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

2025-10-29

AI Summary

To address high deployment latency, external data transmission dependency, and poor adaptability of AI models in Industry 5.0 edge environments, this paper proposes AgentEdge—a lightweight, modular agent-based framework for rapid AI model deployment and real-time local inference across heterogeneous industrial edge devices. It employs a hierarchical agent architecture enabling human–machine collaborative task decomposition and dynamic resource scheduling, thereby significantly reducing end-to-end latency and eliminating the need to transmit sensitive data externally. With minimal resource overhead (<128 MB RAM), strong scenario adaptability, and plug-and-play integration capability, AgentEdge enhances the scalability and accessibility of edge AI. Evaluated on a real-world food manufacturing production line, it shortens model deployment time by 67% and reduces system response latency to under 83 ms. The open-source implementation demonstrates clear industrial applicability and practical deployment value.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Simplifying AI model deployment on industrial edge devices
• Reducing latency through local inference and real-time processing
• Enabling flexible integration via agent-based modular architecture

Innovation

• Agent-based framework for Industry 5.0 edge AI deployment
• Local inference and real-time processing reduce latency
• Modular integration with low resource requirements