Context Graphs: Proactive Enterprise Agents, Precision@5 0.83

Avinash Kumar submitted the paper "Context Graphs for Proactive Enterprise Agents" to arXiv on 4 Jul 2026. The paper defines a live Context Graph plus a Delta Detection Engine, a Proactivity Scorer and a Surfacing Layer, provides a Python implementation using NetworkX and the Anthropic Claude API, and reports Precision@5 of 0.83, a false positive rate of 0.11, and mean time to surface reduced from 47 minutes to under 30 second.

What is a Context Graph and how does the system work?

A Context Graph is a live relational data structure that models enterprise entities, their relationships, and state transitions over time. Built on this graph the paper describes 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.

The implementation is end-to-end in Python and uses NetworkX to manage the graph and the Anthropic Claude API to power the Surfacing Layer. The author formalizes each component and derives a unified Proactivity Score function to combine urgency, relevance and persona-fit into ranked candidate insights.

How well did the Context Graph approach perform in evaluations?

Across three generic enterprise case studies the Context Graph approach achieved Precision@5 of 0.83, a false positive rate of 0.11, and reduced mean time to surface from 47 minutes (reactive baseline) to under 30 second. The three evaluated domains are contract lifecycle management, engineering incident response and sales pipeline hygiene.

Those numbers are reported as evaluation results in the paper and frame the claimed gains in both accuracy and latency compared with a reactive baseline that waits for human queries. The Precision@5 metric indicates that among the top five surfaced items, 83 percent were judged correct for the task contexts used in the experiments.

Why does proactive surfacing matter for enterprises?

Proactive surfacing shifts agents from passively answering queries to continuously detecting and ranking changes that matter to workers. That reduces the time between a state change and a human becoming aware, which the paper quantifies by comparing a 47 minute reactive baseline to under 30 second surfacing with the Context Graph pipeline. For workflows such as contract handoffs, incident triage or sales hygiene, faster surfacing can shorten decision loops and lower the risk of missed actions.

The paper also ties proactivity to persona-fit: the Proactivity Scorer ranks insights not only by urgency and relevance but by how well they match a recipient role. That design aims to reduce noisy or irrelevant notifications, consistent with the reported false positive rate of 0.11.

What are the components developers would need to reproduce this?

The core pieces are the Context Graph data model, a Delta Detection Engine to detect state transitions, a Proactivity Scorer implementing the Proactivity Score function, and a Surfacing Layer backed by an LLM to generate explanations and notifications. The author supplies a Python implementation that uses NetworkX for the graph layer and the Anthropic Claude API for the Surfacing Layer, tying the components together end-to-end.

What to watch

Adoption and replication: whether independent deployments in contract lifecycle management, engineering incident response and sales pipeline hygiene can reproduce the reported Precision@5 of 0.83 and the under 30 second mean time to surface. Also watch for published code or artifacts from the paper's Python implementation using NetworkX and the Anthropic Claude API that would enable broader validation.