AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing

TL;DR

This paper reviews the evolution and integration of AI agents, especially LLM and MLLM-based, in smart manufacturing, highlighting their capabilities, challenges, and future potential in autonomous decision-making.

Contribution

It systematically analyzes the core concepts, technological advancements, and applications of AI agents and Agentic AI in the context of smart manufacturing.

Findings

AI agents enhance manufacturing automation and decision-making.

Emerging AI paradigms face definitional and practical challenges.

Potential for AI agents to revolutionize smart manufacturing environments.

Abstract

AI agents are autonomous systems designed to perceive, reason, and act within dynamic environments. With the rapid advancements in generative AI (GenAI), large language models (LLMs) and multimodal large language models (MLLMs) have significantly improved AI agents' capabilities in semantic comprehension, complex reasoning, and autonomous decision-making. At the same time, the rise of Agentic AI highlights adaptability and goal-directed autonomy in dynamic and complex environments. LLMs-based AI Agents (LLM-Agents), MLLMs-based AI Agents (MLLM-Agents), and Agentic AI contribute to expanding AI's capabilities in information processing, environmental perception, and autonomous decision-making, opening new avenues for smart manufacturing. However, the definitions, capability boundaries, and practical applications of these emerging AI paradigms in smart manufacturing remain unclear. To address this gap, this study systematically reviews the evolution of AI and AI agent technologies, examines the core concepts and technological advancements of LLM-Agents, MLLM-Agents, and Agentic AI, and explores their potential applications in and integration into manufacturing, along with the potential challenges they may face.