Agentic AI for Intent-Based Industrial Automation

• URL: http://arxiv.org/abs/2506.04980v1
• Date: Thu, 05 Jun 2025 12:50:54 GMT
• Title: Agentic AI for Intent-Based Industrial Automation
• Authors: Marcos Lima Romero, Ricardo Suyama,
• Abstract summary: This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm.<n>Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language.<n>A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK)
• Score: 0.6906005491572401
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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