To meet the stringent requirements of emerging applications and the increasingly complex network management and operation, the Next Generation Mobile Networks (NextG), or 6G, will adopt an AI-native architecture on the Core Network (CN). In this movement, the Third Generation Partnership Project (3GPP) has extended the cellular CN with new function as a first step toward integrating analytics, Artificial Intelligence (AI), and machine learning. However, those new functionalities are constrained by a centralized approach and managerial complexity. Furthermore, with the rise of Large Language Models (LLMs), a new era in network orchestration and management begins, leveraging and empowering the Intent-based Networking (IBN) paradigm. In addition, AI agents and Agentic AI integrate Reasoning and Acting (ReAct), enabling the usage of such intents to continuously interact with the network. Unlike state-of-the-art approaches that primarily employ Agentic AI to mitigate deployment and configuration complexity in the CN, this paper introduces AgentxGCore, which leverages an Agentic AI-Native layer to extend the 3GPP architecture and enable a system based on the existing APIs across the Beyond Next Generation Core (xGC) domain. This proposal establishes an AI-driven closed-loop for continuous optimization based on real-time information, enabling self-organization and self-adaptation. Our approach involves a multi-agent specialized system, divided into a network planner agent, capable of visualizing the network state and developing a plan to meet the intents, and a network executor, responsible for criticizing and executing the plan. To validate the proposed solution, an environment was built using an open-source CN, heterogeneous datasets, and different LLMs were employed to demonstrate its effectiveness.

翻译：为满足新兴应用的严苛需求以及日益复杂的网络管理与运维挑战，下一代移动网络（NextG），即6G，将在核心网中采用AI原生架构。在此进程中，第三代合作伙伴计划（3GPP）通过引入新功能扩展蜂窝核心网，作为整合分析、人工智能与机器学习的第一步。然而，这些新功能受限于集中式方法与管理复杂性。此外，随着大语言模型的兴起，网络编排与管理开启了新时代，可借力并赋能基于意图的网络范式。同时，AI智能体与智能体AI融合了推理与行动能力，使此类意图能够持续与网络交互。不同于现有研究主要利用智能体AI降低核心网部署与配置复杂性的做法，本文提出AgentxGCore，通过引入智能体AI原生层扩展3GPP架构，并基于超越下一代核心网域内的现有API构建系统。该方案建立了由实时信息驱动的AI闭环持续优化机制，实现自组织与自适应能力。我们的方法包含一个多智能体专用系统：网络规划器智能体可可视化网络状态并制定满足意图的方案，网络执行器智能体负责评估并执行该方案。为验证所提方案，我们利用开源核心网、异构数据集构建实验环境，并采用多种大语言模型证明其有效性。