Hello, future AI agent engineers! 🚀
If you’ve been following the rapid evolution of AI agent engineering, you know that the field moves fast. One day you’re learning the fundamentals, and the next day there’s a new framework, tutorial, or best-practice guide that changes everything. That’s why I’m here each day to help you stay informed—not just about what’s new, but about what actually matters for your learning journey and career.
Today’s roundup is particularly exciting because we’re seeing two major developments from tech giants who understand what learners need. Both of these resources tackle the same challenge you’re facing: how do I actually build AI agents from the ground up? The resources hitting the scene right now are making that journey clearer than ever.
Let’s dive in.
1. Google ADK Tutorial: Build AI Agents & Workflows from Scratch (Beginner to Advanced)
Source: YouTube
Google has released a comprehensive tutorial on their Agent Development Kit (ADK), walking developers through the complete process of building AI agents and workflows—from foundational concepts all the way to advanced implementations. This isn’t a quick 10-minute explainer; this is a structured learning experience designed to take you from “What even is an AI agent?” to “I can architect complex multi-step workflows.” The tutorial emphasizes hands-on building, real code examples, and practical workflows you can implement immediately.
Why This Matters for Your Learning Path
Google’s ADK represents a major shift in how enterprise AI agents are built. Unlike some frameworks that feel academic or disconnected from production reality, the ADK is built by the same teams shipping AI agents at scale across Google Cloud. That means the examples, patterns, and best practices you learn are the same ones being used in real production systems.
For aspiring AI agent engineers, this tutorial is a masterclass in several critical areas:
Framework Architecture: You’ll see how Google structures agent development into clear layers—state management, action execution, decision-making, and feedback loops. Understanding these patterns will make you more effective no matter which framework you eventually use.
Workflow Design: The tutorial doesn’t just show you how to build one-off agents. It teaches you to design workflows where agents collaborate, handle errors gracefully, and scale to production demands. This is exactly what you need to know for real-world interviews and projects.
Hands-On Implementation: Rather than watching someone else code, you’ll follow along with actual code examples. The beginner section builds up gradually, while the advanced section challenges you with multi-agent systems and custom orchestration patterns. This scaffolded approach is exactly how adults learn new technical skills most effectively.
How to Use This Resource
For beginners: Start with the first section and build each example yourself. Don’t just watch—actually write the code. When you hit confusion, rewind and rewatch that section. The struggle is where learning happens.
For intermediate learners: Jump to the architecture sections and pay close attention to how agents handle state and communication. These patterns will appear in every job interview and production system you touch.
For advanced learners: Focus on the multi-agent and custom orchestration sections. This is where you’ll learn to think like a senior engineer—considering deployment, scaling, and real-world constraints.
2. Microsoft ai-agents-for-beginners
Source: GitHub
In parallel with Google’s comprehensive tutorial, Microsoft has released ai-agents-for-beginners, a structured curriculum of lessons focused on making AI agent engineering approachable for people just starting out. This repository contains not just code samples, but full lesson plans, exercises, and progression paths. It’s designed explicitly for learners who might not have deep ML backgrounds but want to understand how to build and deploy AI agents.
Microsoft’s approach here is different—and complementary—to Google’s. Where Google’s ADK dives deep into production patterns, Microsoft is focused on pedagogy: how do we teach this subject in a way that actually sticks?
Why This Matters for Career Development
If you’re building a career in AI agent engineering, you need to understand that there are multiple mental models for thinking about this field. Microsoft’s curriculum exposes you to their perspective on agent design, which is grounded in practical Azure deployment and enterprise scenarios.
Educational Structure: The repository is organized as a true curriculum, not just a collection of examples. Each lesson builds on previous ones, with clear learning objectives and exercises. This structure is invaluable when you’re trying to build a complete mental model rather than just copy-pasting code.
Community and Collaboration: Because this is on GitHub, you’re not just learning from Microsoft—you’re learning alongside hundreds of other engineers asking questions, contributing improvements, and sharing their own implementations. That community element matters more than many people realize. Some of your best learning moments will come from reading others’ questions and solutions.
Beginner-First Philosophy: The explicit “for-beginners” focus means there’s permission to ask basic questions. You won’t feel like you should understand something that wasn’t explained. This is invaluable if you’re coming from a different technical background or if programming is relatively new to you.
How to Use This Resource
Start with Lesson 1: Seriously. Don’t skip ahead. The foundation lessons are teaching you how to think about agents, not just how to code them. This mental framework is more valuable than any specific code snippet.
Do the Exercises: Each lesson includes hands-on exercises. Do them. The moment you move from “reading about agents” to “writing agent code,” your understanding jumps dramatically.
Engage with the Community: Have questions? Search the GitHub issues—odds are someone asked it before. If not, ask! This community is welcoming, and your questions help others learn too.
Use as a Reference: Once you’ve completed the curriculum, bookmark this repository. You’ll come back to specific lessons as you encounter real problems. It becomes a living reference guide for your career.
What This Means for Your Harness Engineering Journey
Let me be direct: the moment we’re living through right now is special. We’re at a point where quality educational resources for AI agent engineering are becoming abundant. Five years ago, you’d have been piecing together information from scattered blog posts and academic papers. Today, you have Google and Microsoft both investing in comprehensive, free educational resources.
This creates an opportunity—but only if you actually use these resources strategically.
Here’s my advice for the next two weeks:
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Pick one framework to learn deeply: Start with either Google’s ADK or Microsoft’s curriculum. Don’t try to learn both simultaneously; that’s a recipe for confusion. I’d suggest Microsoft’s path if you’re earlier in your learning journey, and Google’s ADK if you have some prior framework experience.
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Build something real: Don’t just follow tutorials passively. By the end of week one, I want you building a small AI agent project that does something you care about. It doesn’t have to be complicated—maybe an agent that researches a topic, summarizes it, and asks clarifying questions. The act of applying the framework to your own problem is where real learning happens.
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Document your learning: Start a learning journal or blog. When you figure something out, write it down. When you get stuck, write about what confused you. This practice serves triple duty: it solidifies your own understanding, it helps future learners, and it gives you portfolio material for job applications.
Your Takeaway
The convergence of Google’s comprehensive ADK tutorial and Microsoft’s beginner-focused curriculum represents a turning point in AI agent engineering education. You no longer have an excuse to say “there aren’t good resources out there.” There are. Your job now is to pick one, commit to it, and build something real with it.
The engineers who will be most successful in this field over the next few years are the ones who started learning now—not the ones waiting for the perfect resource or the right time. You have everything you need to start your journey today.
So pick one of these resources. Open it. And build something.
Your future self will thank you.
What’s your next step? Which resource are you going to dive into first—Google’s ADK or Microsoft’s curriculum? Let me know in the comments, and feel free to share your progress. I’m rooting for you.
Happy building,
Jamie Park
Educator & Career Coach
Harness Engineering Academy