# Agentic Assistant for 6G: Turn-based Conversations for AI-RAN Hierarchical Co-Management

arXiv: https://arxiv.org/abs/2602.13868 · HTML: https://mlsanity.com/paper/2602.13868/

## 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 12 references that are in the map.
- First posted on arXiv 14 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, 5 of 5)

- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [Interactive AI with Retrieval-Augmented Generation for Next Generation Networking](https://mlsanity.com/paper/2401.11391/) (2024)
- [Automatic Retrieval-augmented Generation of 6G Network Specifications for Use Cases](https://mlsanity.com/paper/2405.03122/) (2024)
- [Building AI Service Repositories for On-Demand Service Orchestration in 6G AI-RAN](https://mlsanity.com/paper/2504.09647/) (2025)
- [RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning](https://mlsanity.com/paper/2503.15569/) (2025)

## Abstract (arXiv)

New generations of radio access networks (RAN), especially with native AI services are increasingly difficult for human engineers to manage in real-time. Enterprise networks are often managed locally, where expertise is scarce. Existing research has focused on creating Retrieval-Augmented Generation (RAG) LLMs that can help to plan and configure RAN and core aspects only. Co-management of RAN and edge AI is the gap, which creates hierarchical and dynamic problems that require turn-based human interactions. Here, we create an agentic network manager and turn-based conversation assistant that can understand human intent-based queries that match hierarchical problems in AI-RAN. The framework constructed consists of: (a) a user interface and evaluation dashboard, (b) an intelligence layer that interfaces with the AI-RAN, and (c) a knowledge layer for providing the basis for evaluations and recommendations. These form 3 layers of capability with the following validation performances (average response time 13s): (1) design and planning a service (78% accuracy), (2) operating specific AI-RAN tools (89% accuracy), and (3) tuning AI-RAN performance (67%). These initial results indicate the universal challenges of hallucination but also fast response performance success that can really reduce OPEX costs for small scale enterprise users.