WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks
Jingwen Tong, Zijian Li, Fang Liu, Wei Guo, Jun Zhang

TL;DR
WirelessAgent++ automates the design of wireless network workflows using program search and benchmarking, significantly improving performance over existing methods with minimal search costs.
Contribution
Introduces WirelessAgent++, a framework that automates wireless workflow design via program search, and establishes WirelessBench, a comprehensive benchmark suite for evaluation.
Findings
WirelessAgent++ achieves high test scores across benchmarks.
Outperforms prompting baselines by up to 31%.
Reduces search costs to below $5 per task.
Abstract
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…
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Taxonomy
TopicsSoftware-Defined Networks and 5G · Mobile Agent-Based Network Management · Multi-Agent Systems and Negotiation
