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Applied Sciences
Volume 15
Issue 21
10.3390/app152111414
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Agentic AI in Smart Manufacturing: Enabling Human-Centric Predictive Maintenance Ecosystems

by
Andrés Fernández-Miguel
1,2,3,
Susana Ortíz-Marcos
4,
Mariano Jiménez-Calzado
4,
Alfonso P. Fernández del Hoyo
1,
Fernando E. García-Muiña
3 and
Davide Settembre-Blundo
1,5,*
1
Faculty of Economics and Business Administration (ICADE), Comillas Pontifical University, 28015 Madrid, Spain
2
Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
3
Department of Business Administration (ADO), Rey Juan Carlos University, 28933 Madrid, Spain
4
School of Engineering (ICAI), Comillas Pontifical University, 28015 Madrid, Spain
5
Innovability Unit, Gresmalt Group, 41049 Sassuolo, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(21), 11414; https://doi.org/10.3390/app152111414 (registering DOI)
Submission received: 17 September 2025 / Revised: 19 October 2025 / Accepted: 23 October 2025 / Published: 24 October 2025
(This article belongs to the Special Issue AI-Based Machinery Health Monitoring)
Versions Notes

Featured Application

This research presents an autonomous multi-agent system for predictive machinery health monitoring in ceramic tile manufacturing using a five-level AIMM and distributed AI agents monitoring equipment like hydraulic presses, kilns, and glazing lines—achieving 94% predictive accuracy, 67% fewer false positives, and 43% less unplanned downtime. The federated learning approach ensures data privacy and enables cross-site knowledge sharing. Economic analysis reveals a 1.6-year payback period and a €447,300 NPV over five years. The system supports operator oversight for safety and is suitable for various industries needing advanced predictive maintenance.

Abstract

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, and remain accountable under human oversight. Through federated learning, edge computing, and distributed intelligence, the proposed framework enables intentional, goal-oriented monitoring agents to form self-organizing predictive maintenance ecosystems. Validated in a ceramic manufacturing facility, the system achieved 94% predictive accuracy, a 67% reduction in false positives, and a 43% decrease in unplanned downtime. Economic analysis confirmed financial viability with a 1.6-year payback period and a €447,300 NPV over five years. The framework also embeds explainable AI and trust calibration mechanisms, ensuring transparency and safe human–machine collaboration. These results demonstrate that Agentic AI provides both conceptual and practical pathways for transitioning from reactive monitoring to resilient, autonomous, and human-centered industrial intelligence.
Keywords: agentic AI; smart manufacturing; predictive maintenance; autonomous agents; multi-agent systems; federated learning; human-centric AI; machinery health monitoring; explainable AI; industrial AI; digital transformation; ceramic industry agentic AI; smart manufacturing; predictive maintenance; autonomous agents; multi-agent systems; federated learning; human-centric AI; machinery health monitoring; explainable AI; industrial AI; digital transformation; ceramic industry

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MDPI and ACS Style

Fernández-Miguel, A.; Ortíz-Marcos, S.; Jiménez-Calzado, M.; Fernández del Hoyo, A.P.; García-Muiña, F.E.; Settembre-Blundo, D. Agentic AI in Smart Manufacturing: Enabling Human-Centric Predictive Maintenance Ecosystems. Appl. Sci. 2025, 15, 11414. https://doi.org/10.3390/app152111414

AMA Style

Fernández-Miguel A, Ortíz-Marcos S, Jiménez-Calzado M, Fernández del Hoyo AP, García-Muiña FE, Settembre-Blundo D. Agentic AI in Smart Manufacturing: Enabling Human-Centric Predictive Maintenance Ecosystems. Applied Sciences. 2025; 15(21):11414. https://doi.org/10.3390/app152111414

Chicago/Turabian Style

Fernández-Miguel, Andrés, Susana Ortíz-Marcos, Mariano Jiménez-Calzado, Alfonso P. Fernández del Hoyo, Fernando E. García-Muiña, and Davide Settembre-Blundo. 2025. "Agentic AI in Smart Manufacturing: Enabling Human-Centric Predictive Maintenance Ecosystems" Applied Sciences 15, no. 21: 11414. https://doi.org/10.3390/app152111414

APA Style

Fernández-Miguel, A., Ortíz-Marcos, S., Jiménez-Calzado, M., Fernández del Hoyo, A. P., García-Muiña, F. E., & Settembre-Blundo, D. (2025). Agentic AI in Smart Manufacturing: Enabling Human-Centric Predictive Maintenance Ecosystems. Applied Sciences, 15(21), 11414. https://doi.org/10.3390/app152111414

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