Technology Radar
A context graph is a knowledge representation technique where decisions, policies, exceptions, precedents, evidence and outcomes are modeled as first-class connected nodes in a graph, structured for AI consumption. Where systems of record capture what happened, a context graph captures why, turning institutional reasoning buried in Slack threads, approval chains and people's heads into a queryable, machine-readable structure. This is vital for agent effectiveness; an agent handling a discount exception, for example, cannot determine whether it reflects standing policy or a one-time override and may reason incorrectly. A context graph can directly surface that provenance, enabling agents to traverse decision traces, apply relevant precedents and reason across multi-hop causal chains.
Unlike GraphRAG, which builds from static document corpora, a context graph maintains temporal validity on every edge, so superseded facts are invalidated rather than overwritten. Context graphs are worth assessing for agentic applications that require persistent memory across sessions or traceable decision reasoning.
