arXiv:2607.07721v1 Announce Type: cross 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 Detec
Advances in RAG and agentic frameworks have matured sufficiently to highlight the limitations of reactive AI, prompting a push towards proactive, enterprise-focused solutions. The paper indicates a natural evolution from current successful, yet constrained, AI implementations.
This development proposes a fundamental shift from reactive to proactive AI agents, potentially unlocking significant productivity gains for businesses by anticipating needs rather than merely responding to queries. It suggests a future where AI autonomously surfaces critical insights and actions.
Enterprise AI systems will transition from tools that wait for human input to intelligent collaborators that actively contribute by monitoring, analyzing, and proactively presenting actionable information. This changes the interaction paradigm from pull to push.
- · Enterprise software providers
- · Businesses adopting proactive AI
- · Data integration platforms
- · AI/ML developers
- · Legacy reactive enterprise systems
- · Businesses slow to adopt AI agency
- · Manual data analysis services
Enterprise AI agents gain the ability to monitor and anticipate needs, moving beyond simple query responses.
Increased efficiency and collapsed white-collar workflows emerge as agents autonomously identify and present actionable insights.
The definition of 'employee productivity' fundamentally shifts, requiring new organizational structures and human-AI collaboration models.
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Read at arXiv cs.LG