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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25813 |
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| _version_ | 1866912678004391936 |
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| author | Martinez-Gil, Jorge Pichler, Mario Bountouni, Nefeli Koussouris, Sotiris Barreiro, Marielena Márquez Gusmeroli, Sergio |
| author_facet | Martinez-Gil, Jorge Pichler, Mario Bountouni, Nefeli Koussouris, Sotiris Barreiro, Marielena Márquez Gusmeroli, Sergio |
| contents | We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings. The design reduces latency and avoids external data transfer by enabling local inference and real-time processing. Our implementation is agent-based, which means that individual agents, whether human, algorithmic, or collaborative, are responsible for well-defined tasks, enabling flexibility and simplifying integration. Moreover, our framework supports modular integration and maintains low resource requirements. Preliminary evaluations concerning the food industry in real scenarios indicate improved deployment time and system adaptability performance. The source code is publicly available at https://github.com/AI-REDGIO-5-0/ci-component. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0 Martinez-Gil, Jorge Pichler, Mario Bountouni, Nefeli Koussouris, Sotiris Barreiro, Marielena Márquez Gusmeroli, Sergio Artificial Intelligence We present a novel framework for Industry 5.0 that simplifies the deployment of AI models on edge devices in various industrial settings. The design reduces latency and avoids external data transfer by enabling local inference and real-time processing. Our implementation is agent-based, which means that individual agents, whether human, algorithmic, or collaborative, are responsible for well-defined tasks, enabling flexibility and simplifying integration. Moreover, our framework supports modular integration and maintains low resource requirements. Preliminary evaluations concerning the food industry in real scenarios indicate improved deployment time and system adaptability performance. The source code is publicly available at https://github.com/AI-REDGIO-5-0/ci-component. |
| title | An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0 |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.25813 |