How to build an AI agent: start with the first working tutorial.

If you are figuring out how to build your first AI agent, this page gives you the shortest honest choice. Most readers should start with the first working agent tutorial, switch to the framework route only when architecture blocks the tutorial, then pull in tooling or workflow support after the first path is chosen.

1. Most readers should start with the first working agent tutorial.2. Use the framework route only if architecture blocks the tutorial.3. Add tooling and workflow support after the first route is chosen.

First Click

Your first click should be the practical tutorial.

This router exists to get you to a first working agent fast. Start with the tutorial by default, then use the framework route only when you cannot make progress until the system shape is clear.

First Tutorial

Start Here

Recommended first click for readers searching for how to build an AI agent or an AI agent tutorial.

Build your first working AI agent with the practical tutorial.

This is the default start-here route if you want a beginner-friendly tutorial that gets you to a real working agent before you widen into frameworks, tooling, or scheduling.

Inside ai agent tutorial: Build Your First Real Agent Step by Step

A practical, execution-first guide to build, run, debug, and harden your first AI agent with tools, guardrails, and production checks. If you're deciding between this tutorial-first route and the architecture-first route, use /build-ai-agent as the lane map before you branch.

Choose this if your question is how to build an AI agent from scratch and you want the clearest path to a first working loop.

Start the first working agent tutorial

Framework Path

Architecture First

Architecture-first alternative, not the default start.

Need architecture first? Take the framework route.

This is the architecture-first alternative when you need the lightweight framework, implementation boundaries, or system shape clear before the tutorial can move.

Inside How I Built a Lightweight AI Agent Framework in Python (And Battle-Tested It in One Morning)

I built MAF — a minimal AI agent framework in Python with one core loop, typed tool schemas, and JSONL traces. If the architecture-first route is useful but you still need the broader start-here map, use /build-ai-agent before you commit.

Choose this only when architecture is the blocker and you cannot make tutorial progress until the system shape is clear.

Choose the architecture-first route

After You Choose A Start

Bring in tooling and workflow support after the first route is clear.

These notes help once the tutorial-first or framework-first choice is made. They are follow-on support, not competing starts.

Workflow

Support

Tighten the shipping loop.

Use this after the tutorial or framework choice when you need delegation, review boundaries, and a cleaner path from prompt to verified output.

AI Coding Agent Workflow: Guardrails, Delegation, Review

A practical field guide to running coding agents safely: scope, isolation, verification, and review.

Best once your first route is chosen and you need stronger execution discipline around it.

Tooling

Support

Add the CLI tooling layer after the first build path is clear.

Use this once the tutorial or framework route is chosen and you need the tooling support layer around analytics, automation, and operator workflows.

How to Build CLI Tools That AI Agents Can Actually Use

I built datafast-cli and pointed an autonomous AI agent at it. 13 commands, 2 bugs found, and the 5 principles that make CLI tools genuinely useful as AI agent tools. If you're still choosing the broader first-agent path before the tooling layer, start at /build-ai-agent.

Best after the first route is clear and you are tightening the tooling around a real build.