Generative Intent Prediction Agentic AI empowered Edge Service Function Chain Orchestration

2026-01-20

AI Summary

This work addresses the challenge posed by high user mobility and implicit service intentions in edge networks, which hinder conventional agents from proactively responding to dynamic demands. To overcome this, we propose an intention-driven, proactive service function chain (SFC) orchestration framework. The approach first constructs a multidimensional intention space and employs a generative diffusion model to reconstruct users’ implicit intentions from contextual data, using these as global prompts to guide resource pre-optimization. Furthermore, it integrates large language model–powered agents to shift the operational paradigm from reactive execution to proactive prediction. By uniquely combining generative intention forecasting with edge SFC orchestration, our method significantly outperforms existing baselines under highly concurrent and dynamic conditions, substantially improving both the responsiveness and accuracy of service deployment.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Multiagent Systems: Modeling other Agents
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Agentic AI
• edge computing
• service function chain
• intent prediction
• implicit intent

Innovation

• Generative Intent Prediction
• Agentic AI
• Service Function Chain Orchestration
• Generative Diffusion Model
• Edge Computing