代理检索增强生成：关于代理RAG的综述
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
AI总结：
本文综述了代理RAG系统，分析了其架构分类、设计权衡及应用，指出其在灵活性、可扩展性和上下文感知方面的优势，并探讨了未来研究方向。
摘要：
大型语言模型（LLMs）通过实现类人文本生成和自然语言理解推动了人工智能的发展。然而，其依赖静态训练数据限制了对动态实时查询的响应能力，导致输出过时或不准确。检索增强生成（RAG）作为一种解决方案，通过整合实时数据检索增强LLMs，提供上下文相关且更新的响应。尽管有潜力，传统RAG系统受限于静态工作流，缺乏多步骤推理和复杂任务管理所需的适应性。代理RAG通过在RAG管道中嵌入自主AI代理，利用代理设计模式、规划、工具使用和多代理协作动态管理检索策略，迭代优化上下文理解，并通过从顺序步骤到适应性协作的操作结构调整工作流程。这种整合使代理RAG系统在多样应用中具备灵活性、可扩展性和上下文感知能力。本文对代理RAG系统进行了分析综述，追溯RAG范式的演变，基于代理数量、控制结构、自主性和知识表示提出系统化的代理RAG架构分类，并对现有框架的设计权衡进行了比较分析。综述探讨了医疗、金融、教育和企业文档处理中的应用，并为系统设计者和实践者提炼了实用经验。最后，指出了评估、协调、内存管理、效率和治理等关键开放研究挑战，明确了未来研究方向。
英文摘要：
Large Language Models (LLMs) have advanced artificial intelligence by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their ability to respond to dynamic, real-time queries, resulting in outdated or inaccurate outputs. Retrieval-Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real-time data retrieval to provide contextually relevant and up-to-date responses. Despite its promise, traditional RAG systems are constrained by static workflows and lack the adaptability required for multi-step reasoning and complex task management. Agentic Retrieval-Augmented Generation (Agentic RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline. These agents leverage agentic design patterns reflection, planning, tool use, and multi-agent collaboration to dynamically manage retrieval strategies, iteratively refine contextual understanding, and adapt workflows through operational structures ranging from sequential steps to adaptive collaboration. This integration enables Agentic RAG systems to deliver flexibility, scalability, and context-awareness across diverse applications. This paper presents an analytical survey of Agentic RAG systems. It traces the evolution of RAG paradigms, introduces a principled taxonomy of Agentic RAG architectures based on agent cardinality, control structure, autonomy, and knowledge representation, and provides a comparative analysis of design trade-offs across existing frameworks. The survey examines applications in healthcare, finance, education, and enterprise document processing, and distills practical lessons for system designers and practitioners. Finally, it identifies key open research challenges related to evaluation, coordination, memory management, efficiency, and governance, outlining directions for future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
