The escalating complexity of sixth-generation (6G) networks demands unprecedented levels of autonomy beyond the capabilities of traditional optimization-based and current AI-based resource management approaches. While agentic AI has emerged as a promising paradigm for autonomous RAN, current frameworks provide sophisticated reasoning capabilities but lack mechanisms for empirical validation and self-improvement. This article identifies simulation-in-the-loop validation as a critical enabler for truly autonomous networks, where AI agents can empirically verify decisions and learn from outcomes. We present the first reflection-driven self-optimization framework that integrates agentic AI with high-fidelity network simulation in a closed-loop architecture. Our system orchestrates four specialized agents, including scenario, solver, simulation, and reflector agents, working in concert to transform agentic AI into a self-correcting system capable of escaping local optima, recognizing implicit user intent, and adapting to dynamic network conditions. Extensive experiments validate significant performance improvements over non-agentic approaches: 17.1\% higher throughput in interference optimization, 67\% improved user QoS satisfaction through intent recognition, and 25\% reduced resource utilization during low-traffic periods while maintaining service quality.
@article{arxiv.2512.20640,
title = {Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows},
author = {Yunhao Hu and Xinchen Lyu and Chenshan Ren and Keda Chen and Qimei Cui and Xiaofeng Tao},
journal= {arXiv preprint arXiv:2512.20640},
year = {2026}
}