What is an AI agent? An explanation for businesses

An AI agent is software that uses a language model to work through a task by itself: it reads a request, looks up what it needs in your systems and takes the next step, within limits you set. Anything outside those limits goes to a person. At Goudbeek in Almere we build agents for customer service and back offices.

The word gets used loosely. Sometimes it means a chatbot with a new label, sometimes a system that changes orders without asking anyone. For a business, the useful question is not whether something is called an agent, but what it can read, what it can do, and when it stops.

How is an AI agent different from a chatbot?

A chatbot holds a conversation. It answers a question, and then it is up to a person to do something with that answer. A good chatbot that answers from your own product information works from your catalogue and terms, but its job ends at the text.

An agent finishes a piece of work. It looks up an order, fills in a field, routes a ticket or drafts a reply, and decides for itself which step comes next. The difference is less about technology than about what the software is allowed to touch: a chatbot talks, an agent acts.

It also differs from classic process automation. A fixed rule does the same thing every time with tidy input, such as a web form. An agent can handle an email a customer wrote in their own words. That is exactly what makes it useful, and exactly why it needs limits.

What does an AI agent need to work well?

Three things decide whether an agent is any good: the sources it works from, the access it is given and the limits it stays within. The language model itself is often the least decisive part.

Sources are the material it answers and decides from: help pages, terms, price lists, order data, manuals. Not whatever a model happens to remember. When the answer is not in those sources, an agent should say so rather than make something up.

Access is about which systems it may read and where it may write. It is tempting to connect an agent to a whole mailbox or an entire shared drive, because that is what the integration offers. It is better to decide per task which sources are needed and open only those.

Limits define what it may do without a person. A practical dividing line runs between reversible and irreversible actions: looking something up, summarising and drafting can be undone; sending, paying and deleting cannot. Add a log of every step and every source it used, and an off switch that sits with you. Why that is a design decision rather than a setting is covered in what an AI agent should be allowed to do.

Watch out for text from outside, too. An email or an uploaded PDF can try to pass itself off as an instruction to the agent. This is called prompt injection, and it is on the OWASP Top 10 for applications built on language models. How to limit the damage is covered on our page about AI security and scoped access.

Where does an AI agent fit in a business?

An agent fits where the same kind of request keeps coming back, the answer lives in your own systems, and a colleague now mostly searches, copies and retypes. That usually means customer service or the back office.

Picture a webshop that gets emails every day about where a parcel is, about exchanges and about returns. An agent reads the email, looks up the order and the shipping status, and drafts a reply with the right details. A return above an amount the owner chooses, or an email that sounds like a complaint, goes to a member of staff with the draft attached.

Picture an installation company where fault reports come in by email and through a form on the website. An agent can match the report to the customer and the installation, ask for missing details such as the appliance type or a photo, and prepare a suggested slot in the schedule. The planner still decides, but no longer starts from half a report.

Picture an accountancy firm that receives receipts, invoices and loose questions from its clients. An agent can sort the documents per client, check against a fixed checklist whether a file is complete, and prepare a reminder for anything missing. Real advice questions stay with the adviser.

In all three examples the agent does the groundwork and the judgement stays where it belongs. What that looks like in practice is described under AI agents for customer service and back-office work.

When is an AI agent the wrong choice?

Not every problem needs an agent. If a fixed rule, a better form or a template solves the same thing, that is cheaper, more predictable and easier to maintain.

Nor is an agent a fix for a process nobody can explain. If three colleagues handle the same request in three different ways, an agent mostly speeds up the confusion. In that case the place to start is mapping and improving the process itself.

An agent should also not decide on its own where a lot is at stake and little can be undone: a large payment, legal or medical advice, a complaint that deserves attention. For some uses, such as systems that select job applicants, the European AI Act sets stricter requirements as well. For what applies to your use, read the official text.

And if the knowledge only lives in someone's head, an agent has nothing to work from. Then the first step is writing it down, before any software is involved.

How do you get started with an AI agent?

Start small and concrete: one queue, one kind of request. Pick work that keeps coming back and where you can already tell what a good answer looks like, such as delivery questions or one type of report.

Next, write down how an experienced colleague handles it today. Which sources do they open, which exceptions do they know about, and when do they pass it on? That list becomes the basis for the agent's rules.

Then build one prototype on your own data, not on a demo with sample records. Let it write drafts only, which a person checks and sends. What your colleagues change in those drafts shows you where the sources or the rules still fall short.

Only once a type of request demonstrably goes well do you let the agent handle it on its own. For part of the work an agent often stays on drafts, and that is a choice, not a shortcoming.

How does Goudbeek approach an AI agent?

We start with the process, not the technology. First we map how the work runs today, then we choose the simplest thing that works. Sometimes that is an agent, sometimes another of our AI solutions for businesses, sometimes a form or a fixed link between two systems.

If it does become an agent, we agree a fixed price up front after a short intake. We build one working prototype on one real queue with your own data, and put in writing what it may see, what it may do and what waits for a person.

After that the agent runs on managed hosting from €39 a month, and we adjust it based on real use. The AI model itself is billed separately, on usage. The rules stay yours, and so does the off switch. Read more about how we build AI agents for businesses, or get in touch to talk through your own queue.