Context Is the Missing Layer AI Agents Need (www.graphlit.com)

Foundation Capital's recent article, "Context Graphs: AI's Trillion-Dollar Opportunity," by Jaya Gupta and Ashu Garg, highlights a transformative shift in enterprise AI towards capturing decision traces—detailed records of how and why decisions are made—rather than simply augmenting existing systems like Salesforce and SAP. They argue that the next wave of successful AI platforms will center on creating context graphs, which serve as living records that stitch together decision traces over time. This represents a significant advancement in understanding organizational decision-making processes, moving beyond mere data storage to a more dynamic model that includes past interactions and policy evaluations. For AI/ML professionals, the authors emphasize the need for two foundational layers—operational context and decision context. Operational context involves understanding organizational identity, relationships, and historical states, enabling agents to grasp their environment fully. Meanwhile, decision context builds on this foundation by detailing the specific inputs and policies that influenced decisions. The article points out that current solutions like retrieval-augmented generation (RAG) and AI memory platforms fall short in addressing the operational context, as they fail to model the essential relationships and temporal dynamics within organizations. By developing context graphs and emphasizing the need for standardized decision traces, the AI community can enhance the efficacy of autonomous agents and advance the governance of AI decision-making.