Technical depth · Knowledge graphs
A knowledge graph is not merely a retrieval accessory for an LLM. It can become the connected state and reasoning layer for AI agents, causal analysis and context engineering.
Four roles
The graph creates reusable structure: entities have stable identities, relationships carry meaning, evidence retains provenance and multiple AI components can work from the same governed foundation.
An agent can traverse the graph to understand what exists, what changed, what depends on what and which action is permitted.
A graph can represent candidate causes, effects, confounders, mechanisms and intervention points, then connect them to the evidence used for causal analysis.
Important: a relationship in a knowledge graph does not prove causality. Causal claims require an explicit causal model, identification assumptions, suitable data and refutation or sensitivity testing.
Context engineering is the continual selection of the most useful information for a model or agent. A graph helps assemble that context by relationship, time, authority and task.
Graph algorithms use topology as data. They reveal influence, clusters, bridges, suspicious structures, likely links and efficient routes that tabular features can miss.
Continue the technical journey
The companion page explains how to model, ingest, index and analyse very large graphs—and how to select graph algorithms for the decision at hand.
Explore knowledge graphs at scale →Design principle
RAG is one consumer. Agents, causal models, context assemblers, analytical pipelines and human investigators can all work from the same connected foundation—provided identity, semantics, provenance and access are engineered deliberately.
Discuss a knowledge-graph architecture →Primary references
Start with the decisions, graph topology, evidence and workload—not the technology label.