The evolution of next-Generation (xG) wireless networks marks a paradigm shift from connectivity-centric architectures to Artificial Intelligence (AI)-native designs that tightly integrate data, computing, and communication. Yet existing AI deployments in communication systems remain largely siloed, offering isolated optimizations without intrinsic adaptability, dynamic task delegation, or multi-agent collaboration. In this work, we propose a unified agentic NetGPT framework for AI-native xG networks, wherein a NetGPT core can either perform autonomous reasoning or delegate sub-tasks to domain-specialized agents via agentic communication. The framework establishes clear modular responsibilities and interoperable workflows, enabling scalable, distributed intelligence across the network. To support continual refinement of collaborative reasoning strategies, the framework is further enhanced through Agentic reinforcement learning under partially observable conditions and stochastic external states. The training pipeline incorporates masked loss against external agent uncertainty, entropy-guided exploration, and multi-objective rewards that jointly capture task quality, coordination efficiency, and resource constraints. Through this process, NetGPT learns when and how to collaborate, effectively balancing internal reasoning with agent invocation. Overall, this work provides a foundational architecture and training methodology for self-evolving, AI-native xG networks capable of autonomous sensing, reasoning, and action in complex communication environments.

翻译：下一代（xG）无线网络的演进标志着从以连接为中心的架构向人工智能（AI）原生设计的范式转变，后者将数据、计算与通信紧密集成。然而，现有通信系统中的AI部署大多仍处于孤岛状态，仅提供孤立的优化方案，缺乏内在的自适应性、动态任务委派或多智能体协作能力。本文提出了一种用于AI原生xG网络的统一智能体NetGPT框架，其中NetGPT核心既可执行自主推理，也可通过智能体间通信将子任务委派给领域专用智能体。该框架建立了清晰的模块化职责与可互操作的工作流，实现了跨网络的可扩展分布式智能。为支持协作推理策略的持续优化，本框架进一步通过部分可观测条件与随机外部状态下的智能体强化学习进行增强。训练流程融合了针对外部智能体不确定性的掩码损失、熵引导探索以及多目标奖励机制，共同捕获任务质量、协调效率与资源约束。通过这一过程，NetGPT能够学习何时以及如何进行协作，有效平衡内部推理与智能体调用。总体而言，本研究为具备自主感知、推理与行动能力的自演进AI原生xG网络提供了基础架构与训练方法论，以应对复杂通信环境中的挑战。