发表机构
University of Applied Sciences Kempten（肯普滕应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本调研系统评估了LLM集成应用的七种架构形式，明确了各形式的结构特征，基于22个系统语料库揭示了其架构内涵与适用边界。
AI 中文摘要
大语言模型（LLM）正日益作为组件嵌入软件系统，被冠以聊天机器人、副驾驶、检索增强生成、工作流、编码智能体、AI智能体等标签进行营销。这些标签究竟代表真实的架构形式，还是仅作为品牌宣传，尚未得到系统评估。在本次调研的来源中，这些标签确实承载着架构内涵，在厂商使用场景中体现得最为明显：副驾驶（copilot）指的是一种路由器-工作者架构，在用户逐步确认的情况下运行宿主应用；而近期向“智能体（agent）”标签的转变，与AI规划的多步骤执行同步发生，用户仅能看到执行结果。四大厂商的编码智能体共享同一架构，即委托给子智能体的推理-行动循环。本调研基于智能体与工具的通用术语，描述了七种反复出现的形式——LLM聊天、定制智能体、检索增强生成（RAG）、AI增强工作流、副驾驶、编码智能体，以及部分智能体RAG。每种形式均沿四个结构维度（仅智能体RAG部分适用）进行特征刻画：架构模式、执行控制与用户干预节点、每项任务的智能体调用次数，以及工具使用。来自研究出版物和厂商文档的22个系统组成的说明性语料库为这些描述提供了支撑，并展示了它们的适用边界。
英文摘要
Large language models (LLMs) are increasingly embedded as components in software systems, marketed under labels such as chatbot, copilot, retrieval-augmented generation, workflow, coding agent and AI agent. Whether these labels denote genuine architectural forms or serve as branding has not been assessed systematically. In the sources surveyed, labels do carry architectural content, most clearly in vendor usage: copilot denotes a router-worker architecture operating a host application under step-by-step user confirmation, while the more recent shift to the label agent coincides with AI-planned multi-step execution of which the user sees only the outcome. The coding agents of four major providers share one architecture, a reason-and-act loop delegating to subagents. This survey describes seven recurring forms---LLM chats, custom agents, retrieval-augmented generation (RAG), AI-enhanced workflows, copilots, coding agents, and, in part, agentic RAG---in a common vocabulary of agents and tools. Each is characterized along four structural dimensions (agentic RAG only partially): the architectural pattern, the control of execution and the point of user intervention, the number of agent calls per task, and tool use. An illustrative corpus of 22 systems from research publications and vendor documentation grounds the descriptions and shows where they reach their limit.
