# From RAN Control to Agentic Intelligence: Architecture and Vision for Energy Efficient AI-RAN

arXiv: https://arxiv.org/abs/2606.21955 · HTML: https://mlsanity.com/paper/2606.21955/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 20 Jun 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, 5 of 5)

- [OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN](https://mlsanity.com/paper/2201.05632/) (2022)
- [Beyond Connectivity: An Open Architecture for AI-RAN Convergence in 6G](https://mlsanity.com/paper/2507.06911/) (2025)
- [Symbiotic Agents: A Novel Paradigm for Trustworthy AGI-driven Networks](https://mlsanity.com/paper/2507.17695/) (2025)
- [MX-AI: Agentic Observability and Control Platform for Open and AI-RAN](https://mlsanity.com/paper/2508.09197/) (2025)
- [Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach](https://mlsanity.com/paper/2409.20391/) (2024)

## Abstract (arXiv)

Future 6G networks will rely on highly distributed, AI-native Radio Access Networks (RANs), where communication and AI workloads share a common infrastructure. This evolution, combined with increasing deployment density and continuous AI processing, is expected to significantly increase RAN energy consumption. While Open RAN (O-RAN) introduces a programmable and modular control framework through the RAN Intelligent Controller (RIC) and Service Management and Orchestration (SMO), current approaches remain largely policy-driven, limiting adaptive energy-aware coordination across multiple applications. In parallel, AI-RAN promotes the convergence of AI and RAN infrastructures through AI-for-RAN, AI-on-RAN, and AI-and-RAN paradigms, yet efficient mechanisms to jointly orchestrate performance, latency, and energy remain an open challenge. This article proposes an agentic AI-native RAN architecture that bridges O-RAN's structured control with AI-RAN's unified vision. Leveraging semantic intent abstraction and Large Language Model (LLM)-driven coordination, the framework enables adaptive orchestration, conflict resolution, and energy-aware multi-objective optimization across heterogeneous workloads. Through representative AI-for-RAN and AI-on-RAN use cases, we show how such coordination can improve resource efficiency and reduce operational energy consumption, paving the way toward sustainable 6G networks.