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.

翻译：超越5G和6G网络日益增长的复杂性对自主性和保障性提出了新的范式需求。传统的O-RAN控制环路严重依赖基于RIC的编排，这种集中式智能架构使系统面临策略冲突、数据漂移以及在不可预见条件下执行不安全操作等风险。本文认为，自主网络的未来在于多智能体架构，其中专业化智能体通过协作完成数据收集、模型训练、预测、策略生成、验证、部署及保障任务。通过以分布式智能体替代紧密耦合的集中式RIC工作流，该框架实现了自主性、弹性、可解释性及系统级安全性。为验证这一构想，我们设计并评估了流量激增与数据漂移场景下的流量疏导用例。针对RRC连接用户数、IP吞吐量、PRB利用率和SINR四项关键绩效指标的实验结果表明：简单的预测驱动部署虽能提升局部指标，却会导致相邻节点失稳；而智能体系统能够拦截不安全策略，从而保障整体网络健康。本研究论证了多智能体架构可作为下一代无线接入网中可信赖人工智能驱动自主系统的可靠基础。