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

arXiv: https://arxiv.org/abs/2510.25813 · HTML: https://mlsanity.com/paper/2510.25813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Edge AI: Algorithms and Systems](https://mlsanity.com/paper/2002.09668/) (2020)
- [Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence](https://mlsanity.com/paper/1909.00560/) (2019)
- [Large Language Models Empowered Autonomous Edge AI for Connected Intelligence](https://mlsanity.com/paper/2307.02779/) (2023)
- [Edge AI: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2407.04053/) (2024)
- [OpenEI: An Open Framework for Edge Intelligence](https://mlsanity.com/paper/1906.01864/) (2019)
- [Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review](https://mlsanity.com/paper/2012.09049/) (2020)
- [Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Review](https://mlsanity.com/paper/2312.16174/) (2023)
- [Automated Knowledge Graph Learning in Industrial Processes](https://mlsanity.com/paper/2407.02106/) (2024)

## Abstract (arXiv)

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.