# Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence

arXiv: https://arxiv.org/abs/2602.24115 · HTML: https://mlsanity.com/paper/2602.24115/

## Key facts

- Cited by 1 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 8 of its 17 references that are in the map.
- First posted on arXiv 27 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, 8 of 8)

- [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024)
- [AgentRAN: An Agentic AI Architecture for Autonomous Control of Open 6G Networks](https://mlsanity.com/paper/2508.17778/) (2025)
- [Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research](https://mlsanity.com/paper/2307.00319/) (2023)
- [Advanced Architectures Integrated with Agentic AI for Next-Generation Wireless Networks](https://mlsanity.com/paper/2502.01089/) (2025)
- [Empowering the 6G Cellular Architecture with Open RAN](https://mlsanity.com/paper/2312.02746/) (2023)
- [A Comprehensive Tutorial and Survey of O-RAN: Exploring Slicing-aware Architecture, Deployment Options, Use Cases, and Challenges](https://mlsanity.com/paper/2405.03555/) (2024)
- [Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance](https://mlsanity.com/paper/2512.12400/) (2025)
- [Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2307.02754/) (2023)

## Built on by (most cited first, 1 of 1)

- [Z2-ACT: End-to-End Verifiable Agentic Intent Control for Open 6G RAN](https://mlsanity.com/paper/2608.21049/) (2026)

## Abstract (arXiv)

Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder to operate multi-tenant, multi-objective RANs in a safe and auditable manner. In parallel, agentic AI systems with explicit planning, tool use, memory, and self-management offer a natural way to structure long-lived control loops. This article surveys how such agentic controllers can be brought into O-RAN: we review the O-RAN architecture, contrast agentic controllers with conventional ML/RL xApps, and organise the task landscape around three clusters: network slice life-cycle, radio resource management (RRM) closed loops, and cross-cutting security, privacy, and compliance. We then introduce a small set of agentic primitives (Plan-Act-Observe-Reflect, skills as tool use, memory and evidence, and self-management gates) and show, in a multi-cell O-RAN simulation, how they improve slice life-cycle and RRM performance compared to conventional baselines and ablations that remove individual primitives. Security, privacy, and compliance are discussed as architectural constraints and open challenges for standards-aligned deployments. This framework achieves an average 8.83% reduction in resource usage across three classic network slices.