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

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.