How Context Graphs Power the Next Generation of Autonomous Agents
Autonomous agents are only as effective as the context in which they operate. Many enterprise AI initiatives struggle to scale beyond pilots due to siloed data, inconsistent outputs, and limited reasoning. Without predictable outcomes, it’s difficult to drive measurable business impact.
In this webinar, we’ll explore how context graphs enable more reliable and explainable AI systems. Picture an agent drawing on a wealth of institutional knowledge, historical decision-making, and business data to act rationally and avoid repeating past mistakes. You’ll learn how to evolve your data architecture to support next-generation AI outcomes.
See how Electronic Arts achieved 10× faster time-to-insight and improved agent reliability by adopting a graph-based approach to context engineering.
Join us to discover how to transition from simple context windows to implementing context graphs within your organization.
SPEAKER
Jim Webber
Chief Scientist, Neo4j
Dr. Jim Webber is Neo4j’s Chief Scientist and Visiting Professor at Newcastle University. At Neo4j, Jim leads the Systems Research Group, working on a variety of database research topics with a focus on fault-tolerance. He also co-authored several books on graph technology including Graph Databases (1st and 2nd editions, O’Reilly), Graph Databases for Dummies (Wiley), and Building Knowledge Graphs (O’Reilly). Prior to Neo4j, Jim worked on fault-tolerant distributed systems. First at Newcastle University startup Arjuna and then for a variety of clients for global consulting firm ThoughtWorks. Along the way Jim co-authored the distributed systems books REST in Practice (O’Reilly) and Developing Enterprise Web Services - An Architect’s Guide (Prentice Hall).
