What is an AI agent? The difference from chatbots and classic automation

The term AI agent is currently used for very different things – from a simple chatbot to complex systems that act autonomously. A clear distinction helps set the right expectations: An AI agent plans the necessary steps itself and carries them out independently, based on the connected systems. Simple cases are completed, while complex ones are passed on to a team with full context.

AI agent vs. chatbot

A chatbot answers questions – it provides information. An AI agent goes one step further: based on a request, it can carry out several actions in different systems, for example check a request, update a record, and send a confirmation. The chatbot responds, the agent acts.

AI agent vs. traditional automation

Traditional automation (also known as RPA) follows fixed, predefined rules: When X occurs, do Y. This works reliably as long as the case exactly matches what was programmed. As soon as a task involves multiple steps, decisions, or access to different systems requires, this logic reaches its limits – every deviation needs a new rule branch. An AI agent, by contrast, plans the path to the goal independently, instead of having every case preprogrammed individually.

Where the boundary is deliberately drawn

A well-configured agent does not take over everything. Simple, clear-cut cases are completed independently. Complex or consequential cases are passed to a team with full context – the agent prepares, but does not decide where the decision requires responsibility that a human should carry.

• Chatbot – answers questions from a knowledge base
• Classic automation – executes fixed rules for predefined cases
• AI agent – plans and executes multi-step tasks independently, handing over to humans when needed

Practical example: A multi-step request

A customer reports a problem with an order. A chatbot would look up opening hours or general information. Classic automation could map a rule such as “create a ticket when the keyword complaint appears”. An AI agent, by contrast, checks the order status in the system, identifies the error case, initiates the appropriate action – for example, a replacement delivery – and informs the customer, while more complex complaints are passed to a human with full context.

By the way: The Consulting and guided implementation of such tools is eligible for funding. Through INQA-Coaching 80% of consulting costs are covered nationwide, up to €11,520. Funding applies to the implementation, not the software itself – the funding check shows in just a few minutes what is eligible for you.

Frequently asked questions about AI agents

Is an AI agent the same as ChatGPT?

No. A language model like ChatGPT answers requests in text form. An AI agent uses such a model for planning, but also carries out concrete actions in connected systems.

Does an AI agent make decisions completely autonomously?

Only within clearly defined boundaries. Simple, unambiguous cases are completed independently; everything else is passed to a team with full context — this boundary is deliberately defined during setup.

When does an agent make sense compared with traditional automation?

As soon as a task involves multiple steps, decisions, or multiple systems and cannot be sensibly captured in rigid rules.

Can it still be traced what an agent has done?

Yes, that is a prerequisite for productive use: every action of an agent is logged and remains traceable.

Next step

More on the technical implementation on the product page AI agents. For an assessment of your use case, you can reach us via the contact page.

As of September 2026.