{"slug": "context-graphs-for-proactive-enterprise-agents", "title": "Context Graphs for Proactive Enterprise Agents", "summary": "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.", "body_md": "arXiv:2607.07721v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/context-graphs-for-proactive-enterprise-agents", "canonical_source": "https://arxiv.org/abs/2607.07721", "published_at": "2026-07-10 04:00:00+00:00", "updated_at": "2026-07-10 04:09:41.438111+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-products", "ai-research"], "entities": ["Anthropic", "Claude", "NetworkX"], "alternates": {"html": "https://wpnews.pro/news/context-graphs-for-proactive-enterprise-agents", "markdown": "https://wpnews.pro/news/context-graphs-for-proactive-enterprise-agents.md", "text": "https://wpnews.pro/news/context-graphs-for-proactive-enterprise-agents.txt", "jsonld": "https://wpnews.pro/news/context-graphs-for-proactive-enterprise-agents.jsonld"}}