AI Agents, RAG, and N8n: How Companies Can Move from Experimentation to Real Automation
What does an AI agent actually do, beyond the hype?
In this Commit & Push episode, Damien Filiatrault sits down with Micah Johnson, co-founder of Biggest Goal, to unpack a question many teams are still struggling to answer clearly: what is an AI agent, and where does it create real business value?
Their conversation moves from definitions into practical examples. They cover how agents differ from basic chat interfaces, where retrieval-augmented generation (RAG) fits into the picture, why tools like N8n have become so popular, and why so many company AI initiatives stall before they create meaningful results.
What an AI Agent Really Is
Micah’s definition is refreshingly practical. Instead of treating an agent as an abstract AI buzzword, he describes it as an AI-powered system that has instructions, access to tools, and the ability to decide what actions to take and in what order.
That distinction matters.
A normal chat interaction is usually one prompt followed by one response. An agent is different because it can loop. It can examine its instructions, decide which tool to use, take action, evaluate whether the task is complete, and continue until it reaches a stopping point.
In other words, the leap is not just “AI that answers questions.” It is AI that can operate.
That also means an agent does not always need a human to initiate every step. In many business settings, an agent can sit behind the scenes waiting for a trigger, such as an event inside Monday or ClickUp, and then take action automatically. That kind of workflow begins to look less like a chatbot and more like a digital operator embedded inside a company’s systems.