2 articles tagged with "Tutorial"

A small SaaS team bumps a dependency on Monday morning and 14 tests go red. Nothing is seriously broken. A helper now rounds a little differently, and someone gives up the morning to fixes that are dull, careful and easy to get slightly wrong.

That morning is a good job for an AI coding agent that fixes failing tests. It takes the first pass while the team does something else, but only if "done" means the tests really pass and a person still approves the merge. An agent that says it's finished, or quietly edits the tests until they agree, costs more time than it saves.

By the end of this guide you'll have a short Node.js script that fixes a failing test, refuses to stop early, can't rewrite your tests, and opens a pull request for someone to review. It runs on a cloud model or on your own machine.

Picture the support inbox at an online retailer on a Monday morning. Ticket after ticket asks the same thing: where is my order? Each one means a rep checks the order, then the carrier, then types a reply.

A plain chatbot can't help. It doesn't know your orders, so it guesses. An AI agent can look things up on its own and hand anything risky to a person. This guide shows how to build an AI agent in Python for that job, without a big framework.

You'll start with the idea, write the agent in one file, run it on a hosted model and on a free one on your laptop, then add the checks you'd want before customers see it. Along the way, a tiny local model shows where things quietly go wrong.