5-Day-AI-Agents-Intensive-Course-with-Google

# 5-Day AI Agents Intensive Course with Google > **42万+开发者参与的 AI Agent 课程** | 感谢 [sdivyanshu90](https://github.com/sdivyanshu90/5-Day-AI-Agents-Intensive-Course-with-Google) 和 Google 提供的免费课程资源 5 天直播回放视频：[youtube](https://www.youtube.com/playlist?list=PLqFaTIg4myu9r7uRoNfbJhHUbLp-1t1YE) 谷歌新课程：[25天Agents课程-圣诞节礼物](https://mp.weixin.qq.com/s/H4C65Vvh58G3g8AZ1vl1lA)，学习笔记：[25-Day-Agents-Course-by-Google](https://github.com/anxiong2025/25-Day-Agents-Course-by-Google) ## Quick Start ### Prerequisites - Python 3.12+ - [uv](https://docs.astral.sh/uv/) package management tool - Google API Key ([获取地址](https://aistudio.google.com/apikey)) ### Installation and Configuration ```bash # Install dependencies uv sync # Configure API key cp .env.example .env # Edit the .env file and add your GOOGLE_API_KEY ``` ### VS Code Configuration **Required Plugin**: Jupyter - Install: `Cmd+Shift+P` → "Extensions: Install Extensions" → Search "Jupyter" - Select Kernel: Click the kernel selector in the notebook → Select `.venv (Python 3.12.x)` ### Run Notebook Open `day1/*.ipynb` in VS Code and press `Shift+Enter` to run the cell --- ## Course Outline and Daily Assignments ### ✅ Day 1: Agent Introduction **Topic**: Agent Classification, Agent Ops, Interoperability and Security Basics - 📄 [Whitepaper: Introduction to Agents](https://www.kaggle.com/whitepaper-agents) - 🎙️ [Podcast: Unit 1 Summary](https://www.kaggle.com/whitepaper-agents-podcast) - 💻 **Code Experiments**: - [Build Your First Agent with Gemini and ADK](https://www.kaggle.com/code/markishere/day-1-prompting-with-gemini) - [Build Your First Multi-Agent System](https://www.kaggle.com/code/markishere/day-1-agent-architectures) - 📚 [Code Experiment Troubleshooting Guide](https://www.kaggle.com/discussions/general/552193) **Local Notebooks**: - `day1/day1-01-from-prompt-to-action.ipynb` - Basic Agent + Google Search - `day1/day1-02-agent-architectures.ipynb` - Multi-Agent Mode (Sequential/Parallel/Dynamic) - `day1/day1-01-First-Agent-Web-UI.py` - FastAPI Web Interface (Run: `uv run day1/day1-01-First-Agent-Web-UI.py`) --- ### Day 2: Agent Tools & Interoperability (MCP) **Topic**: External Tools, Real-Time Data Retrieval, Model Context Protocol (MCP) - 📄 [Whitepaper: Agent Tools & Interoperability with MCP](https://www.kaggle.com/whitepaper-agents-tools) - 🎙️ [Podcast: Unit 2 Summary](https://www.kaggle.com/whitepaper-agents-tools-podcast) - 💻 **Code Experiments**: - [Extend Agent Capabilities with New Tools](https://www.kaggle.com/code/markishere/day-2-tools) - [Tool Best Practices: MCP and Long-Running Operations](https://www.kaggle.com/code/markishere/day-2-mcp-and-long-running-tools) --- ### Day 3: Context Engineering: Sessions & Memory **Topic**: Context Window Management, Sessions, Memory (Long-Term Persistence) - 📄 [Whitepaper: Context Engineering: Sessions & Memory](https://www.kaggle.com/whitepaper-agents-memory) - 🎙️ [Podcast: Unit 3 Summary](https://www.kaggle.com/whitepaper-agents-memory-podcast) - 💻 **Code Experiments**: - [Implement Session Management for Instant Context](https://www.kaggle.com/code/markishere/day-3-sessions) - [Implement a Memory System for Long-Term Personalization](https://www.kaggle.com/code/markishere/day-3-memory) --- ### Day 4: Agent Quality Assurance **Topic**: Evaluation Framework, Observability (Logs/Traces/Metrics), LLM-as-a-Judge, Human-in-the-Loop (HITL) - 📄 [Whitepaper: Agent Quality](https://www.kaggle.com/whitepaper-agents-quality) - 🎙️ [Podcast: Unit 4 Summary](https://www.kaggle.com/whitepaper-agents-quality-podcast) - 💻 **Code Experiments**: - [Implement Observability for Debugging](https://www.kaggle.com/code/markishere/day-4-observability) - [Evaluate Your Agent](https://www.kaggle.com/code/markishere/day-4-evaluation) --- ### Day 5: From Prototype to Production **Topic**: Deployment, Scaling, Agent2Agent (A2A) Protocol, Vertex AI Agent Engine - 📄 [Whitepaper: Prototype to Production](https://www.kaggle.com/whitepaper-agents-production) - 🎙️ [Podcast: Unit 5 Summary](https://www.kaggle.com/whitepaper-agents-production-podcast) - 💻 **Code Experiments**: - [Implement Multi-Agent Communication Using A2A Protocol](https://www.kaggle.com/code/markishere/day-5-a2a) - [[Optional] Deploy to Google Cloud Agent Engine](https://www.kaggle.com/code/markishere/day-5-agent-engine) --- ## Notes - Kaggle code experiments require phone verification - The project uses `.env` to store API keys (do not commit `.env` to git)