When does an AI agent make sense for your business?

What is an AI agent?

An AI agent is software that completes a task by itself in a few steps. It fetches information, chooses the next step and carries out something in a system. For example preparing a draft reply, updating a record or sending a notification.

The heart of an agent is a language model: the same kind of AI as behind ChatGPT, Copilot or Claude. The difference lies in what surrounds it. An agent gets instructions, access to knowledge and a number of actions it is allowed to perform. Microsoft describes it like this: agents can retrieve information, summarise data and even take actions, such as sending emails or updating something in a system.

The word agent is used a lot at the moment, and by no means everyone means the same thing by it. So look mainly at what a system does, not at what it is called. A short explanation of the term is under AI agent.

What is the difference between a chatbot, an AI assistant and an AI agent?

The difference lies in how much the software does by itself. In plain language:

The boundaries are not sharp. A chatbot that puts an appointment in your calendar is already doing something of an agent's job. And an AI assistant that searches your email also uses your systems. For the choice, it does not matter much what you call it. What does matter is how much the software is allowed to do without a person keeping an eye on it.

How does an AI agent work?

An AI agent works in a loop of reading, choosing and doing. Step by step:

1. Something comes in. An email, a form, a document or a point in time.
2. The agent reads it and looks up what it needs, for example the customer details from your CRM.
3. It chooses an action from the list you give it. For example: make a draft reply, or forward it to a colleague.
4. It carries out the action through an integration with your system, or it prepares it for approval.
5. It checks the result and stops, or moves on to the next step.

To do this, an agent needs four things: instructions (what should it do), knowledge (where may it search), actions (what may it do) and limits (what may it not do). That last one is at least as important as the rest.

Is an AI agent the same as automation?

No, but they are close. In ordinary workflow automation, all the steps are fixed: if this happens, do that. With an agent, a language model chooses which step fits.

Fixed rules are predictable and cheap. A language model is more flexible, but sometimes makes mistakes. That is why a mix often works best. You do the fixed steps with ordinary automation. You leave only the part where text needs to be understood to AI. For example: what does this customer actually want?

Can a task be done entirely with fixed rules? Then you do not need an agent. That is cheaper and easier to check.

Which tasks are suitable for an AI agent?

Suitable tasks are those that come up often, have clear rules and where a person can check the result. Think of:

• Reading and sorting incoming email and preparing a draft reply.
• Comparing an order confirmation from a supplier with your own purchase order, and reporting differences.
• Completing an enquiry with data from your systems, so a colleague can get on with it straight away.
• Going through a file and reporting which documents are still missing.
• Taking data from a document and putting it into your administration as a draft.

In all these examples, the agent prepares the work and a person keeps the final decision. That way you work with AI without a mistake going straight to a customer.

Which tasks are not suitable?

Unsuitable tasks are those where a mistake does a lot of damage, or where there are no clear rules. For example:

• Decisions about people. Rejecting a job applicant or refusing a credit application. Under the GDPR, a decision that significantly affects someone may as a rule not be taken by a computer alone (Article 22). If an exception applies, that person as a rule has the right to human intervention.
• Payments and other actions you cannot undo. Have a person approve them.
• Tasks without a fixed way of working. If your colleagues do it differently every time themselves, an agent will not know what is right either.
• Tasks that rarely occur. Then building and testing is more work than the task itself.
• Work with messy data. If customer details are duplicated or out of date in your system, clean that up first.

Why should an agent work in your own systems?

An agent is only useful if it can get to the right data and puts the result in the right place. An agent that stands apart from your systems leaves you copying and pasting again. Then it is of little use to you.

Working in your own systems goes through an API: the fixed entry point of a software package. A few rules apply:

• As few permissions as possible. Is the agent only allowed to read? Then do not give it write permissions. Use a separate API key for the agent, not an employee's.
• Record everything. Record what the agent did and why. Then you can trace mistakes.
• An off switch. You must be able to stop the agent without the rest of your systems grinding to a halt.

What are the risks of an AI agent?

