I Built an AI Agent That Sends Me My Numbers Every Monday Morning.

A step-by-step n8n tutorial: An AI agent that reads your live metrics data and automatically reports it every Monday morning.

AI agents have one embarrassing habit. Ask them a question about your data, and they’ll give you an answer, even when they’re making that number up.

I showed this on my weekly live session yesterday morning (Every Wednesday at 8:30 AM EST). I built a small AI agent in n8n whose only job was to answer questions about my startup’s weekly metrics. I asked for last week’s churn rate. It gave me a number, a cancellation count, even a quarterly comparison. All of it was invented. It never once looked at Asaura’s user data.

An agent like that doesn’t crash or flag a problem. It hands you a wrong answer that looks just like a right one, and you don’t catch it until someone manually checks. The fix is a set of techniques that make an agent read from a live source, remember what it’s doing, recover on its own when a step breaks, and run on its own.

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Hey there! 👋🏿👋🏿👋🏿 I’m Hodman Murad, Founder of The Data Letter, Between Thinking and Doing, and Asaura AI. In case you’re new here, are some recent articles you may have missed:

Code w/ Claude: 5 Data Science Trends I’m Watching → Five shifts coming for the data scientist role by 2027, from treating model upgrades like dependency bumps to curating memory the way we curate feature stores.

Build Your Own AI Agent Before Google Ships You Theirs → Why Google’s five overlapping consumer agents don’t fit a small ops team, and how to put one working agent on one recurring job this week instead.

Build a RAG System with NotebookLM in Under an Hour → A hands-on build that gets you a private RAG running on your own documents, plus the ten engineering terms you’ll need to lead any AI conversation at work.

Build Your First AI Data Pipeline in Python: From Raw CSV to Predictions → A step-by-step scikit-learn tutorial that turns vehicle data into CO2 predictions and teaches you to read your model’s metrics honestly, even when the answer is that your model is useless.

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Today’s build is the next one in this series.

This guide builds that agent from scratch in n8n, step by step. You’ll start with a simple agent running on a local model. Then you’ll connect it to your live metrics, give it memory, make it handle failures, and schedule it to report on its own every Monday (or whichever days you pull your numbers). It runs entirely free.