Context Graphs for Proactive Enterprise Agents

Researchers propose Context Graphs to enable proactive enterprise agents that surface actionable information before users ask, achieving Precision@5 of 0.83 and reducing mean time to surface from 47 minutes to under 30 seconds in case studies on contract management, incident response, and sales pipeline hygiene.

arXiv:2607.07721v1 Announce Type: new Abstract: Retrieval-Augmented Generation RAG and agentic frameworks have advanced enterprise AI considerably, yet agents remain fundamentally reactive: they wait for a human query before acting. This paper argues that genuine enterprise productivity gains require proactive agents: systems that surface relevant, actionable information to workers before they ask. We propose the Context Graph, a live relational data structure that models enterprise entities, their relationships, and state transitions over time. Built on this graph, we define a Delta Detection Engine that continuously monitors state changes, a Proactivity Scorer that ranks candidate insights by urgency, relevance, and persona-fit, and a Surfacing Layer powered by an LLM that delivers ranked notifications with grounded explanations. We formalize each component, derive a unified Proactivity Score function, and provide a complete end-to-end Python implementation using NetworkX and the Anthropic Claude API. Evaluation across three generic enterprise case studies contract lifecycle management, engineering incident response, and sales pipeline hygiene demonstrates that context-graph-driven proactivity achieves Precision@5 of 0.83, a false positive rate of 0.11, and reduces mean time to surface from 47 minutes reactive baseline to under 30 second.