Literature review demonstrates multi-agent language models improve control in complex telecommunication networks, highlighting pathways toward trustworthy autonomous operations.
The growing complexity of 5G/6G, IoT, and edge networks exposes the limits of single-agent LLM control in scalability, real-time performance, and reliability. Multi-Agent Systems (MAS) combined with LLMs offer a path forward. This paper surveys state-of-the-art LLM-based MAS for network management. We propose a taxonomy that re-interprets the Centralized/Decentralized/Hybrid split under two networking-specific axes (a strict LLM-core inclusion criterion and an anchoring on the NetOps lifecycle), complemented by a role-based lens (Coordinator, Translator, Negotiator). We finally identify key challenges such as hallucination and inference latency, and outline directions towards trustworthy autonomous networks.
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Kim et al. (2026) studied this question.
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