Google AI Agents Intensive - Learning Repository

Overview

This repository contains my learning journey through the Google AI Agents Intensive course (November 10–14). The course is a comprehensive 5-day intensive program focused on building production-ready AI agents using modern frameworks and tools.

Purpose

This repository serves as:

• A structured learning workspace for daily exercises and experiments
• A knowledge base for concepts, notes, and reflections
• A portfolio of hands-on projects and capstone work
• A reference guide for future AI agent development

Course Structure

Day 1: Agent Basics

Focus: Introduction to AI agents, foundational concepts, and basic agent architecture.

📁 Day 1 Folder

Day 2: Tools and Interoperability

Focus: Integrating tools with agents, API interactions, and multi-tool orchestration.

📁 Day 2 Folder

Day 3: Context and Memory

Focus: Managing agent context, implementing memory systems, and state management.

📁 Day 3 Folder

Day 4: Evaluation and Quality

Focus: Testing agents, quality metrics, evaluation frameworks, and debugging.

📁 Day 4 Folder

Day 5: Prototype to Production

Focus: Building a complete, production-ready AI agent project.

📁 Day 5 Folder

Repository Structure

.
├── Day1_Agent_Basics/
│   ├── README.md
│   ├── notebook_template.ipynb
│   └── concepts.md
├── Day2_Tools_and_Interoperability/
│   ├── README.md
│   ├── notebook_template.ipynb
│   └── concepts.md
├── Day3_Context_and_Memory/
│   ├── README.md
│   ├── notebook_template.ipynb
│   └── concepts.md
├── Day4_Evaluation_and_Quality/
│   ├── README.md
│   ├── notebook_template.ipynb
│   └── concepts.md
├── Day5_Prototype to Production/
│   ├── README.md
│   ├── notebook_template.ipynb
│   └── concepts.md
├── Resources/
│   └── (Additional materials, links, and references)
├── Notes/
│   └── (General course notes and reflections)
├── requirements.txt
└── README.md

Daily Learning Goals

• Day 1: Understand core agent concepts and build a simple agent
• Day 2: Integrate multiple tools and APIs with agents
• Day 3: Implement context management and memory systems
• Day 4: Evaluate agent performance and ensure quality
• Day 5: Complete a full-stack agent project

Setup Instructions

1. Clone this repository:

   git clone <repository-url>
   cd <repository-name>

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Launch Jupyter:

   jupyter notebook

Resources

• Google AI Documentation
• Agent Development Kit
• Introduction to Agents white paper
• Youtube

Notes

• Each day's folder contains a code.ipynb for hands-on experiments
• Use concepts.md to document key learnings and insights
• The Resources/ folder is for additional materials and references
• The Notes/ folder is for general course notes and reflections

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Course Dates: November 10–14
Last Updated: November 2024