Chatbot vs AI agent: what is the difference, and which one does your business need?

The simplest way I explain it to clients: a chatbot talks, an AI agent acts. A chatbot answers the question a person asks, and the conversation ends. An agent gets a goal (“handle this enquiry”) and takes several steps on its own: it reads the email, finds the customer in the CRM, checks the order status, drafts a reply and flags the task for the team.

Both can run on the same language model, so from the outside they look alike. The difference is what they are allowed to reach. A chatbot usually has a knowledge base and a contact form. An agent has access to tools: email, calendar, CRM, inventory or invoicing. That access is exactly why an agent needs more work before launch and clearer rules.

So “which one is better” is not quite the right question. The right question is whether your problem is unanswered questions or repetitive manual steps. A chatbot solves the first, an agent the second.

There is also a middle option I often suggest: a chatbot that answers questions and takes one or two simple actions, such as booking an appointment in the calendar or creating a contact in the CRM. Strictly speaking, that is already agent behavior, but the risk is low because the actions are tightly defined. Starting this way shows you how customers behave before you decide whether you need more.

Running costs for both are made of similar parts: language model usage (billed by the amount of text processed), the platform or server everything runs on, and maintenance. A chatbot mostly gives short answers, so model costs per conversation are small. An agent may call the model several times for one task and connect to other systems along the way, so each task costs more.

On the other hand, an agent’s value is measured differently. A chatbot saves time on answering; an agent takes over a whole chain of work a person used to do. If an agent handles dozens of identical enquiries every day, the higher cost per task is usually still less than an employee’s time. Model providers change their prices often, so check current rates before you do the math.

My own pricing is simple: the first solution starts at €450, and support starts at €150/month. What your case will cost depends on how many systems need to be connected and how many steps the agent takes without human approval.

Picture a small furniture workshop in Kaunas: five people, a website with an order form, Gmail, and a Google Sheets file where orders are tracked. Most enquiries repeat: “how much is a wardrobe in these dimensions,” “when will my order be ready,” “do you deliver to Klaipėda.” The owner answers emails in the evening because during the day he is in the workshop.

The first step I would suggest is a chatbot on the website. It answers questions about materials, lead times and delivery, and it collects quote requests with dimensions and a photo, so the owner does not have to ask again. It goes live quickly, and you can see right away how many questions it closes on its own.

The second step is an agent, once it is clear where work still gets stuck. Take “when will my order be ready”: the agent finds the order in the spreadsheet, reads the status and drafts a reply. At first it sends replies only after the owner approves them; later it handles the simple cases itself. This is an illustrative example, but this order usually pays off: chatbot first, then an agent for one specific process.

If you get a few enquiries a week and answer them in minutes, a chatbot will not save you anything. A clearer FAQ page and a quick phone reply work better. Likewise, an agent makes no sense if the process is different every time and the decision depends on a person’s judgment rather than rules.

Another warning sign is messy data. If orders live in three different places and prices live in the owner’s head, neither a chatbot nor an agent has anywhere to get correct answers from. In that case, set up one source of truth first and add AI later.

I say this plainly in consultations: if I see that a solution will not pay for itself, I suggest not building it, or starting with a simpler step.

Chatbots and agents both process customer data, so GDPR applies: you need to know where data goes, how long it is kept and who can see it. I choose providers that process data in the EU or at least have a clear data processing agreement. The EU AI Act also requires that people know they are talking to AI, so the chatbot always introduces itself.

For an agent, the key is the limits on what it can do. It gets only the permissions a specific task needs: read but not delete; write a draft but not send it without approval until we have checked how it behaves on real cases. Every action is logged, so you can trace what happened and why.

One more practical point is who answers when the AI does not know. I always set up a clear handoff to a person: if a question falls outside the knowledge base or the customer is unhappy, the conversation and its full history go to you by email or another agreed channel. The same rule applies to an agent: when in doubt, it asks instead of guessing.