Road to NODES AI - Build Grounding Agents with Microsoft Agent Context Provider

​This hands-on lab is designed to demonstrate how to build LLM-powered agents grounded in a Neo4j knowledge graph, without relying on traditional retrieval tools or custom orchestration code. Instead of defining tools and debugging when (or if) they are invoked, you will configure a Neo4j Context Provider that injects graph context automatically before every LLM call.

​The workshop walks through three progressive configurations. You will start with full-text search using a Neo4j index to provide immediate, structured context to the agent. Next, you will extend the same pattern to vector search with embeddings for semantic matching. Finally, you will implement graph-enriched retrieval queries that traverse relationships and return connected context that goes beyond what indexes alone can provide.

​Through guided exercises, you will build a working self-grounding agent that consistently receives relevant knowledge from a graph on every interaction. You will leave with practical configuration patterns you can adapt to any Neo4j schema and apply to real-world agent architectures.

​You will learn:

• ​How to configure the Neo4j Context Provider for fulltext and vector search

• ​How to write retrieval queries that traverse graph relationships

• ​How to tune key parameters such as top_k, message_history_count, and index selection

• ​How to debug and validate context injection by inspecting what the agent receives