# Agentic AI for Intent-driven Optimization in Cell-free O-RAN

arXiv: https://arxiv.org/abs/2602.22539 · HTML: https://mlsanity.com/paper/2602.22539/

## Key facts

- Cited by 2 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 9 of its 14 references that are in the map.
- First posted on arXiv 26 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, 9 of 9)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks](https://mlsanity.com/paper/2508.17778/) (2025)
- [dApps: Enabling Real-Time AI-Based Open RAN Control](https://mlsanity.com/paper/2501.16502/) (2025)
- [ALLSTaR: Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN](https://mlsanity.com/paper/2505.18389/) (2025)
- [MX-AI: Agentic Observability and Control Platform for Open and AI-RAN](https://mlsanity.com/paper/2508.09197/) (2025)
- [TailO-RAN: O-RAN Control on Scheduler Parameters to Tailor RAN Performance](https://mlsanity.com/paper/2508.12112/) (2025)
- [Distributed Precoding for Cell-free Massive MIMO in O-RAN: A Multi-agent Deep Reinforcement Learning Framework](https://mlsanity.com/paper/2510.27069/) (2025)

## Built on by (most cited first, 2 of 2)

- [Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems](https://mlsanity.com/paper/2606.24416/) (2026)
- [Z2-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN](https://mlsanity.com/paper/2608.21049/) (2026)

## Abstract (arXiv)

Agentic artificial intelligence (AI) is emerging as a key enabler for autonomous radio access networks (RANs), where multiple large language model (LLM)-based agents reason and collaborate to achieve operator-defined intents. The open RAN (O-RAN) architecture enables the deployment and coordination of such agents. However, most existing works consider simple intents handled by independent agents, while complex intents that require coordination among agents remain unexplored. In this paper, we propose an agentic AI framework for intent translation and optimization in cell-free O-RAN. A supervisor agent translates the operator intents into an optimization objective and minimum rate requirements. Based on this information, a user weighting agent retrieves relevant prior experience from a memory module to determine the user priority weights for precoding. If the intent includes an energy-saving objective, then an open radio unit (O-RU) management agent will also be activated to determine the set of active O-RUs by using a deep reinforcement learning (DRL) algorithm. A monitoring agent measures and monitors the user data rates and coordinates with other agents to guarantee the minimum rate requirements are satisfied. To enhance scalability, we adopt a parameter-efficient fine-tuning (PEFT) method that enables the same underlying LLM to be used for different agents. Simulation results show that the proposed agentic AI framework reduces the number of active O-RUs by 41.93% when compared with three baseline schemes in energy-saving mode. Using the PEFT method, the proposed framework reduces the memory usage by 92% when compared with deploying separate LLM agents.