The integration of large language models (LLMs) into wireless networks has sparked growing interest in building autonomous AI agents for wireless tasks. However, existing approaches rely heavily on manually crafted prompts and static agentic workflows, a process that is labor-intensive, unscalable, and often suboptimal. In this paper, we propose WirelessAgent++, a framework that automates the design of agentic workflows for various wireless tasks. By treating each workflow as an executable code composed of modular operators, WirelessAgent++ casts agent design as a program search problem and solves it with a domain-adapted Monte Carlo Tree Search (MCTS) algorithm. Moreover, we establish WirelessBench, a standardized multi-dimensional benchmark suite comprising Wireless Communication Homework (WCHW), Network Slicing (WCNS), and Mobile Service Assurance (WCMSA), covering knowledge reasoning, code-augmented tool use, and multi-step decision-making. Experiments demonstrate that \wap{} autonomously discovers superior workflows, achieving test scores of $78.37\%$ (WCHW), $90.95\%$ (WCNS), and $97.07\%$ (WCMSA), with a total search cost below $\$ 5$ per task. Notably, our approach outperforms state-of-the-art prompting baselines by up to $31\%$ and general-purpose workflow optimizers by $11.1\%$, validating its effectiveness in generating robust, self-evolving wireless agents. The code is available at https://github.com/jwentong/WirelessAgent-R2.

翻译：将大语言模型（LLMs）集成到无线网络中，引发了人们对构建面向无线任务的自主AI智能体日益增长的兴趣。然而，现有方法严重依赖人工设计的提示词和静态的智能体工作流，这一过程劳动密集、难以扩展且通常并非最优。本文提出WirelessAgent++，一个为各类无线任务自动化设计智能体工作流的框架。通过将每个工作流视为由模块化算子组成的可执行代码，WirelessAgent++将智能体设计转化为程序搜索问题，并利用领域适配的蒙特卡洛树搜索（MCTS）算法求解。此外，我们建立了WirelessBench，一个标准化的多维度基准测试套件，包含无线通信作业（WCHW）、网络切片（WCNS）和移动业务保障（WCMSA），覆盖知识推理、代码增强的工具使用以及多步决策制定。实验表明，\wap{} 能自主发现更优的工作流，在WCHW、WCNS和WCMSA上分别达到 $78.37\%$、$90.95\%$ 和 $97.07\%$ 的测试得分，且每项任务的总搜索成本低于 $\$ 5$。值得注意的是，我们的方法优于最先进的提示词基线高达 $31\%$，并优于通用工作流优化器 $11.1\%$，验证了其在生成鲁棒、自演进的无线智能体方面的有效性。代码发布于 https://github.com/jwentong/WirelessAgent-R2。