Recent advances in intelligent network control have primarily relied on task-specific Artificial Intelligence (AI) models deployed separately within the Radio Access Network (RAN) and Core Network (CN). While effective for isolated models, these suffer from limited generalization, fragmented decision-making across network domains, and significant maintenance overhead due to frequent retraining. To address these limitations, we propose a novel AI agent-based RAN-CN converged intelligence framework that leverages a Large Language Model (LLM) integrated with the Reasoning and Acting (ReAct) paradigm. The proposed framework enables the AI agent to iteratively reason over real-time, cross-domain state information stored in a centralized monitoring database and to synthesize adaptive control policies through a closed-loop thought-action-observation process. Unlike conventional Machine Learning (ML) based approaches, it does not rely on model retraining. Instead, the AI agent dynamically queries and interprets structured network data to generate context-aware control decisions, allowing for fast and flexible adaptation to changing network conditions. Experimental results demonstrate the enhanced generalization capability and superior adaptability of the proposed framework to previously unseen network scenarios, highlighting its potential as a unified control intelligence for next-generation networks.

翻译：近年来，智能网络控制领域的进展主要依赖于部署在无线接入网和核心网中的任务专用人工智能模型。这些模型虽然在各自领域有效，但存在泛化能力有限、跨网络域决策碎片化，以及因频繁重训练导致的显著维护开销等问题。为应对这些局限，本文提出一种新颖的基于AI智能体的无线接入网-核心网融合智能框架，该框架利用与“推理-执行”范式相结合的大语言模型。所提框架使AI智能体能够迭代地推理存储于集中式监控数据库中的实时跨域状态信息，并通过闭环的“思考-行动-观察”过程合成自适应控制策略。与传统的基于机器学习的方法不同，该框架不依赖于模型重训练。相反，AI智能体动态查询并解析结构化网络数据，以生成情境感知的控制决策，从而实现对动态网络条件的快速灵活适应。实验结果表明，所提框架在面对先前未见过的网络场景时，展现出增强的泛化能力和卓越的适应性，凸显了其作为下一代网络统一控制智能体的潜力。