LLM驱动的智能体系统突破传统限制，实现跨领域自主决策。
LLM-Powered AI Agent Systems and Their Applications in Industry
- 用大语言模型替代规则逻辑，实现自然语言交互与多模态处理。
- 在客服、制造、金融等领域展现高效自动化能力，提升响应灵活性。
- 适合关注AI落地应用的开发者与产业技术决策者参考。
大语言模型（LLMs）的兴起重塑了智能体系统。与传统基于规则的智能体相比，基于LLM的智能体具备更强的灵活性、跨领域推理能力和自然语言交互能力。随着多模态大语言模型的集成，当前智能体系统能够处理文本、图像、音频和结构化表格等多种数据模态，支持更丰富、自适应的真实世界行为。本文全面回顾了从预LLM时代到当前基于LLM的智能体架构的演进历程。我们将智能体系统分为软件类、物理类及自适应混合类，并探讨其在客户服务、软件开发、制造自动化、个性化教育、金融交易和医疗健康等领域的应用。同时分析了当前面临的主要挑战，包括高推理延迟、输出不确定性、缺乏评估指标以及安全漏洞，并提出相应的缓解方案。
原文摘要 · Abstract (English)
The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered agents offer greater flexibility, cross-domain reasoning, and natural language interaction. Moreover, with the integration of multi-modal LLMs, current agent systems are highly capable of processing diverse data modalities, including text, images, audio, and structured tabular data, enabling richer and more adaptive real-world behavior. This paper comprehensively examines the evolution of agent systems from the pre-LLM era to current LLM-powered architectures. We categorize agent systems into software-based, physical, and adaptive hybrid systems, highlighting applications across customer service, software development, manufacturing automation, personalized education, financial trading, and healthcare. We further discuss the primary challenges posed by LLM-powered agents, including high inference latency, output uncertainty, lack of evaluation metrics, and security vulnerabilities, and propose potential solutions to mitigate these concerns.
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