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.

翻译：新一代无线接入网络（RAN），尤其是具备原生AI服务的网络，对人类工程师进行实时管理的难度日益增加。企业网络通常采用本地化管理，而相关专业人才却十分稀缺。现有研究主要集中于开发检索增强生成（RAG）大语言模型，这些模型仅能辅助规划和配置RAN及核心网层面。RAN与边缘AI的协同管理仍是空白领域，它引发了需要基于回合制人机交互来解决的层次化动态问题。本文构建了一个智能体网络管理器及回合制对话助手，能够理解与AI-RAN中层次化问题相匹配的、基于人类意图的查询。所构建的框架包含：（a）用户界面与评估仪表盘，（b）与AI-RAN对接的智能层，以及（c）为评估与建议提供依据的知识层。这三个层次构成了三层能力体系，其验证性能如下（平均响应时间13秒）：（1）服务设计与规划（准确率78%），（2）操作特定AI-RAN工具（准确率89%），（3）优化AI-RAN性能（准确率67%）。这些初步结果表明，尽管幻觉问题仍是普遍挑战，但快速的响应性能已取得成功，这确实能够为小规模企业用户有效降低运营成本。