For a couple of years, the pattern for AI assistants was almost always the same: the user asked, the system fetched a handful of relevant chunks from a knowledge base and, with that, wrote an answer. That technique, classic RAG, works well for answering questions. But in 2026 the conversation has shifted: it's no longer enough for AI to answer, we want it to act. And that's where agentic RAG comes in.
The difference is deeper than it looks. In traditional RAG, the flow is fixed in advance: retrieve and answer, every time. In the agentic approach, the model itself decides what it needs to look up, when, and how to combine the information, and it can even take several steps —consult a source, reason about the result, consult another— before reaching a conclusion or carrying out an action. It goes from being a search engine with good phrasing to something closer to a collaborator that knows how to use tools.
Why everyone's talking about it
The underlying reason is that companies no longer want just a chatbot that replies; they want systems able to complete tasks from start to finish. And many of those tasks are too complex for a single model working alone, which is why the idea of several specialised agents collaborating is gaining ground, each handling a part of the problem. It's the difference between an assistant that tells you how to do something and one that, with the right permissions, does it for you.
What it means for a normal business
You don't need to build a multi-agent system to feel the change. The key takeaway is that a well-designed assistant today can go beyond answering questions: it can check an order's status and, where appropriate, start a return; it can search your catalogue and prepare a quote; it can gather data from several places and leave you a summary ready to decide on. The important thing, as with any AI connected to real data, is to give it clear limits and keep a human in control where the decision deserves it.
At Luxion we already build assistants connected to real data —like the one for BigBuy— and the step towards agentic capabilities is the natural evolution of that work. If you can picture an assistant that doesn't just inform but resolves, we'll show you a prototype before committing to anything.