5-Day AI Agents Intensive Course with Google

This repository contains educational materials, notebooks, and resources from the "5-Day AI Agents Intensive Course with Google". The course covers fundamental concepts of AI agents, from basic prompt-to-action workflows to advanced agent architectures and tool integration.

Course Overview

This intensive course provides hands-on learning about AI agents, covering:

• Fundamentals: Understanding what AI agents are and how they work
• Architectures: Different approaches to building AI agent systems
• Tool Integration: How agents interact with external tools and APIs
• Best Practices: Industry standards and optimization techniques
• Practical Implementation: Real-world examples and use cases

Repository Structure

├── day-1/                    # Day 1: Foundations
│   ├── Day_1a_From_Prompt_to_Action.ipynb
│   └── Day_1b_Agent_Architectures.ipynb
├── day-2/                    # Day 2: Tools & Integration  
│   ├── Day_2a_Agent_Tools.ipynb
│   └── Day_2b_Agent_Tools_Best_Practices.ipynb
├── day-3/                    # Day 3: Sessions & Memory
│   ├── day-3a-agent-sessions.ipynb
│   └── day-3b-agent-memory.ipynb
├── day-4/                    # Day 4: Observability & Evaluation
│   ├── day-4a-agent-observability.ipynb
│   └── day-4b-agent-evaluation.ipynb
├── day-5/                    # Day 5: (Content will be updated when released by Google & Kaggle)
├── whitepapers/              # Reference Materials
│   ├── Agent Quality.pdf
│   ├── Agent Tools & Interoperability with Model Context Protocol (MCP).pdf
│   ├── Context Engineering_ Sessions & Memory.pdf
│   └── Introduction to Agents.pdf
└── README.md                 # This file

Getting Started

Prerequisites

• Python 3.11+ (notebooks are tested with Python 3.11.13)
• Jupyter Notebook or Google Colab access
• Basic understanding of machine learning and natural language processing

Running the Notebooks

Option 1: Google Colab (Recommended)

Each notebook includes a "Open in Colab" badge at the top. Simply click it to run the notebook directly in Google Colab with all dependencies pre-installed.

Option 2: Local Jupyter

1. Clone this repository:

   git clone https://github.com/michaelwnau/google-agents-intensive.git
   cd google-agents-intensive

2. Install required dependencies (check individual notebooks for specific requirements):

   pip install jupyter notebook
   # Additional dependencies will be listed in each notebook

3. Start Jupyter Notebook:

   jupyter notebook

4. Navigate to the desired day folder and open the notebook files.

Course Content

Day 1: Foundations

• Day 1a - From Prompt to Action: Introduction to basic agent workflows, understanding how prompts translate into actions
• Day 1b - Agent Architectures: Overview of different agent architectural patterns and design principles

Day 2: Tools & Integration

• Day 2a - Agent Tools: Hands-on exploration of agent tool integration and external API usage
• Day 2b - Agent Tools Best Practices: Industry best practices for tool integration, error handling, and optimization

Day 3: Sessions & Memory

• Day 3a - Agent Sessions: Understanding how to manage persistent agent sessions and context
• Day 3b - Agent Memory: Implementation of memory systems for agents including short-term and long-term memory patterns

Day 4: Observability & Evaluation

• Day 4a - Agent Observability: Monitoring and debugging agent behavior, logging, and performance tracking
• Day 4b - Agent Evaluation: Methodologies for evaluating agent performance, testing frameworks, and quality metrics

Reference Materials

The whitepapers/ directory contains essential reading materials:

• Introduction to Agents.pdf: Foundational concepts and theoretical background
• Agent Tools & Interoperability with Model Context Protocol (MCP).pdf: Advanced topics on tool integration and protocol standards
• Context Engineering: Sessions & Memory.pdf: Comprehensive guide to managing agent context and memory systems
• Agent Quality.pdf: Best practices and methodologies for maintaining high-quality agent systems

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

• Google & Kaggle: For providing the comprehensive AI Agents Intensive Course
• Course Instructors: For the excellent educational content and practical examples
• Community Contributors: For feedback and improvements to the materials

Support

For questions about the course content or technical issues:

• Create an issue in this repository
• Refer to the official Google AI documentation
• Check the individual notebook cells for specific troubleshooting guidance

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Last updated: November 2025