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.

翻译：第六代（6G）网络日益增长的复杂性要求远超传统优化方法和当前基于AI的资源管理范式的自主能力。尽管智能体AI已成为自主无线接入网（RAN）中的一种有前途的范式，但现有框架虽提供复杂的推理能力，却缺乏经验性验证与自我改进的机制。本文指出，仿真回环验证是实现真正自主网络的关键使能技术——AI智能体可据此实证验证决策并从结果中学习。我们首次提出一种反思驱动的自优化框架，该框架以闭环架构将智能体AI与高保真网络仿真相集成。系统协调四个专业化智能体（包括场景智能体、求解器智能体、仿真智能体与反思智能体）协同工作，将智能体AI转化为具备自我修正能力的系统，使其能够逃离局部最优、识别隐式用户意图并适应动态网络条件。大量实验验证了该方法相比非智能体方案具有显著性能提升：干扰优化中吞吐量提升17.1%，通过意图识别使QoS用户满意度提升67%，低流量时段在保持服务质量的同时资源利用率降低25%。