At Relevance AI, we’re building an agentic automation platform. We often get compared to AI workflow solutions like n8n or Zapier. It’s a natural comparison—we both help teams automate work and both integrate deeply with LLMs.

But this comparison misses something fundamental about what each tool is designed to do.

The likes of Relevance and n8n solve different problems; understanding the distinction is key to building an effective automation strategy.

This post is quite theoretical, but the goal is to create a really clear foundation as to what type of work makes most sense for AI Agents.

Firstly, it’s not about LLMs

The most common misconception about agentic automation is that it’s just traditional automation with the introduction of LLMs.

Browse any workflow automation platform—n8n, Zapier, Make—and you’ll find LLM integrations everywhere. GPT-4 for text generation, Claude for summarization, even “agentic modules”.

Workflow automations have gotten way better thanks to LLMs. Agentic automation has been made possible thanks to LLMs.

The presence of language models tells you nothing about which type of automation you’re looking at. There’s a more fundamental distinction to be found.