An Agentic Task Offloading Workflow Method for Intellicise Wireless Networks in the Industrial Internet of Things

To address the challenge of inaccurate intent recognition in Intellicise (intelligent and concise) Wireless Networks-enabled edge-cloud-terminal computing scenarios, which utilize large language models (LLMs) for task offloading and resource allocation, we propose a two-tier multi-agent workflow method. This method employs slot-filling technology in the Intention Layer to parse user requests and subsequently coordinates multiple agents in the Efficiency Layer to collaboratively optimize scheduling policies by converting subjective intentions into measurable utility metrics. LLMs perceive key factors—such as device location, task type, and context—and adjust task allocation parameters dynamically. They then guide agents to use appropriate tools and perform optimized scheduling based on the task's nature. Experimental results demonstrate that our method accurately recognizes user intents by considering multi-dimensional factors such as edge device deployment-related regional economic factors, network conditions, and IoT device metrics, thereby meeting diverse user intent.