Agent Engineer

Learn the fundamentals of AI agents and how to build them with Google Cloud AI.

Who is this for?

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.

Course overview

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.

Lessons

Part 1: fundamentals

#	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

Part 2: building and shipping

#	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

Part 3: deep dives

#	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

How to use this course

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.

Philosophy

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.

Prerequisites

Basic Python knowledge (functions, classes, HTTP requests)
A Google Cloud account (free trial available)
Familiarity with REST APIs and JSON

Additional resources

Contributing

Found a typo? Have a suggestion? PRs and issues are welcome. See CONTRIBUTING.md for guidelines.

License

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