AI代理與代理式AI：概念分類、應用與挑戰

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

本研究精確區分了AI代理（AI Agents）與代理型AI（Agentic AI），提供了一個結構化的概念分類、應用映射及挑戰分析，以釐清它們在設計哲學與能力上的差異。我們首先概述了搜索策略與基礎定義，將AI代理描述為由大型語言模型（LLMs）和大型圖像模型（LIMs）驅動的模塊化系統，專注於狹窄、任務特定的自動化。生成式AI被定位為先驅，而AI代理則通過工具集成、提示工程和推理增強向前推進。相比之下，代理型AI系統代表了一種範式轉變，其特徵是多代理協作、動態任務分解、持久記憶和協調自主性。通過對架構演進、操作機制、交互風格和自主層次的順序評估，我們對這兩種範式進行了比較分析。諸如客戶支持、日程安排和數據摘要等應用領域與代理型AI在研究自動化、機器人協調和醫療決策支持中的部署形成對比。我們進一步探討了每種範式中的獨特挑戰，包括幻覺、脆弱性、湧現行為和協調失敗，並提出了針對性的解決方案，如ReAct循環、RAG、協調層和因果建模。本工作旨在為開發健壯、可擴展且可解釋的AI代理及代理型AI驅動系統提供明確的路線圖。>AI代理，代理驅動，視覺-語言模型，代理型AI決策支持系統，代理型AI應用

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