Agentic AI for Intent-Based Industrial Automation

TL;DR

This paper introduces a framework combining Agentic AI with intent-based paradigms to enhance human-machine interaction and automation in Industry 5.0, demonstrated through a predictive maintenance proof of concept.

Contribution

It presents a novel integration of Agentic AI with intent-based processing for industrial automation, enabling natural language goal expression and autonomous task execution.

Findings

Feasibility of intent decomposition and agent orchestration demonstrated

Potential to reduce technical barriers in automation

Effective in predictive maintenance scenarios

Abstract

The recent development of Agentic AI systems, empowered by autonomous large language models (LLMs) agents with planning and tool-usage capabilities, enables new possibilities for the evolution of industrial automation and reduces the complexity introduced by Industry 4.0. This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm, originally developed in network research, to simplify human-machine interaction (HMI) and better align automation systems with the human-centric, sustainable, and resilient principles of Industry 5.0. Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language, which are decomposed into actionable components. These intents are broken into expectations, conditions, targets, context, and information that guide sub-agents equipped with specialized tools to execute domain-specific tasks. A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK), demonstrating the feasibility of intent decomposition, agent orchestration, and autonomous decision-making in predictive maintenance scenarios. The results confirm the potential of this approach to reduce technical barriers and enable scalable, intent-driven automation, despite data quality and explainability concerns.