梳理AI代理在智能制造中的演进与应用前景
AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing
- 系统分析大语言模型与多模态模型驱动的AI代理技术
- 揭示智能制造中自主决策与环境感知的新能力边界
- 适合关注AI落地制造业的研究者与工程师参考
AI代理是能够感知、推理并在动态环境中自主行动的系统。随着生成式AI、大语言模型（LLMs）和多模态大语言模型（MLLMs）的快速发展，AI代理在语义理解、复杂推理和自主决策方面的能力显著提升。同时，基于目标导向与自适应性的Agentic AI兴起，进一步增强了其在复杂动态环境中的运作能力。基于LLMs的AI代理（LLM-Agents）、基于MLLMs的AI代理（MLLM-Agents）以及Agentic AI共同推动了信息处理、环境感知与自主决策能力的扩展，为智能制造开辟了新路径。然而，这些新兴AI范式在智能制造中的定义、能力边界与实际应用仍不清晰。为此，本文系统回顾了AI及AI代理技术的发展历程，深入剖析了LLM-Agents、MLLM-Agents与Agentic AI的核心概念与技术进展，并探讨其在制造领域的潜在应用与集成路径，以及可能面临的技术挑战。
原文摘要 · Abstract (English)
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.
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