Tutorial

Practical & hands-on

Learn how to build
AI automation that works

Step-by-step tutorials on AI agents, coding agents, and automation tools — written for builders who want to understand the why, not just copy the output.

⬇︎
Just want a ready-to-use workflow? Skip the theory — browse downloadable workflow templates in the Workflows section.
Browse Workflows →
● Beginner

Foundations

New to AI agents? Start here. These tutorials cover what agents actually are, how coding agents work, and how to run your first real task.

AI Agent · Concepts

What Is an AI Agent? A Beginner-Friendly Guide

A core architecture overview without the marketing jargon. Learn how tools, memory, and planning work together.

Claude Code · Getting Started

Claude Code in 20 Minutes: Your First Real Agentic Task

Install, configure, and run Claude Code on an actual codebase — not a toy example. What it does well, where it hands back control, and when to intervene.

Codex · Getting Started

OpenAI Codex CLI: What It Is and How to Run Your First Agent Task

Codex CLI vs the API vs ChatGPT — what each actually does, and a walkthrough of running a real file-editing task from the terminal.

AI Agent · Tool Calling

How Tool Calling Works: What Happens When an Agent Uses a Tool

The mechanics behind function calling — how the model decides to use a tool, what gets sent, and how the result comes back into the agent loop.

● Intermediate

Core Skills

You understand the basics. These tutorials go into agent architecture, tool choice, and how to combine coding agents with automation workflows.

Coding Agents · Comparison

Claude Code vs Kilo Code vs Codex: How to Pick the Right Coding Agent

Not a spec sheet — a decision framework. What each agent is optimized for, where each breaks down, and which fits which type of task.

→ Full comparison available

AI Agent · Architecture

How Agent Routing Actually Works: Intent, Tools, and Decision Paths

Why agents follow the paths they do — intent classification, tool selection logic, and how to design decision trees that stay predictable under real conditions.

n8n · AI Integration

Using n8n as an Orchestration Layer for AI Agents

n8n is not an agent — it's a workflow engine. Here's how to use it correctly: triggering agents, handling outputs, and chaining LLM calls without turning your workflow into spaghetti.

⬇︎ Workflow template available

Prompt Engineering · Agents

Writing System Prompts for Agents: Why Chat Prompts Break in Production

Structured outputs, role definition, and constraint design — how to write prompts that make agents behave predictably across hundreds of runs, not just the first one.

● Advanced

Production & Architecture

For builders deploying agents in real environments. These tutorials assume you've already shipped something and hit real constraints.

Multi-Agent · Design

Orchestrator-Worker Patterns: Designing Multi-Agent Systems That Don't Spiral

Agent handoff protocols, failure propagation, and loop prevention — how to architect a multi-agent pipeline that stays in control when one agent goes off-script.

AI Agent · Memory

Memory Architecture for Production Agents: Context, State, and Storage

Context windows, vector stores, session state, and external memory — when each makes sense and how to combine them without adding latency you can't afford.

Coding Agents · Integration

Integrating Claude Code into a CI/CD Pipeline: What Actually Works

Beyond running Claude Code locally — how to use it as part of an automated pipeline, what guardrails you need, and where human-in-the-loop checkpoints belong.