Agentic AI (AAI), which extends Large Language Models with enhanced reasoning capabilities, has emerged as a promising paradigm for autonomous edge service scheduling. However, user mobility creates highly dynamic service demands in edge networks, and existing service scheduling agents often lack generalization capabilities for new scenarios. Therefore, this paper proposes a novel Intent-Driven General Agentic AI (IGAA) framework. Leveraging a meta-learning paradigm, IGAA enables AAI to continuously learn from prior service scheduling experiences to achieve generalized scheduling capabilities. Particularly, IGAA incorporates three core mechanisms. First, we design a Network-Service-Intent matrix mapping method to allow agents to simulate novel scenarios and generate training datasets. Second, we present an easy-to-hard generalization learning scheme with two customized algorithms, namely Resource Causal Effect-aware Transfer Learning (RCETL) and Action Potential Optimality-aware Transfer Learning (APOTL). These algorithms help IGAA adapt to new scenarios. Furthermore, to prevent catastrophic forgetting during continual IGAA learning, we propose a Generative Intent Replay (GIR) mechanism that synthesizes historical service data to consolidate prior capabilities. Finally, to mitigate the effect of LLM hallucinations on scenario simulation, we incorporate a scenario evaluation and correction model to guide agents in generating rational scenarios and datasets. Extensive experiments demonstrate IGAA's strong generalization and scalability. Specifically, IGAA enables rapid adaptation by transferring learned policies to analogous new ones, such as applying latency-sensitive patterns from real-time computing to optimize novel Internet of Vehicles (IoV) services. Compared to scenario-specific methods, IGAA maintains the intent-satisfaction rate gap within 3.81%.

翻译：智能体AI通过增强大型语言模型的推理能力，已成为自主边缘服务调度的新兴范式。然而，用户移动性导致边缘网络中的服务需求呈现高度动态性，现有服务调度智能体往往缺乏对新场景的泛化能力。为此，本文提出一种新颖的意图驱动通用智能体AI框架。该框架利用元学习范式，使智能体AI能够持续从历史服务调度经验中学习，从而获得通用化调度能力。具体而言，IGAA包含三项核心机制：首先，设计网络-服务-意图矩阵映射方法，使智能体能够模拟新场景并生成训练数据集；其次，提出由易到难的泛化学习方案，包含两种定制算法——资源因果效应感知迁移学习与行动潜力最优性感知迁移学习，以帮助IGAA适应新场景；此外，为防止持续学习中的灾难性遗忘，提出生成式意图重放机制，通过合成历史服务数据巩固既有能力；最后，为降低大语言模型幻觉对场景模拟的影响，引入场景评估与修正模型，指导智能体生成合理的场景与数据集。大量实验验证了IGAA出色的泛化能力与可扩展性：该框架能够通过迁移已学策略快速适应类似新场景，例如将实时计算中的时延敏感模式应用于新型车联网服务优化。相较于场景专用方法，IGAA将意图满足率差距保持在3.81%以内。