My first steps into automation with AI agents using n8n

Setting the stage: n8n in my homelab

As someone constantly tinkering in my Proxmox-based homelab, I recently decided to explore workflow automation with AI agents using n8n — a powerful, self-hostable automation tool that connects anything to everything.

To get started, I spun up a new container and installed n8n via Portainer, running it in Docker. For anyone looking to do the same, here’s the official n8n install guide for Docker and Portainer:

Install n8n with Docker & Portainer
Link to environment variables

Trial and error: The hands-on approach

Initially, I went in completely blind. I began dragging in random nodes, loosely configuring them, and hoped for some magic.

Spoiler: it didn’t work.

I kept hitting walls — nodes not connecting, credentials misbehaving, and the dreaded “nothing happened” moments. But each small failure taught me a bit more about how n8n thinks and how its workflows execute.

💡 The language learning idea

While experimenting, I had a lightbulb moment: why not use AI to help me learn a new language?

Here was the concept:

• Schedule a daily trigger
• Use an AI agent to ask a chatmodel (e.g., Google Gemini) for a new sentence in the target language
• Include translation, pronunciation, grammar, and spelling tips
• Send the output to a Discord channel via webhook

This felt like the perfect fusion of AI, automation, and a personal need.

Turning to tutorials

Realizing I needed a stronger foundation, I dove into some n8n tutorials:

1. Your First n8n Workflow
   This tutorial helped me understand how to structure a simple workflow from trigger to output.

2. Intro to Advanced AI Workflows
   This one introduced the concept of using AI models within workflows — exactly what I needed.

After going through these, things started to click.

The first major roadblock

I created my workflow: a Schedule Trigger connected to an AI agent and ending in a Discord webhook. It worked beautifully when I ran it manually, but… nothing happened when the scheduled time passed.

Problem:

The Schedule Trigger didn’t fire on its own.

Solution:

• My Docker container was running, but n8n wasn’t in active/production mode.
• I updated my Docker container’s environment variables:
  • N8N_EXECUTIONS_MODE=own
  • TZ=Europe/Brussels (to match my timezone)
• I also switched the trigger from interval to cron (45 9 * * *) for reliability.
• Finally, I ensured the workflow was activated and saved.

After these changes, the scheduled execution worked like a charm.

Early results & simplifying things

Initially, I tried to go way too complex — chaining advanced AI nodes, formatting logic, and conditionals. Eventually, I scaled it back to something simpler just to start getting data flowing and build confidence.

The minimal setup: Schedule Trigger → AI Agent → Discord Webhook

An AI agent is not fully required as this could have been configured as an automation workflow, this was purely for testing purposes. This project provided value and a great learning experience.

Next steps & ideas

Here’s where I want to take this next:

• Interactive Discord bot: Respond to messages in a channel, maybe even answer grammar questions.
• Clean-up logic: Automatically delete older language messages to keep the channel fresh.
• Add memory node: Track which sentences have already been sent and avoid duplicates (or regenerate).
• Logging to Google Sheets: Keep a log of daily sentences and explanations for review.

Final thoughts

Exploring automation with n8n + AI has been one of the most satisfying and productive rabbit holes I’ve gone down in my homelab journey. With a bit of trial and error, and help from the excellent n8n docs and community, I now have a working system that automates part of my language learning journey — and that’s just the beginning.

If you’re on the fence about diving into automation or AI integration, my advice? Just start — even the mistakes are great teachers.

Want to see more of my homelab experiments? Let me know — happy to keep sharing!