Context Graphs to bridge the Decision Intelligence gap and build a Digital Twin

We present the vision of the context graph as a unified layer of institutional knowledge to bridge the decision intelligence gap in an enterprise. Today, with the rise of large language models and agentic AI, we see high adoption rate of agents to perform diverse enterprise tasks. However, for these agents to deliver exponential value, it is especially important to curate business context and provide it in a structure that agents can consume. This is where the context layer and the context graph play a significant role. The context graph curates knowledge from different source systems, data lake-houses, business documents, and institutional knowledge maintained in the Enterprise in different forms. Using agentic perception, this knowledge is analyzed, connected to each other, and a dynamic context graph is developed. The dynamic nature of the graph helps adapt to ever-changing business dynamics and proactively respond with right course of action. Given the right context, the agents are empowered to make decisions and drive value beyond traditional automation with high-value reasoning and decision making. A key element of the context graph is decision intelligence. Historical decision traces and reasoning steps of agents that have driven decisions, serve as very useful knowledge which is often not considered while bringing context together. We envisage decision and reasoning traces as a key element of the context graph and show how these can ground future decisions made by agents. Taking an example of a finance digital twin, curating domain knowledge using an open knowledge format, we represent it as a graph and use this connectivity information to enable customer specific hyper personalization. We envision the context graph to grow into the core heart of the Enterprise turning into a true “moat” for the modern Enterprise.