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

TL;DR

This paper introduces an agent-based framework for Industry 5.0 that streamlines the deployment of edge AI solutions, reducing latency and enhancing adaptability in industrial environments.

Contribution

It proposes a novel, modular, agent-based framework for rapid deployment of edge AI in Industry 5.0, emphasizing local inference and real-time processing.

Findings

Improved deployment time in real food industry scenarios

Enhanced system adaptability and low resource usage

Framework supports flexible, modular integration

Abstract

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.