AI agents can follow prompts and use tools, but often lack the institutional context needed to explain why a decision is made. That reasoning: policies, precedents, and past outcomes are usually scattered across systems and human memory.
Context graphs capture this missing layer by modeling decision traces over time, including causality and context. By giving agents access to just enough historical and organizational knowledge, context graphs enable more explainable, consistent, and auditable decisions.