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We present a novel framework for Industry 5.0, simplifying AI model deployment on edge devices across industrial environments. The design minimizes latency, prevents external data transfer, and supports real-time processing through local inference. Utilizing an agent-based approach, it delegates specific tasks to individual agents—human, algorithmic, or collaborative—providing flexibility and straightforward integration. Modular integration and low resource requirements enhance system adaptability. Preliminary evaluations in the food industry demonstrate significant improvements in deployment time and adaptability. Source code is publicly available at https://github.com/AI-REDGIO-5-0/ci-component .

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An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0

  • 会议论文

  • 发表日期：

    2026年

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  • Jorge Martinez-Gil,
  • Mario Pichler,
  • Nefeli Bountouni,
  • Sotiris Koussouris,
  • Marielena Márquez Barreiro,
  • Sergio Gusmeroli

摘要

We present a novel framework for Industry 5.0, simplifying AI model deployment on edge devices across industrial environments. The design minimizes latency, prevents external data transfer, and supports real-time processing through local inference. Utilizing an agent-based approach, it delegates specific tasks to individual agents—human, algorithmic, or collaborative—providing flexibility and straightforward integration. Modular integration and low resource requirements enhance system adaptability. Preliminary evaluations in the food industry demonstrate significant improvements in deployment time and adaptability. Source code is publicly available at https://github.com/AI-REDGIO-5-0/ci-component .