What is an AI agent and how does it differ from a chatbot

An AI agent is a program that receives a task, decides for itself which steps will accomplish it, and also carries those steps out — it reads a document, writes a row into a system, sends an email, checks the stock. A chatbot, by contrast, holds a conversation: it answers a question, gives advice, explains, but doesn’t change anything in any system itself. The difference isn’t which one is “smarter”, but whether it has tools in its hands or only language.

How does an AI agent differ from a chatbot?

A chatbot on a website or in an app answers questions from what it knows — from the text you give it, or from what it has been trained to say. When you ask it about the status of an order, at best it tells you where to find that information. It won’t open the e-shop system for you and find out itself.

An AI agent has not only a language model but also access to tools — to your email, CRM, warehouse, invoices. When it gets the task “check the status of order 4521 and reply to the customer”, it works out for itself where to find that order, looks it up and writes the reply. It makes decisions while it works, not just at the start according to a pre-written script.

What is an agent made of?

Three things turn a language model into an agent:

• A language model (LLM). It understands the assignment, decides between steps and formulates the output — text, a structured record, a reply to a customer.
• Tools and integrations. Interfaces through which the agent reads and writes data — your ERP’s API, an email inbox, a warehouse system. Without them, the model only talks about what it would do.
• Memory and context, or RAG. So that the agent knows what happened in the previous step, and can look up specific data in your documents instead of giving a generic answer.

Without any one of these three things it isn’t an agent, just a chatbot with a better prompt.

What does such an agent look like in practice?

Concrete examples are clearer than a definition:

• An order arrives by email as unstructured text. The agent extracts the items, quantities and address, creates the order in the system and confirms receipt to the customer. The procedure is described in the article on automated invoice and document processing — the principle for orders is the same.
• A PDF invoice arrives from a supplier. The agent extracts the amount, the variable symbol (the payment reference used in Slovakia) and the items, assigns them to the right cost centre and prepares the invoice for approval — it doesn’t post it itself, it only prepares it.
• A customer writes to support with a complaint. The agent looks up the order, checks its status according to your rules and either replies straight away or hands the case over to a colleague with the supporting information.

In all three cases, the agent doesn’t make a decision that carries risk without oversight — that is by design, not a limitation.

Where does a person stay in the process?

For sensitive steps — a payment, a contract, a price outside the usual range — the agent prepares the groundwork and leaves the decision to a person. This is called human-in-the-loop: the machine does the repetitive work that takes time, the person signs off on what they are responsible for. Without this safeguard an agent can be fast, but at the first exception nobody expected it will do damage before anyone notices.

What does an agent run on?

Simpler automations — moving data between two systems, triggering by a rule — can be built on an off-the-shelf tool such as n8n or Make. An overview of how they differ and when each is enough is in the article n8n, Make or Zapier: what to choose for automation. An agent that is supposed to make decisions and work with your documents across several systems is usually custom-built — the tool is chosen according to the task, not the other way round.

When does an agent pay off and when doesn’t it?

An agent makes sense where a process repeats, can be described with rules and takes time rather than judgment — retyping orders, sorting inquiries, extracting data from documents. It doesn’t make sense where the process changes every month, happens a few times a year, or where context and accountability decide, which you don’t want to delegate to software. How to calculate the return in advance is covered in the article How much an AI agent costs in a company, and when it pays off.

Before you embark on your first agent, it’s worth choosing a process where it really fits — not the most visible one, but the one with the best ratio of benefit to risk.

If you’re considering where to deploy an AI agent in your company, take a look at AI process automation — a pilot on one process with measurement costs from €2,900 and takes 1 to 4 weeks.