The paper proposes a framework for the collective collaboration of recursive autonomous agents (RAAs) in smart factories. It introduces a common task and capability interface that enables recursive decomposition of production objectives from the top down and aggregation of conditional capabilities from the bottom up. The framework includes organizational models for leaf and composite units, conditions for finite task expansion, and operational mechanisms linking task decomposition, resource commitments, result aggregation, and coordination across changing production conditions.
By Ziqing Zhou, Linqiang Hu, Yuning Chen, Hongda Zhang, Zhao Huang, Ding Zhou, Haining Xu
The article reviews how large language models (LLMs) enable industrial agents to move beyond predefined workflows toward autonomous planning and execution in smart manufacturing. It identifies five key challenges, defines the concept and core characteristics of industrial agents, and presents a unified taxonomy covering application scenarios, embodiment forms, and capability levels. The authors propose a four‑layer system architecture with core modules for closed‑loop task execution, review representative applications across the industrial lifecycle, and outline future research directions.
By Lei Ren, Haiteng Wang, Shixiang Li, Jing Zhang, Yuqing Wang, Jingwei Guo, Yunfei Zhu, Yuanjun Laili, Yang Tang, Lihui Wang
arXiv:2606. 15575v1 Announce Type: new Abstract: Organizational knowledge is fragmented across a variety of software systems, tacit expertise, and manual documents that have traditionally been designed for human consumption.
By Anne S. R. Marx, Ricardo M. Avelino, Torbj{\o}rn Netland, Mennatallah El-Assady
arXiv:2606. 12835v1 Announce Type: cross Abstract: The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action.
By Quanyan Zhu
arXiv:2608. 07148v1 Announce Type: new Abstract: Modern manufacturing imposes six coupled demands on adaptive control: local decisions with global consequences, partial observability, nonstationarity, reflex speed response with long horizon effects, delayed and diffuse outcomes, and dynamics that resist explicit modeling.
By Fouad Bahrpeyma, Dirk Reichelt
arXiv:2608. 10153v1 Announce Type: new Abstract: Enterprises are deploying autonomous AI agents faster than they can govern them, and prevailing approaches stretch a single discipline, typically DevSecOps built for deterministic automation, across every scale of agency.
By Srinivas Telukunta, Georgios Nektarios Lilis, Lucio Baron
arXiv:2607. 23438v1 Announce Type: new Abstract: As AI systems increasingly exhibit agentic behavior, discussions of autonomy often conflate what systems are technically capable of doing with what they should be permitted to do in practice.
By Haining Zheng, Qian Dong, Rodolfo K. Depena, Jonathan D. Bhatia, Feng Xiao, Peng Xu
arXiv:2608. 03413v1 Announce Type: new Abstract: As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve real business and industrial problems.
By Zuojun Max Shen, Yuan Qu, Pujun Zhang, Anbang Liu, Yunhao Liang
arXiv:2606. 16649v1 Announce Type: new Abstract: Agentic AI marks a new phase of enterprise automation.
By Christopner Koch, Joshua A. Wellbrock
arXiv:2609.05552v1 Announce Type: cross
Abstract: Industrial environments increasingly rely on collaboration between humans and AI-enabled agents. Effective teamwork requires aligning how agents perc...
By Kolitha Kottagaha W. M, Jos A. C. Bokhorst, Ben Gaffinet, Christos Emmanouilidis
The paper defines an autonomous agent as a system that maintains task goals and viability within explicit identity and authority boundaries, continuously acting with world and self-models, and adjusting control mechanisms through feedback. It traces the evolution from homeostasis to biological autonomy and identifies six operationalization lines, culminating in a one-boundary, four-loop framework that organizes task execution, self-maintenance, metacontrol, and governed learning. Five simulation experiments test permission constraints, road-closure response, health maintenance, capability re-estimation, and cross-task learning, offering preliminary evidence for the feasibility of these mechanisms while noting the need for broader testing.
By Hongda Zhang, Ziqing Zhou, Yuning Chen, Linqiang Hu, Yi Liu, Ding Zhou, Zhao Huang, Haining Xu
The paper introduces a data‑driven self‑learning control method for highly flexible, modular manufacturing systems. It uses a model‑based reinforcement learning framework that incorporates approximate inverse process models, separating actuation dynamics from state‑space dynamics so that training occurs only in task space. A lightweight feedforward architecture for these inverse models is integrated into standard RL policy networks and tested on a laboratory modular production testbed, showing improved performance and faster training, especially for off‑policy algorithms.
By Andreas Schwung, Steve Yuwono, Sofiene Lassoued, Dorothea Schwung