Space-air-ground integrated networks (SAGIN) promise ubiquitous 6G connectivity but face significant resource management challenges due to heterogeneous infrastructure, dynamic topologies, and stringent quality-of-service (QoS) requirements. Conventional model-driven approaches struggle with scalability and adaptability in such complex environments. This paper presents an agentic artificial intelligence (AI) framework for autonomous SAGIN resource management by embedding large language model (LLM)-based agents into a Monitor-Analyze-Plan- Execute-Knowledge (MAPE-K) control plane. The framework incorporates three specialized agents, namely semantic resource perceivers, intent-driven orchestrators, and adaptive learners, that collaborate through natural language reasoning to bridge the gap between operator intents and network execution. A key innovation is the hierarchical agent-reinforcement learning (RL) collaboration mechanism, wherein LLM-based orchestrators dynamically shape reward functions for RL agents based on semantic network conditions. Validation through UAV-assisted AIGC service orchestration in energy-constrained scenarios demonstrates that LLM-driven reward shaping achieves 14% energy reduction and the lowest average service latency among all compared methods. This agentic paradigm offers a scalable pathway toward adaptive, AI-native 6G networks, capable of autonomously interpreting intents and adapting to dynamic environments.

翻译：空天地一体化网络（SAGIN）有望提供无处不在的6G连接，但由于其异构的基础设施、动态的拓扑结构以及严格的服务质量（QoS）要求，面临着显著的资源管理挑战。传统的模型驱动方法在此类复杂环境中难以实现可扩展性和适应性。本文提出了一种用于自主SAGIN资源管理的智能体化人工智能（AI）框架，通过将基于大语言模型（LLM）的智能体嵌入到监控-分析-规划-执行-知识（MAPE-K）控制平面中来实现。该框架包含三个专用智能体，即语义资源感知器、意图驱动编排器和自适应学习器，它们通过自然语言推理进行协作，以弥合运营商意图与网络执行之间的鸿沟。一个关键的创新是分层智能体-强化学习（RL）协作机制，其中基于LLM的编排器根据语义网络条件动态地为RL智能体塑造奖励函数。通过在能量受限场景下对无人机辅助的AIGC服务编排进行验证，结果表明，LLM驱动的奖励塑造实现了14%的能耗降低，并且在所有对比方法中取得了最低的平均服务延迟。这种智能体化范式为构建自适应、AI原生的6G网络提供了一条可扩展的路径，使其能够自主解释意图并适应动态环境。