LLM-Driven Multi-Agent Architectures for Intelligent Self-Organizing Networks

Managing the growing complexity of Self-Organizing Networks (SONs) in next-generation communication systems requires agile, real-time strategies that can adapt to multidimensional and highly dynamic conditions. Traditional SON management rooted in centralized, rule-based, and static models, struggles to meet these evolving requirements. Recent advances in multi-agent systems (MAS) and Large Language Models (LLMs) enable the design of intelligent and context-aware frameworks that span multiple operational layers. In this paper, we introduce LaMA-SON, an LLM-driven MAS for intelligent SON management. Specifically, LaMA-SON incorporates specialized agents to handle three critical operational functions: traffic management, quality of service (QoS) optimization, and security threat detection. We perform a proof-of-concept evaluation using a real-world network traffic classification dataset, where traffic, security, and QoS optimization agents make decisions based on role-specific features and structured prompts. Our experimental results demonstrate that LaMA-SON achieves higher accuracy and recall while preserving balanced precision-recall trade-offs and outperforms standalone LLM baselines, highlighting the benefits of multi-agent collaboration. Finally, we highlight various open research challenges that require further investigation to fully realize the potential of LLM-based MAS frameworks in network operations management.