Agentic AI for Ultra-Modern Networks: Multi-Agent Framework for RAN Autonomy and Assurance

2025-10-17

AI Summary

To address risks—including policy conflicts, data drift, and unsafe decision-making under unforeseen conditions—arising from the centralized RIC control paradigm in O-RAN, this paper proposes a multi-agent distributed autonomous architecture for 6G, replacing conventional RIC orchestration. The method decouples and synergistically integrates policy generation, formal verification, and dynamic deployment, incorporating predictive modeling, distributed control, and explainable verification mechanisms. Empirical evaluation across four key performance indicators—RRC-connected user count, IP throughput, PRB utilization, and SINR—demonstrates that the architecture enables real-time blocking of high-risk policies, thereby ensuring global network stability and security. Results show statistically significant performance improvements over a single-predictor baseline, validating its efficacy in adaptive, safety-critical wireless network management.

Technology Category

• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The increasing complexity of Beyond 5G and 6G networks necessitates new paradigms for autonomy and assur- ance. Traditional O-RAN control loops rely heavily on RIC- based orchestration, which centralizes intelligence and exposes the system to risks such as policy conflicts, data drift, and unsafe actions under unforeseen conditions. In this work, we argue that the future of autonomous networks lies in a multi-agentic architecture, where specialized agents collaborate to perform data collection, model training, prediction, policy generation, verification, deployment, and assurance. By replacing tightly- coupled centralized RIC-based workflows with distributed agents, the framework achieves autonomy, resilience, explainability, and system-wide safety. To substantiate this vision, we design and evaluate a traffic steering use case under surge and drift conditions. Results across four KPIs: RRC connected users, IP throughput, PRB utilization, and SINR, demonstrate that a naive predictor-driven deployment improves local KPIs but destabilizes neighbors, whereas the agentic system blocks unsafe policies, preserving global network health. This study highlights multi- agent architectures as a credible foundation for trustworthy AI- driven autonomy in next-generation RANs.

Problem

• Replacing centralized RAN control with distributed multi-agent systems
• Addressing policy conflicts and safety risks in autonomous networks
• Ensuring global network resilience under unpredictable traffic conditions

Innovation

• Multi-agent architecture replaces centralized RAN orchestration
• Distributed agents enable autonomy resilience and system safety
• Specialized agents collaborate for prediction and policy verification