Forming service clusters efficiently is a primary challenge in implementing user‑oriented Open RAN architectures. This work introduces an AI Agent built on a large language model to dynamically organize and schedule resources across Open RAN cells. Simulation results indicate that the proposed solution more than doubles the median user throughput ($2\times$) compared to conventional network‑oriented designs, markedly enhancing user experience.
Review: Leveraging the reasoning capabilities of language models for adaptive cluster management showcases the promise of generative AI in wireless network optimization, meriting further validation in real‑world deployments.