Context Graphs Enable Proactive Enterprise AI Agents

Current enterprise AI agents, often built on Retrieval-Augmented Generation (RAG) frameworks, are largely reactive, waiting for user input before providing information. This research proposes a shift towards proactive agents through the introduction of "Context Graphs." These graphs are dynamic relational data structures that model enterprise entities, their relationships, and how their states change over time. The goal is to deliver actionable insights to workers *before* they explicitly ask for them. The proposed system comprises several key components: a Delta Detection Engine that continuously monitors state changes within the graph, a Proactivity Scorer that ranks potential insights based on urgency, relevance, and user persona, and an LLM-powered Surfacing Layer that delivers these ranked notifications with clear explanations. The paper formalizes each component and provides a Python implementation. Evaluations across various enterprise scenarios, including contract management and sales pipeline hygiene, show that this context-graph-driven approach significantly reduces the time to surface relevant information, improving efficiency.