# WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks arXiv: https://arxiv.org/abs/2603.00501 ยท HTML: https://mlsanity.com/paper/2603.00501/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use. - Builds on 26 of its 37 references that are in the map. - First posted on arXiv 28 Feb 2026. ## Placed in - LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/) ## Builds on (most cited first, 26 of 26) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023) - [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023) - [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024) - [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024) - [Automated Design of Agentic Systems](https://mlsanity.com/paper/2408.08435/) (2024) - [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023) - [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019) - [AutoFlow: Automated Workflow Generation for Large Language Model Agents](https://mlsanity.com/paper/2407.12821/) (2024) - [AutoML-GPT: Automatic Machine Learning with GPT](https://mlsanity.com/paper/2305.02499/) (2023) - [NetLLM: Adapting Large Language Models for Networking](https://mlsanity.com/paper/2402.02338/) (2024) - [TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge](https://mlsanity.com/paper/2310.15051/) (2023) - [WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence](https://mlsanity.com/paper/2405.17053/) (2024) - [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024) - [WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks](https://mlsanity.com/paper/2505.01074/) (2025) - [AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks](https://mlsanity.com/paper/2508.17778/) (2025) - [Fantastic Bugs and Where to Find Them in AI Benchmarks](https://mlsanity.com/paper/2511.16842/) (2025) - [Agentic TinyML for Intent-aware Handover in 6G Wireless Networks](https://mlsanity.com/paper/2508.09147/) (2025) - [Agoran: An Agentic Open Marketplace for 6G RAN Automation](https://mlsanity.com/paper/2508.09159/) (2025) - [An LLM-based Agentic Framework for Accessible Network Control](https://mlsanity.com/paper/2509.20600/) (2025) ## Built on by (most cited first, 7 of 7) - [QoEReasoner: An Agentic Reasoning Framework for Automated and Explainable QoE Diagnosis in RANs](https://mlsanity.com/paper/2606.01925/) (2026) - [BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding](https://mlsanity.com/paper/2603.18855/) (2026) - [Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models](https://mlsanity.com/paper/2605.02911/) (2026) - [RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation](https://mlsanity.com/paper/2606.01862/) (2026) - [WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents](https://mlsanity.com/paper/2608.14573/) (2026) - [Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role](https://mlsanity.com/paper/2608.26093/) (2026) - [AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC](https://mlsanity.com/paper/2609.39964/) (2026) ## Abstract (arXiv) 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.