Agentic AI for Intent-Based Industrial Automation

2025-06-05

AI Summary

To address the challenges of complex human–machine interaction, lack of human-centricity, and insufficient adaptability in Industry 5.0 systems, this paper proposes an intent-driven intelligent automation framework. It pioneers the adaptation of the intent-driven paradigm—originally developed for networking—to industrial automation, enabling operators to specify high-level business objectives via natural language and automatically decomposing them into executable tasks. The framework features five-dimensional intent modeling (expectation, condition, goal, context, information), multi-agent collaborative orchestration, and an autonomous large language model–based ADK (Autonomous, Domain-aware, Knowledge-grounded) agent architecture. Evaluated on the C-MAPSS dataset for predictive maintenance, the approach significantly lowers technical barriers while enhancing system scalability, intent alignment, and explainability; it also partially alleviates data quality bottlenecks. This work advances industrial automation toward human-centeredness, sustainability, and resilience.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Integrates Agentic AI with intent-based paradigm to simplify human-machine interaction
• Enables high-level natural language goals for industrial automation systems
• Demonstrates intent-driven automation feasibility in predictive maintenance scenarios

Innovation

• Agentic AI integrates intent-based industrial automation
• Natural language decomposes goals into actionable components
• Autonomous agents execute tasks using specialized tools