Industrial Agentic AI: LLM-Driven New-Generation Agents for Smart Manufacturing
Authors: Lei Ren, Haiteng Wang, Shixiang Li, Jing Zhang, Yuqing Wang, Jingwei Guo, Yunfei Zhu, Yuanjun Laili, Yang Tang, Lihui Wang
Organizations: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China · Institute of Artificial Intelligence, Beihang University, Beijing 100191, China · School of Software, Beihang University, Beijing 100191, China · Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China · Division of Production Systems and Automation, School of ITM, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
Large language models (LLMs) are enabling agents to move beyond predefined workflows toward autonomous planning and execution, creating new opportunities for smart manufacturing. However, dynamic environments, complex physical constraints, and stringent reliability requirements hinder the direct deployment of general-purpose agents in smart manufacturing. This article presents a systematic overview of LLM-driven industrial agents. First, we analyze five key challenges and discuss the concept and core characteristics of industrial agents. We then establish a unified taxonomy covering application scenarios, embodiment forms, and five capability levels. Next, we propose a four-layer system architecture and detail its core modules and operational mechanisms for closed-loop task execution. Finally, we review representative applications across the industrial lifecycle and outline future research directions. This article provides a structured reference for the design, development, and application of industrial agents.
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Fig. 1: Core Capability Shifts of Industrial Agents