# AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks

arXiv: https://arxiv.org/abs/2508.17778 · HTML: https://mlsanity.com/paper/2508.17778/

## Key facts

- Cited by 16 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 18 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## 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)

- [TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications](https://mlsanity.com/paper/2406.01768/) (2024)
- [A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems](https://mlsanity.com/paper/1609.03590/) (2016)
- [dApps: Enabling Real-Time AI-Based Open RAN Control](https://mlsanity.com/paper/2501.16502/) (2025)
- [Intent Assurance using LLMs guided by Intent Drift](https://mlsanity.com/paper/2402.00715/) (2024)
- [Serverless Computing: A Survey of Opportunities, Challenges and Applications](https://mlsanity.com/paper/1911.01296/) (2019)
- [AutoRAN: Automated and Zero-Touch Open RAN Systems](https://mlsanity.com/paper/2504.11233/) (2025)
- [ALLSTaR: Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN](https://mlsanity.com/paper/2505.18389/) (2025)
- [Symbiotic Agents: A Novel Paradigm for Trustworthy AGI-driven Networks](https://mlsanity.com/paper/2507.17695/) (2025)
- [Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing](https://mlsanity.com/paper/2506.00574/) (2025)

## Built on by (most cited first, 16 of 16)

- [WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks](https://mlsanity.com/paper/2603.00501/) (2026)
- [Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows](https://mlsanity.com/paper/2512.20640/) (2025)
- [Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models](https://mlsanity.com/paper/2601.07978/) (2026)
- [Agentic AI for Intent-driven Optimization in Cell-free O-RAN](https://mlsanity.com/paper/2602.22539/) (2026)
- [GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing](https://mlsanity.com/paper/2605.27360/) (2026)
- [Graph-Symbolic Policy Enforcement and Control (G-SPEC): A Neuro-Symbolic Framework for Safe Agentic AI in 5G Autonomous Networks](https://mlsanity.com/paper/2512.20275/) (2025)
- [Should I Have Expressed a Different Intent? Counterfactual Generation for LLM-Based Autonomous Control](https://mlsanity.com/paper/2601.20090/) (2026)
- [Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence](https://mlsanity.com/paper/2602.24115/) (2026)
- [AgentxGCore: Agentic AI for Next-Generation Mobile Core Network](https://mlsanity.com/paper/2606.00417/) (2026)
- [AGORA: Agentic Green Orchestration Architecture for Beyond 5G Networks](https://mlsanity.com/paper/2602.13290/) (2026)
- [Executor-Side Progressive Risk-Gated Actuation for Agentic AI in Wireless Supervisory Control](https://mlsanity.com/paper/2605.02697/) (2026)
- [Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems](https://mlsanity.com/paper/2606.24416/) (2026)
- [LLM-Powered Agentic AI for 5G/6G Networks: A Tutorial and Survey on Architectures, Protocols, and Standardization](https://mlsanity.com/paper/2607.16066/) (2026)
- [WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents](https://mlsanity.com/paper/2608.14573/) (2026)
- [Z2-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN](https://mlsanity.com/paper/2608.21049/) (2026)
- [NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning](https://mlsanity.com/paper/2610.16226/) (2026)

## Abstract (arXiv)

Despite the programmable architecture of Open RAN, today's deployments still rely heavily on static control and manual operations. To move beyond this limitation, we introduce AgentRAN, an AI-native, Open RAN-aligned agentic framework that generates and orchestrates a fabric of distributed AI agents based on natural language intents. Unlike traditional approaches that require explicit programming, AgentRAN's LLM-powered agents interpret natural language intents, negotiate strategies through structured conversations, and orchestrate control loops across the network. AgentRAN instantiates a self-organizing hierarchy of agents that decompose complex intents across time scales (from sub-millisecond to minutes), spatial domains (cell to network-wide), and protocol layers (PHY/MAC to RRC). A central innovation is the AI-RAN Factory, which continuously generates improved agents and algorithms from operational data, transforming the network into a system that evolves its own intelligence. We validate AgentRAN through live 5G experiments, demonstrating dynamic adaptation to changing operator intents across power control and scheduling. Key benefits include transparent decision-making (all agent reasoning is auditable), bootstrapped intelligence (no initial training data required), and continuous self-improvement via the AI-RAN Factory.