# ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks

arXiv: https://arxiv.org/abs/2601.19607 · HTML: https://mlsanity.com/paper/2601.19607/

## Key facts

- Cited by 8 papers in the map (data through 7 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 27 Jan 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, 10 of 10)

- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Large Language Model Enhanced Multi-Agent Systems for 6G Communications](https://mlsanity.com/paper/2312.07850/) (2023)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions](https://mlsanity.com/paper/2308.06250/) (2023)
- [Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends](https://mlsanity.com/paper/2409.14457/) (2024)
- [WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks](https://mlsanity.com/paper/2505.01074/) (2025)
- [A Survey of Recent Advances in Optimization Methods for Wireless Communications](https://mlsanity.com/paper/2401.12025/) (2024)
- [A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms and Open Problems](https://mlsanity.com/paper/1609.04069/) (2016)
- [Wireless Agentic AI with Retrieval-Augmented Multimodal Semantic Perception](https://mlsanity.com/paper/2505.23275/) (2025)
- [Joint User Association and Beamforming Design for ISAC Networks with Large Language Models](https://mlsanity.com/paper/2506.05637/) (2025)

## Built on by (most cited first, 8 of 8)

- [GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing](https://mlsanity.com/paper/2605.27360/) (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)
- [Executor-Side Progressive Risk-Gated Actuation for Agentic AI in Wireless Supervisory Control](https://mlsanity.com/paper/2605.02697/) (2026)
- [Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models](https://mlsanity.com/paper/2606.31334/) (2026)
- [WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents](https://mlsanity.com/paper/2608.14573/) (2026)
- [FL-MAESTRO: Multi-Agent LLM Orchestration for Resource-Constrained Federated Learning](https://mlsanity.com/paper/2608.20518/) (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)

Emerging 6G networks rely on complex cross-layer optimization, yet manually translating high-level intents into mathematical formulations remains a bottleneck. While Large Language Models (LLMs) offer promise, monolithic approaches often lack sufficient domain grounding, constraint awareness, and verification capabilities. To address this, we present ComAgent, a multi-LLM agentic AI framework. ComAgent employs a closed-loop Perception-Planning-Action-Reflection cycle, coordinating specialized agents for literature search, coding, and scoring to autonomously generate solver-ready formulations and reproducible simulations. By iteratively decomposing problems and self-correcting errors, the framework effectively bridges the gap between user intent and execution. Evaluations demonstrate that ComAgent achieves expert-comparable performance in complex beamforming optimization and outperforms monolithic LLMs across diverse wireless tasks, highlighting its potential for automating design in emerging wireless networks.