Agentic GraphRAG

Chapter 2. Agentic Graph Architecture Foundations

Your AI system sees the world as floating text fragments. It can tell you that two paragraphs are semantically similar. But ask it whether Service A depends on Service B, whether that dependency was introduced last Tuesday, or whether removing it will cascade through four downstream services, and it draws a blank.

That is the gap that Chapter 1 diagnosed as a representation problem rather than a model bottleneck. Vector RAG treats everything as points in embedding space. It knows that things are near each other but not how they’re connected. The five flaws (context amnesia, relationship blindness, temporal ignorance, reasoning paralysis, and tool chaos) are all symptoms of this underlying representation problem. You cannot reason about relationships that don’t exist in your data. You cannot plan across dependencies that are invisible to your retrieval system. You cannot learn from experience when every query starts from scratch.

This chapter introduces the architecture that closes the gap. We will start with the conceptual shift that makes it possible: moving from strings to things, from flat text embeddings to structured entities with explicit relationships. From there, we will introduce the dual-graph architecture, the central framework of this book, which pairs a vertical knowledge graph with a horizontal workflow graph. We will preview the eight pillars of implementation that turn this architecture into a working system, mapping ...