AI代理与代理式AI：概念分类、应用与挑战

AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenge

May 15, 2025

作者: Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee

cs.AI

摘要

本研究深入辨析了AI代理（AI Agents）与代理式AI（Agentic AI）之间的差异，通过构建系统的概念分类体系、应用映射及挑战分析，阐明了二者在设计理念与能力上的显著区别。首先，我们概述了研究策略与基础定义，将AI代理描述为由大型语言模型（LLMs）和大型图像模型（LIMs）驱动的模块化系统，专注于特定任务的自动化。生成式AI被视为其前身，而AI代理则通过工具集成、提示工程和推理增强不断进化。相比之下，代理式AI系统标志着一场范式转变，其特征体现在多代理协作、动态任务分解、持久记忆及协调自主性上。通过对架构演进、操作机制、交互方式及自主层级的顺序评估，我们对这两种范式进行了对比分析。在应用领域方面，如客户支持、日程安排和数据摘要等，与代理式AI在科研自动化、机器人协调及医疗决策支持中的部署形成鲜明对比。此外，我们探讨了各自范式中的独特挑战，包括幻觉现象、脆弱性、涌现行为及协调失败，并提出了针对性的解决方案，如ReAct循环、RAG（检索增强生成）、协调层及因果建模。本工作旨在为开发健壮、可扩展且可解释的AI代理及代理式AI驱动系统提供一份明确的路线图。>AI代理，代理驱动，视觉-语言模型，代理式AI决策支持系统，代理式AI应用

English

This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven by Large Language Models (LLMs) and Large Image Models (LIMs) for narrow, task-specific automation. Generative AI is positioned as a precursor, with AI Agents advancing through tool integration, prompt engineering, and reasoning enhancements. In contrast, Agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy. Through a sequential evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both paradigms. Application domains such as customer support, scheduling, and data summarization are contrasted with Agentic AI deployments in research automation, robotic coordination, and medical decision support. We further examine unique challenges in each paradigm including hallucination, brittleness, emergent behavior, and coordination failure and propose targeted solutions such as ReAct loops, RAG, orchestration layers, and causal modeling. This work aims to provide a definitive roadmap for developing robust, scalable, and explainable AI agent and Agentic AI-driven systems. >AI Agents, Agent-driven, Vision-Language-Models, Agentic AI Decision Support System, Agentic-AI Applications