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面向小白的、最全最详细的 AI Agent 面试准备资料，包含面试八股文、企业级实战项目（Python/Java/Go 三版本）、简历模板、STAR 面试稿、漫画图解。
- 9 大模块面试八股文：覆盖 Agent 基础、ReAct/Plan-and-Execute 框架、RAG、工具调用、记忆系统、多智能体、大模型基础、工程化实践、Prompt 工程，合计 200+ 道面试题及详细答案
- 企业级实战项目：对标 nageoffer/ragent 的架构复杂度，Python / Java / Go 三个语言版本
- 完整面试闭环：学习路线 → 八股文 → 项目实战 → 简历撰写 → STAR 面试稿 → 模拟问答
- 哆啦 A 梦漫画图解：6 张原创漫画，生动形象地解释核心概念
| 目录 | 内容 | 说明 |
|---|---|---|
| 00-学习路线图 | 从零开始的学习路线 | 6 个阶段，约 7-9 个月完整路径 |
| 01-面试八股文 | 9 大模块分类面试题 | 概念、原理、Q&A、代码示例 |
| 02-企业招聘分析 | 大厂岗位需求汇总 | 腾讯/小红书/字节等岗位分析 |
| 03-开源项目学习笔记 | 优秀开源项目剖析 | Claude Code、ragent 等 |
| 04-简历模板 | AI Agent 简历写法 | STAR 法则模板，可直接复制 |
| 05-STAR 面试稿 | 面试话术准备 | 可朗读练习的完整面试稿 |
| 06-面试问答集 | 项目面试问答 | 100+ 道项目面试题及 STAR 回答 |
| 版本 | 技术栈 | 目标人群 |
|---|---|---|
| Python 版 | FastAPI + LangChain + Milvus + Redis | AI/算法岗、Python 后端 |
| Java 版 | Spring Boot 3 + Spring AI + MyBatis Plus + Milvus | Java 后端开发 |
| Go 版 | Gin + 自研框架 + Milvus + Redis | Go 后端、云原生岗 |
| 漫画 | 对应知识点 |
|---|---|
| 什么是 AI Agent | Agent 的定义与核心组成 |
| ReAct 循环 | Thought → Action → Observation 循环 |
| RAG 流程 | 检索增强生成的完整流程 |
| 多 Agent 协作 | 多智能体分工与协作模式 |
| 记忆系统 | 短期记忆 vs 长期记忆 |
| 面试场景 | 从零到 Offer 的面试之旅 |
ai-agent-interview-guide/ ├── README.md # 本文件 ├── docs/ # 文档区 │ ├── 00-学习路线图/ # 从零开始的学习路线 │ ├── 01-面试八股文/ # 9 大模块分类面试题+答案 │ │ ├── 01-基础概念.md │ │ ├── 02-核心框架.md │ │ ├── 03-RAG技术.md │ │ ├── 04-工具调用.md │ │ ├── 05-记忆系统.md │ │ ├── 06-多智能体.md │ │ ├── 07-大模型基础.md │ │ ├── 08-工程化实践.md │ │ └── 09-Prompt工程.md │ ├── 02-企业招聘分析/ # 岗位需求汇总 │ ├── 03-开源项目学习笔记/ # 开源项目+网上项目分析 │ ├── 04-简历模板/ # AI Agent 简历写法 │ ├── 05-STAR面试稿/ # STAR 法面试准备 │ └── 06-面试问答集/ # 项目可能被问的所有问题 ├── comics/ # 哆啦 A 梦风格漫画图解 ├── project-python/ # 企业级 AI Agent 项目 Python 版 ├── project-java/ # 企业级 AI Agent 项目 Java 版 └── project-go/ # 企业级 AI Agent 项目 Go 版 如果你是完全的小白，建议按以下顺序学习：
Python 版（推荐首选）：
cd project-python cp .env.example .env # 编辑 .env 填入你的 API Key pip install -r requirements.txt uvicorn app.main:app --reload --host 0.0.0.0 --port 8000Java 版：
cd project-java mvn clean package -DskipTests java -jar target/agent-platform-1.0.0.jarGo 版：
cd project-go go build -o agent-server ./cmd/server ./agent-server本项目实现了一个企业级智能客服/知识助手 Agent 平台，核心架构：
用户请求 → API 网关 → 意图识别 → Agent 编排器 ├── ReAct Agent（思考-行动-观察循环） ├── 规划 Agent（任务分解与执行） ├── RAG Agent（知识检索与生成） └── 反思 Agent（质量校验） 支撑层： ├── 多路检索引擎（向量 + BM25 + 混合检索 + RRF 融合） ├── 记忆系统（短期：Redis 滑窗 + 长期：向量数据库） ├── 工具系统（搜索、计算器、数据库查询 + MCP） ├── 模型路由（多模型候选 + 三态熔断器 + 自动降级） ├── 全链路追踪（每步操作的 Trace 记录） └── 文档 ETL（PDF/Word 解析 → 分块 → 向量化 → 入库） | 模块 | 核心知识点 | 面试题数 |
|---|---|---|
| 基础概念 | Agent 定义、组成、分类、应用场景 | 27 题 |
| 核心框架 | ReAct、Plan-and-Execute、Reflexion、LangGraph | 27 题 |
| RAG 技术 | 分块策略、向量数据库、混合检索、重排序 | 24+ 题 |
| 工具调用 | Function Calling、MCP、工具路由、安全 | 17+ 题 |
| 记忆系统 | 短期/长期记忆、摘要压缩、记忆检索 | 20 题 |
| 多智能体 | 协作模式、通信机制、冲突解决 | 20 题 |
| 大模型基础 | Transformer、Attention、KV Cache、LoRA、RLHF | 28 题 |
| 工程化实践 | 熔断器、Token 优化、可观测性、部署 | 29+ 题 |
| Prompt 工程 | CoT、Few-shot、ReAct 模板、注入防御 | 28 题 |
欢迎提交 Issue 和 Pull Request！
- 发现面试题有误或需要补充？请提 Issue
- 有新的面试经验想分享？欢迎 PR
- 项目代码有 Bug？请提 Issue 并附上复现步骤
本项目采用 MIT License 开源。
面试八股文和学习资料仅供学习参考，请勿用于商业用途。