Learn the fundamentals of AI agents and how to build them with Google Cloud AI.
Software engineers who want to understand what AI agents are, how they work, and how to build them. No prior AI/ML experience required - just curiosity and some Python knowledge.
This course is split into three parts:
Part 1: Fundamentals (101) - Understand the core concepts behind AI agents. These lessons are platform-agnostic and focused on building your mental model.
Part 2: Building and shipping (201) - Put those fundamentals into practice using Google Cloud AI, Vertex AI, and the Agent Development Kit (ADK).
Part 3: Deep dives (301) - Go deeper on specific topics that matter for real-world agent development.
| # | Lesson | What you will learn |
|---|---|---|
| 01 | What are AI agents? | The big picture - what agents are, why they matter, and when to use them |
| 02 | How agents think | LLMs as the reasoning engine - how models plan, decide, and generate |
| 03 | Tools - giving agents hands | Function calling, tool design, and connecting agents to the real world |
| 04 | Agentic design patterns | ReAct, reflection, planning, and other core patterns |
| 05 | Memory and context | How agents remember things - sessions, context windows, and long-term memory |
| 06 | Planning and reasoning | How agents break down complex tasks and make decisions |
| 07 | Multi-agent systems | When one agent is not enough - coordination, delegation, and teamwork |
| 08 | Agentic RAG | Going beyond basic retrieval - agents that search, evaluate, and refine |
| 09 | Evaluating and testing agents | How to know if your agent actually works - metrics, evals, and observability |
| 10 | Guardrails and safety | Keeping agents trustworthy - security, alignment, and responsible AI |
| # | Lesson | What you will learn |
|---|---|---|
| 11 | From prototype to production | The journey from demo to deployed - CI/CD, rollout, and operations |
| 12 | Getting started with Vertex AI and ADK | The Google Cloud AI stack for agents - what is available and how it fits together |
| 13 | Building your first agent | Hands-on - build a working agent with ADK step by step |
| 14 | Agent protocols - MCP and A2A | How agents talk to tools and to each other using open standards |
| # | Lesson | What you will learn |
|---|---|---|
| 15 | AGENTS.md | Giving AI coding agents context about your project with a standard config file |
| 16 | MCP deep dive | How MCP works under the hood, MCP vs. CLI tools, and security considerations |
| 17 | Agent skills | Packaging reusable domain expertise as portable skill modules |
| 18 | Orchestrators | Managing agent control flow - patterns, frameworks, and best practices |
| 19 | Where to go from here | Resources, codelabs, community, and next steps |
- Read in order if you are new to agents. Each lesson builds on the previous one.
- Jump around if you already know the basics. Each lesson is self-contained enough to read on its own.
- Follow the links to official docs, codelabs, and tutorials for hands-on practice. We intentionally link out to maintained resources rather than duplicating API docs or code samples that go stale.
This course follows a few principles:
- Analogies first. We use everyday comparisons to explain complex concepts before diving into technical details.
- Fundamentals over frameworks. Understand the "why" before the "how." Frameworks change, but the core ideas stick around.
- Link, don't duplicate. For API references, code samples, and setup instructions, we point to official Google Cloud docs and codelabs. This keeps our content focused on concepts and ensures you always see up-to-date information.
- Honest about trade-offs. Every architectural choice has costs. We try to show both sides.
- Basic Python knowledge (functions, classes, HTTP requests)
- A Google Cloud account (free trial available)
- Familiarity with REST APIs and JSON
- Google Cloud AI documentation
- Vertex AI documentation
- Agent Development Kit (ADK) documentation
- Google Cloud AI codelabs
- Gemini API documentation
Found a typo? Have a suggestion? PRs and issues are welcome. See CONTRIBUTING.md for guidelines.
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.