KP-A: A Unified Network Knowledge Plane for Catalyzing Agentic Network Intelligence

TL;DR

KP-A introduces a unified Network Knowledge Plane that streamlines knowledge management for agentic network intelligence, improving interoperability and reducing complexity in autonomous 6G networks.

Contribution

It proposes a novel unified knowledge plane architecture that decouples knowledge management from intelligence logic, enhancing interoperability and simplifying development.

Findings

Improves knowledge retrieval efficiency in network intelligence tasks.

Enhances interoperability among network intelligence agents.

Reduces development and maintenance complexity.

Abstract

The emergence of large language models (LLMs) and agentic systems is enabling autonomous 6G networks with advanced intelligence, including self-configuration, self-optimization, and self-healing. However, the current implementation of individual intelligence tasks necessitates isolated knowledge retrieval pipelines, resulting in redundant data flows and inconsistent interpretations. Inspired by the service model unification effort in Open-RAN (to support interoperability and vendor diversity), we propose KP-A: a unified Network Knowledge Plane specifically designed for Agentic network intelligence. By decoupling network knowledge acquisition and management from intelligence logic, KP-A streamlines development and reduces maintenance complexity for intelligence engineers. By offering an intuitive and consistent knowledge interface, KP-A also enhances interoperability for the network intelligence agents. We demonstrate KP-A in two representative intelligence tasks: live network knowledge Q&A and edge AI service orchestration. All implementation artifacts have been open-sourced to support reproducibility and future standardization efforts.