Connect the Dots With Context Graphs
Stateless prompts and RAG aren’t enough for agentic AI systems to reason and make decisions. Context gets lost in prompts without persistent memory, causing agents to misinterpret information and repeatedly make mistakes.
The emergence of context graphs as “living graphs” can help you turn agent interactions into reusable memory that improves over time, enabling multi-hop, structured context assembly.
Join us on March 24 as we discuss how context graphs enable agents to search for precedents by linking decisions, tool calls, and provenance. We’ll also explore how you can use context engineering to design a context layer with policies and rationale for agentic AI. You’ll learn the importance of structured, relevant input for explainable decision paths and why context graphs are important for enterprise-scale trust.
SPEAKER
Stephen Chin
Vice President, Developer Relations
Stephen Chin is VP of Developer Relations at Neo4j and author of The Definitive Guide to Modern Client Development, Raspberry Pi with Java, Pro JavaFX Platform, and the DevOps Tools for Java Developers title from O'Reilly. He has keynoted numerous conferences around the world including AI DevSummit, Devoxx, DevNexus, JNation, JavaOne, Joker, swampUP, and Open Source India. Stephen is an avid motorcyclist who has done evangelism tours in Europe, Japan, and Brazil, interviewing hackers in their natural habitat. When he is not traveling, he enjoys teaching kids how to do AI, embedded, and robot programming together with his daughters.
