Introduction
In early 2023, AutoGPT made headlines as a self-directing agent that could research, plan, and execute tasks autonomously. Developers gave it goals like "research competitors and write a market analysis"—and watched as it burned through hundreds of dollars in API costs, stuck in infinite loops.
Two years later, Manus launched to immediate acclaim. Same premise—autonomous task completion—but it actually delivered: research reports, working websites, coordinated multi-step projects. No infinite loops. No runaway costs.
Model intelligence matters—but it's not enough. What separates agents that fail from agents that deliver is engineering: structured workflows, intelligent context management, robust error handling. This tutorial teaches you that engineering.
Why This Tutorial Exists
The field of AI agent development is moving at breakneck speed—papers drop weekly, "best practices" are rewritten monthly. Yet most resources fall into two camps: high-level explainers that stop at "what is an agent?" or academic deep-dives that never touch production code.
This tutorial is different. We teach by building. Every concept is grounded in runnable code. We cover failure modes, not just happy paths. By the end, you won't just understand agents—you'll have built one.
What You Will Actually Build
By the end of this tutorial, you won't just "understand" agents—you will have built YourGPT, a full-stack personal assistant with:
- Multi-tool capabilities: Web search, code execution, and real-time information retrieval
- Persistent memory: It remembers your preferences across sessions
- Real-time voice interaction: Talk to it like a human, with sub-second response times
- Production guardrails: Input validation, output filtering, and graceful error handling
More importantly, you'll have the fundamental skills to build virtually any agent architecture you can imagine:
| Agent Type | What It Does | Skills You'll Use |
|---|---|---|
| Research Agent | Reads hundreds of papers, synthesizes key insights, produces structured reports | RAG, context engineering, tool orchestration |
| Coding Agent | Autonomously writes, tests, debugs, and deploys code | Function calling, sandboxed execution, self-correction loops |
| Customer Support Agent | Triages tickets, answers FAQs, escalates complex issues to humans | Routing workflows, memory, guardrails |
| Data Analyst Agent | Queries databases, generates visualizations, explains insights in plain language | Structured output, tool chaining, multi-step reasoning |
| Personal Assistant | Manages calendar, answers emails, surfaces relevant information proactively | Memory, real-time voice, multi-tool orchestration |
What We'll Cover
This isn't a weekend project. It's a comprehensive curriculum designed to take you from "I've used ChatGPT" to "I can architect autonomous systems."
The First Principles Approach
This tutorial uses Mastra as our primary framework—a TypeScript-first toolkit with excellent memory, workflows, and tool integration. But the goal isn't to teach you Mastra. The goal is to teach you agent engineering.
Frameworks evolve monthly. Mastra today, something else tomorrow. But the underlying patterns—context management, tool orchestration, error recovery, memory architecture—these are stable. Learn them once, apply them anywhere.
We'll start with raw fundamentals so you understand exactly what's happening under the hood. When we introduce framework abstractions, you'll know what they're abstracting. The patterns you learn here apply whether you're using Gemini, GPT, Claude, or open-source models.
The AI-Native Mindset
Building agents requires a fundamental shift in how you think about software. Traditional development is deterministic: given input X, produce output Y. Agent development is probabilistic: given input X, pursue goal Y.
Here's the difference:
| Traditional Approach | AI-Native Approach |
|---|---|
| "Add a chatbot feature to our app" | "The agent is the product—it autonomously manages user interactions end-to-end" |
| "Parse this JSON and extract the date field" | "Understand this document and extract any relevant dates, handling edge cases gracefully" |
| "If error, show error message" | "If error, reason about the failure and try an alternative approach" |
| Write code that executes instructions | Cultivate behavior in a system that pursues goals |
This isn't just philosophical—it changes how you debug, how you test, and how you think about "correctness."
Throughout this tutorial, we'll be building AI-Native: designing systems where intelligence is the core, not an add-on.
Prerequisites
This tutorial assumes:
- Basic programming proficiency (TypeScript/JavaScript preferred, but concepts transfer to any language)
- Familiarity with APIs (you've made a REST call before)
- Some LLM exposure (you've used ChatGPT, Claude, or similar)
You do not need:
- A PhD in machine learning
- Prior experience with agent frameworks
- Deep knowledge of transformers or neural network architecture
Feeling intimidated by the word "engineering"? Don't be. If you can write a function that calls an API, you have everything you need. The rest is patterns and practice.
For hands-on exercises, you'll need API access to a foundation model (we'll use Google Gemini, but alternatives work). Setup instructions are in the next chapter.
How to Use This Tutorial
Run the code. Every chapter includes working examples. Don't just read—execute them locally, inspect the outputs, modify the parameters. Understanding comes from experimentation.
Complete the exercises. Most chapters end with a "Build It" challenge. These aren't optional—they're where the learning happens. Resist the urge to skip ahead.
Embrace failure. We include deliberate failure exercises throughout. When your agent loops infinitely or hallucinates confidently, you're learning something important. Debug it.
Build incrementally. Each chapter builds on the last. By the capstone, you'll have assembled a complete agent from components you understand deeply.
Use AI to learn AI. When something doesn't click, pause and ask ChatGPT, Gemini, or Claude: "Explain context windows like I'm a backend engineer." When implementing exercises, pair with tools like Cursor, GitHub Copilot, or Claude Code. You're learning to build with AI—use it as your learning partner too. It's turtles all the way down.
Let's build something that thinks. And more importantly—something that does.
→ Next: Agent Fundamentals — What makes an agent an agent, and your first one in 10 minutes.