This paper proposes an agentic artificial intelligence (AI)-based network optimization framework that integrates mixture of experts (MoE) architectures with large language models (LLMs). The LLM acts as a semantic gate to reason over operator objectives and dynamically compose suitable optimization agents for 6G mobile networks.
The telecom industry is undergoing a massive transformation, evolving from traditional networks to intelligent, software-defined systems powered by AI/ML. This shift addresses scalability and complexity challenges in 5G, applying AI/ML for RAN optimization, anomaly detection, and traffic prediction.
The telecommunications industry faces increasing demands for connectivity, pushing traditional operational models to their limits. Agentic AI tools are transforming this sector by independently acting, coordinating systems, and optimizing network operations in real-time, moving beyond rule-based automation.