Shaurya Agrawal will demonstrate how temporal knowledge graphs in Neo4j provide agents with structured, queryable memory for planning, backtracking and multi-step reasoning. You'll learn design patterns for modeling agent observations, state transitions and provenance as temporal nodes and relationships.

By the end of this session, you'll understand how to build agents that remember context across sessions, justify their reasoning with graph paths and scale to production workloads with testable memory fidelity and retrieval latency SLOs.