Agentic GraphRAG

Preface

The gap between a polished agent demo and a production system you’d stake a service-level agreement on is wider than most teams realize. A single-shot chatbot looks fine in a screenshare but lacks production-level capability. A prompt-chained agent hits a wall the moment it has to carry state across a dozen steps, reconcile information from conflicting sources, or act inside a system where the wrong API call costs real money.

We wrote this book because we kept watching capable engineers run into the same wall and reach for the same tools: more context, a bigger model, a longer prompt, or another framework. The scaffolding gets taller; the failure modes just shift up a floor.

Graph-based architectures cut through that loop, not because graphs are magic (they aren’t) but because they make the two things agents most often get wrong inspectable: what the agent knows and what the agent is doing. Once you see those as distinct structures instead of a blob of tokens streaming through a prompt window, a lot of agent reliability problems start looking like plain process design problems. Those, as a field, we do know how to solve.

This is a genuinely exciting moment to be building in this space. The underlying models keep improving. Knowledge graph tooling has matured past its academic phase and into something you can actually deploy. The integration patterns that bind them together, such as GraphRAG, the dual-graph architecture, and semantic backpropagation, are finally concrete ...