Ask your agent "who do I know connected to flights to Spain?" and vector search returns Iberia, Madrid and Spain as three disconnected pieces. The pieces are there; nothing joins them. Store what the agent knows, what it did, and why as graphs, and traversal answers the question: in demos you can rerun, multi-hop goes from 1 of 4 to 4 of 4, and the reverse audit ("this source was wrong, what did it touch?") from 2 of 4 to 4 of 4, with the provenance path as the receipt.

What you'll learn:
• Identify the three graphs already hiding in any agent system: context, execution, and provenance
• Understand why vector similarity structurally cannot answer multi-hop questions, and why traversal can
• Store agent memory as a Neo4j knowledge graph and combine similarity (entry point) with Cypher traversal (answer)
• Capture decision traces automatically with framework lifecycle hooks, with zero changes to your tools
• Run the reverse audit a flat store cannot express: follow provenance at any depth with one variable-length Cypher query

Outline:
• The claim: your agent is already a graph
• Context Graph: multi-hop questions need edges
• Execution + Provenance Graphs: remember WHY, audit in reverse
• Close the loop