Transparent Multi-Agent AI with Neo4j

Session Track: Data Intelligence

Session Time: 07:30 am - 08:00 am November 6

Session description

Multi-agent AI systems often operate as mysterious "black boxes," making it difficult for developers and users to understand decision-making processes, trace knowledge sources, or debug unexpected behaviors. This session demonstrates how to build transparent, explainable multi-agent systems using Neo4j's capabilities and visualization. You'll learn how to: • Design multi-agent architectures • Implement Neo4j GraphRAG to provide transparent knowledge retrieval and reasoning paths • Visualize agent decision-making processes through graph relationships • Track knowledge provenance from source documents to final answers • Debug and optimize agent interactions using graph-based insights • Build real-time dashboards showing agent collaboration and knowledge flow This will be a live demo of a complete multi-agent system featuring specialized agents (research, analysis, code generation) coordinated by a supervisor, with Neo4j GraphRAG providing transparent knowledge access. Attendees will see live demonstrations of agents collaborating, making decisions, and presenting their reasoning through interactive graph visualizations. Walk away with practical patterns for building trustworthy, explainable AI systems that developers and users can understand, debug, and improve.