The biggest risks are mistakes that nobody notices, and an agent that can do more than necessary. In a list:

• Made-up information. A language model can claim something that is not true. This is called a hallucination.
• Hidden instructions. An agent that reads email or web pages can come across text there that makes it do something else. This is called prompt injection. Large providers, such as Microsoft, also build separate security for this.
• Too many permissions. The more an agent is allowed to do, the greater the damage if something goes wrong.
• Unclear responsibility. Agree who manages the agent and who gets a notification if something goes wrong.

You limit those risks with the same measures: few permissions, a person who approves actions with consequences, and a log. Providers also offer settings for this. In ChatGPT, for example, you set per connected app whether ChatGPT first asks for permission before it changes anything. And in Microsoft Copilot, an administrator decides which agents are allowed in the company.

Which rules apply to an AI agent?

The same rules apply to an AI agent as to other use of AI, plus a few extra points of attention:

• GDPR. If the agent processes personal data, the GDPR applies. Do you have another party do that, such as an AI provider or a developer who hosts the system? Then you conclude a data processing agreement with that party.
• Transparency. Does the agent talk directly with customers, for example in a chat? Then under the EU AI Act those customers must know that they are dealing with AI. That rule has applied since 2 August 2026 (Article 50(1)).
• AI literacy. Businesses that use AI take measures to support the AI literacy of their people (Article 4). Anyone who works with the agent must know what it does and where it can go wrong.
• Your own agreements. Record in an AI policy which tasks an agent may do, with which data, and who checks.

This is an explanation in plain language, not legal advice.

Can you buy an AI agent, or do you have one built?

Both are possible. Large software packages now have their own agents. In Microsoft Copilot you can use and create agents that work with your Microsoft 365 data. And in ChatGPT you can connect apps, such as Google Drive or Slack, and have ChatGPT carry out actions in them.

Such a standard agent is enough if your work mainly happens in that one package. Custom work is a better fit if the agent has to work in systems a standard agent cannot reach, such as your accounting, your scheduling or an industry-specific package. Or if the agent has to follow your own rules, with a fixed step in which a person approves.

Often an AI assistant is already enough, without an agent. Do you mainly want to ask questions of your own manuals and procedures? Then read What is an AI assistant on your own documents?

How do you start small with an AI agent?

Start with one task, let the agent only make suggestions at first, and only expand once that goes well. This is how you go about it:

1. Choose one task that comes up often and has clear rules.
2. Write down the rules as you would explain them to a new colleague. What is allowed, what is not, and when do you ask someone?
3. Let the agent only make suggestions at first. A person approves or rejects each suggestion.
4. Test with real examples from the past few months, and see where it goes wrong.
5. Only give it more freedom once the suggestions have been good for a while. Actions with consequences for customers or money stay with a person.

What does it cost to have an AI agent built?

That depends on the task, the number of systems the agent works in and how much human oversight you want. The more steps the agent takes by itself, the more has to be built and tested. That is why with me you start with a business analysis. In it, I look at which task is suitable and which is not. Then you get a proposal with a fixed price, so you know in advance where you stand. The proposal states what I build: one improvement, connected to your systems and explained to a colleague. It also states what is added per month for hosting, subscriptions and maintenance. Also read how your price is determined.

How do I help you with an AI agent?

I first check whether an agent suits your task, or whether a simpler solution is enough. If it does suit, I build the agent myself, in the systems you already use, with a person who checks. I do not receive commission from suppliers. Read more about AI in your business, or schedule a conversation. Not sure who you need? Read What does an AI consultant do?

Frequently asked questions

Sources: Microsoft Learn: Compare declarative and custom engine agents (what an agent is), Microsoft Learn: Data, privacy and security for Microsoft Copilot (prompt injection and managing agents), OpenAI Help: Managing app permissions in ChatGPT, Dutch Data Protection Authority (Autoriteit Persoonsgegevens): text of the GDPR (Article 22), Dutch Data Protection Authority: AI transparency requirements apply from 2 August, Dutch Data Protection Authority: Data processing agreement and Regulation (EU) 2026/1744 (Article 4). Consulted on 7 October 2026.