NODES AI 2026 – Agentic AI Governance: Data Foundations & Real-Time Policy Enforcement

Join William Liang and Murthy Chandrapaty at NODES AI for this session: "Agentic AI Governance: Data Foundations & Real-Time Policy Enforcement".

As agentic AI advances, new architectural approaches are needed for control, governance, and compliance at scale. This session details the engineering required for trustworthy, production-ready AI. Learn to view governance not just as compliance, but as a technical stack grounded in solid Data Foundations and Real-Time Policy Enforcement.

Adobe Experience Platform (AEP) enforces data policies with a Neo4j-based governance graph, evaluating real-time compliance at scale across complex data hierarchies.

We present two architectural innovations

1. Data Foundations: How Adobe moved from a document-centric (Cosmos DB) to a property-centric Neo4j graph, making policies and labels first-class nodes.

2. Real-Time Policy Enforcement: See how agentic AI translates natural language into Cypher, runs queries on governance graphs, and explains results. Learn schema-aware prompt engineering for reliable LLM-generated Cypher and agentic workflows (LLM + Neo4j + external context) for graph debugging.

You will learn

• Model governance policies as property-centric graphs
• Schema-aware prompt engineering for scalable, reliable LLM-generated Cypher
• Multi-tool agentic workflows (LLM + Neo4j + external data) for graph debugging
• Patterns for deep inheritance traversals and policy evaluation at scale