With the development of artificial intelligence (AI), Agentic AI (AAI) based on large language models (LLMs) is gradually being applied to network management. However, in edge network environments, high user mobility and implicit service intents pose significant challenges to the passive and reactive management of traditional AAI. To address the limitations of existing approaches in handling dynamic demands and predicting users' implicit intents, in this paper we propose an edge service function chain (SFC) orchestration framework empowered by a Generative Intent Prediction Agent (GIPA). Our GIPA aims to shift the paradigm from passive execution to proactive prediction and orchestration. First, we construct a multidimensional intent space that includes functional preferences, QoS sensitivity, and resource requirements, enabling the mapping from unstructured natural language to quantifiable physical resource demands. Second, to cope with the complexity and randomness of intent sequences, we design an intent prediction model based on a Generative Diffusion Model (GDM), which reconstructs users' implicit intents from multidimensional context through a reverse denoising process. Finally, the predicted implicit intents are embedded as global prompts into the SFC orchestration model to guide the network in proactively and ahead-of-time optimizing SFC deployment strategies. Experiment results show that GIPA outperforms existing baseline methods in highly concurrent and highly dynamic scenarios.

翻译：随着人工智能（AI）的发展，基于大语言模型（LLM）的智能体AI（AAI）正逐步应用于网络管理。然而，在边缘网络环境中，用户的高移动性和隐式服务意图对传统AAI的被动响应式管理构成了重大挑战。为克服现有方法在处理动态需求与预测用户隐式意图方面的局限，本文提出一种由生成式意图预测智能体（GIPA）赋能的边缘服务功能链（SFC）编排框架。我们的GIPA旨在将范式从被动执行转向主动预测与编排。首先，我们构建了一个包含功能偏好、QoS敏感度与资源需求的多维意图空间，实现了从非结构化自然语言到可量化物理资源需求的映射。其次，为应对意图序列的复杂性与随机性，我们设计了一种基于生成式扩散模型（GDM）的意图预测模型，该模型通过逆向去噪过程从多维上下文中重构用户的隐式意图。最后，预测得到的隐式意图作为全局提示嵌入SFC编排模型，以引导网络主动且超前地优化SFC部署策略。实验结果表明，在高并发与高动态场景下，GIPA的性能优于现有基线方法。