy" rel="noopener noreferrer" target="_blank">https://art19.com/privacy</a> and California Privacy Notice at <a href="https://art19.com/privacy#do-not-sell-my-info" rel="noopener noreferrer" target="_blank">https://art19.com/privacy#do-not-sell-my-info</a>.

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