01 / Build AI agent
Start with the practical AI agent tutorial.
A routing page for readers who need a first working agent path, not another abstract framework pile.
Use the framework path only if architecture blocks the first build. Otherwise ship the smallest working loop first.
Recommended first click for readers searching for how to build an AI agent or an AI agent 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 How to Build Your First AI Agent: A Beginner Tutorial That Actually Ships
Build your first AI agent with a local runtime, one constrained tool, visible traces, stop conditions, and a practical production checklist.
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.
Architecture-first alternative, not the default start.
This is the architecture-first alternative when you need the lightweight framework, implementation boundaries, or system shape clear before the tutorial can move.
Inside Build an AI Agent Framework in Python: MAF Loop, Tools, Traces
Build an AI agent framework in Python with MAF's one-loop runtime, typed tool schemas, JSONL traces, real API battle tests, and honest failure modes.
Choose this only when architecture is the blocker and you cannot make tutorial progress until the system shape is clear.
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.
Use this after the tutorial or framework choice when you need delegation, review boundaries, and a cleaner path from prompt to verified output.
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.
Use this once the tutorial or framework route is clear and the next problem is routing work between coding CLIs, preserving continuity, or setting wrapper boundaries.
A practical field guide to the coding agent harness layer: when to stay native, when wrappers help, and when a harness earns its complexity.
Best after a first route exists and orchestration, delegation boundaries, or multi-runtime control are now the blocker.
Use this once the tutorial or framework route is chosen and you need the tooling support layer around analytics, automation, and operator workflows.
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.
Best after the first route is clear and you are tightening the tooling around a real build.