Agentic AI System

Most chatbots work according to a simple pattern: you write something, the program responds, done. An agentic AI system goes beyond that. It receives a goal and then works through several steps independently, without you having to trigger each individual one. In doing so, it’s also allowed to do things and not just talk: search the internet, open a file, send an email, run a small program. After each step, it looks at the result and decides what makes sense to do next. The word “agentic” comes from the Latin agere, to act — that’s exactly what this is about.

From answer-giver to contractor

The difference sounds small, but economically it’s huge. A chatbot saves you time thinking. An agent is supposed to take an entire task off your hands. Instead of “write me a sample email to a customer,” the request becomes “find all customers who haven’t bought anything in six months, and write each of them a suitable email.”

That’s why so much money is currently flowing in this direction. Companies normally pay for software per user per month. An agent that replaces work hours can be sold for much more. Major providers like OpenAI, Google, Microsoft, and Anthropic have introduced almost nothing but agent products in 2024 and 2025. When business news talks about “agentic AI,” this promise is usually what’s meant.

At the same time, the risk is rising. A chatbot that states nonsense is embarrassing. An agent that states nonsense and then independently places an order or deletes data causes real harm. Experts call this the question of permissions: how much is such a system actually allowed to touch? Many companies therefore only let agents work internally or require human confirmation for critical steps.

Plan, grab a tool, verify

At the core of an agent sits a language model — a program that has learned to continue texts in a meaningful way. Around this model, three things are built. First, a loop: the model is called again and again, rather than just once. Second, tools, meaning programs it’s allowed to call. Third, a memory, in which the previous steps and results are stored.

A run typically looks like this. The model breaks the goal down into sub-steps. For the first sub-step, it doesn’t write a normal sentence but a structured command like “search for train connection Hamburg Munich March 14.” The surrounding program recognizes this command, calls the actual search, and puts the result back into the text. Then the model runs again, now with the new knowledge. This cycle repeats until the goal is reached or a limit kicks in.

This very loop is also where the biggest weakness lies. Errors add up. If each step succeeds 95 percent of the time, twenty steps are completely error-free in only about one out of three cases. Some agents also go in circles and try the same unsuccessful idea multiple times. This whole setup is often confused with automation in the classic sense: there, a human defines the sequence in advance, whereas with an agent it only emerges at runtime.

Agents in the browser, in the code editor, and in support

Agents are furthest along in programming. Tools like GitHub Copilot Workspace, Claude Code, or Cursor receive a bug description, find the relevant spot in the project, change the code, and run the automated tests. If a test fails, they try again. This works well here because a test gives clear feedback: passed or not.

There are also agents that control a browser and fill out forms or compare prices for you. In customer service, they process refunds by actually looking into the order database. Such systems are also being tested at financial firms, for instance to compile reports from multiple sources.

When reading marketing promises, some skepticism is worthwhile. Not every product called an “agent” actually plans on its own; often it’s a chatbot with a few fixed workflows. A good test question is: is the system allowed to change something, and does it decide the order of its own steps? Only then is it agentic in the proper sense.