让企业智能代理主动提醒关键信息，提升工作效率。
Context Graphs for Proactive Enterprise Agents
- 构建动态关系图谱，实时追踪企业实体状态变化。
- 主动发现并排序高价值信息，准确率83%，响应时间缩短至30秒内。
- 适合需要提前预警的企事业场景，如合同管理与故障响应。
检索增强生成（RAG）与智能体框架虽显著推动了企业AI发展，但现有系统仍以被动响应为主——只有在收到人类查询后才行动。本文主张真正的企业效率提升依赖于主动型智能体：能在用户提问前主动呈现相关且可操作的信息。为此，我们提出上下文图（Context Graph），一种实时建模企业实体、其关系及随时间演化的状态转换的动态数据结构。基于此图，我们设计了差分检测引擎持续监控状态变更，主动度评分器依据紧迫性、相关性和用户角色匹配度对潜在洞察进行排序，并通过大模型驱动的浮现层生成带可信解释的优先通知。我们形式化定义各组件，推导统一的主动度评分函数，并提供基于NetworkX与Anthropic Claude API的完整端到端Python实现。在三个通用企业案例（合同生命周期管理、工程事故响应、销售管道健康度维护）中的评估显示，基于上下文图的主动性方案达到Precision@5为0.83，误报率为0.11，平均信息浮现时间从原始反应式基线的47分钟降至30秒以下。
原文摘要 · Abstract (English)
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.
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