从规则系统到大模型融合，解析智能体的演进与应用
AI Agents: Evolution, Architecture, and Real-World Applications
- 结合大语言模型与感知、规划、工具模块构建现代智能体
- 提出兼顾任务效果、效率、鲁棒性与安全性的评估框架
- 覆盖企业、个人助手等场景，适合研究智能体系统者参考
本文回顾了智能体从早期基于规则的系统到融合大语言模型与感知、规划、工具使用模块的现代复杂系统的演进历程。强调理论基础与实际部署并重，综述关键智能体范式，讨论现有评估基准的局限性，并提出一个综合考量任务有效性、效率、鲁棒性和安全性的评估框架。分析了智能体在企业、个人助理及专业领域中的应用实践，展望了未来更稳健、自适应的智能体系统的研究方向。
原文摘要 · Abstract (English)
This paper examines the evolution, architecture, and practical applications of AI agents from their early, rule-based incarnations to modern sophisticated systems that integrate large language models with dedicated modules for perception, planning, and tool use. Emphasizing both theoretical foundations and real-world deployments, the paper reviews key agent paradigms, discusses limitations of current evaluation benchmarks, and proposes a holistic evaluation framework that balances task effectiveness, efficiency, robustness, and safety. Applications across enterprise, personal assistance, and specialized domains are analyzed, with insights into future research directions for more resilient and adaptive AI agent systems.
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