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

2026-02-14

AI Summary

This work addresses the challenges of coordinated management between AI-native Radio Access Networks (AI-RAN) and edge AI in the 6G era, particularly the lack of human-in-the-loop interaction mechanisms and the scarcity of on-site domain experts in enterprise settings. To this end, it proposes the first turn-based conversational agent framework for hierarchical collaborative AI-RAN management. The framework integrates a retrieval-augmented generation (RAG)-enhanced large language model within a three-tier architecture—comprising a user interface, an AI-RAN intelligent interface layer, and a knowledge layer—to enable intent understanding and dynamic decision-making across design planning, tool operation, and performance tuning. Experimental results demonstrate an average system response time of 13 seconds, with task accuracy rates of 78%, 89%, and 67% in service design, tool operation, and performance tuning, respectively, significantly reducing operational costs for small enterprises.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• AI-RAN
• co-management
• turn-based conversation
• hierarchical management
• 6G

Innovation

• Agentic Assistant
• AI-RAN Co-Management
• Turn-based Conversation
• Hierarchical Network Management
• 6G