An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0
This addresses the challenge of rapid AI deployment in industrial edge computing, though it appears incremental as it builds on existing agent-based and edge AI concepts.
The paper tackles the problem of deploying AI models on edge devices in Industry 5.0 by presenting an agent-based framework that enables local inference and real-time processing, with preliminary evaluations in the food industry showing improved deployment time and system adaptability.
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.