6 articles tagged with "AI Agents"

You run the support queue or the Friday customer update, and you know what an AI helper should do there. You don't write code. Claude Code or Codex will write it for you from one plain request.

The trouble starts when the work comes back. If you build AI agents with Claude Code or Codex, you get a few hundred lines you can't read, and you're asked to trust them.

IntelliNode changes what comes back. The agent builds the tool as a flow, a small graph of steps. It draws a picture of the graph before anything runs, then runs it and reports back in plain words. This guide starts with one example on an OpenAI or Claude key, then moves the same tool to an offline model and shows what our tests found.

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 an online store with a small support team. Much of the inbox is some version of "where is my order", and every answer means opening the order system and copying a status into a reply. You've used ChatGPT, maybe called an AI API once, and now the team wants an agent to take that first pass.

This guide shows you how to build AI agents in Node.js by building that one, a piece at a time. By the end it looks orders up by itself, asks a person before it gives money back, and keeps answering when an AI service goes down. You start on your laptop with no API key and move to a hosted model when you're ready.

We ran every example on a deliberately tiny model and show you where it got things wrong. Those mistakes are much cheaper to meet here than in front of a customer.

Most teams build their first agentic workflow in Python almost by accident. A script sends support tickets to a model, the demo sorts twenty of them nicely, and then the real queue shows up. Soon one prompt has become five jobs: read the ticket, pull out the facts, spot customers who might leave, draft a reply, and have someone check it before it goes out.

Now the hard part is the wiring, not the prompt: what runs first, what can run side by side, what happens when a step breaks, and what it all costs each month. It's tempting to grab the biggest framework you've heard of. Often you don't need it.

This guide builds the few shapes that nearly every multi-step AI process takes, from ticket triage to content work to research briefs, in short readable code. Then it helps you decide whether a lighter tool can carry your process or a heavier framework is worth the weight.

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.

Updated in October 2026. An earlier version of this article walked through the IntelliCloud app, which has since been retired. Everything it did now runs in the open source libraries, Intelli for Python and IntelliNode for Node.js.

Most teams don't start by looking for an AI framework. They start with a job that eats hours every week: answering the same customer questions, reading invoices, writing product copy, or arguing about which of three models is the best.

This article goes through six of those jobs. For each one you'll see what IntelliNode does and where to find working code, so you can pick the one closest to your own problem and start there.