AI agents in business: how they differ from chatbots

An AI agent is a program built on a language model that steers its own work. It decides which steps to take, which tools to use and when it is done. A chatbot, by contrast, mainly answers questions. An agent with access to your systems can also complete the task: find the documents, create a record or update a ticket. To do this reliably, it needs the right data, well-described tools and clear rules for when to stop.

Chatbot, fixed workflow and agent: three different things

In its study Building effective agents, which draws on work with dozens of teams, Anthropic distinguishes two types of so-called agentic systems.

A workflow is a process written in advance by a developer. The model and the tools follow the steps exactly as defined. For example: receive an inquiry, assign it a category, draft a reply, check the draft against the rules. The result is predictable.

An agent is a system in which the model itself directs the process and chooses the tools. Nobody knows the path to the goal in advance; the agent decides as it goes. This suits open-ended tasks where you cannot tell how many steps will be needed.

A chatbot is the simplest case: a person asks, the model answers. Anthropic notes that customer support combines the familiar chat interface with tool integration. Such a system can pull up customer data, order history and knowledge base articles. It can also take an action, such as updating a ticket. The move from plain chat to an agent is a gradual slope, not a sharp line.

What an agent actually does

According to Anthropic, agents are typically language models that use tools in a loop and evaluate feedback from their environment. A run looks like this:

1. The agent receives a task from a person, or clarifies the task in conversation.
2. It plans the steps and starts working on its own.
3. At every step it checks the real state of things. Anthropic calls this 'ground truth': actual data from the environment, such as the result of a query to a system. The agent uses it to tell whether it is on track, instead of relying on its own guess.
4. If it is stuck, or reaches a point that a human should approve, it stops and asks.
5. It finishes when the work is done, or when it hits a stopping condition, such as a maximum number of steps.

The last point is essential. An agent with no limit and no checkpoints is a risk. Anthropic itself warns that autonomy means higher costs and the possibility of compounding errors. It recommends extensive testing in a sandboxed environment, along with guardrails.

What an agent needs in order to work

Anthropic describes the basic building block as a language model augmented with retrieval, tools and memory. For a company, that translates into four concrete tasks.

Access to the right data. The agent must be able to find the information it needs: contracts, price lists, procedures, customer history. Without it, it will simply guess with confidence.

Well-described tools. A tool is anything the agent is allowed to use: looking up an order, sending an email, running a calculation. According to Anthropic, a good tool definition includes example usage and edge cases. The clearer the description, the fewer mistakes the agent makes.

Memory. The agent has to retain what it has already found out and done, so it does not lose the thread during a long task.

Rules. When to ask a human, how many steps it may take, what it must never do. Nobody will fill these in for you.

How an agent connects to company systems

An open standard called MCP (Model Context Protocol) was created to connect agents with data and tools. Its official documentation compares it to a USB-C port: one uniform way to connect AI applications to external systems. Through MCP, applications can connect to data sources (such as files or databases), tools (such as search engines or calculators) and prepared workflows.

According to the MCP documentation, it is supported by products such as Claude, ChatGPT, Visual Studio Code and Cursor. One example it gives is an enterprise chatbot that connects to several databases across an organization. The documentation also has a separate section on security (Security best practices). The more systems you open to an agent, the more carefully you must define what exactly it may access.

Do you need an agent at all? Questions to ask

Anthropic recommends starting with the simplest possible solution and adding complexity only when it demonstrably improves results. Agentic systems usually increase both latency and cost. For many applications, the study says, it is enough to improve a single model call that uses retrieval and examples in the prompt.

Go through these questions from the top and stop at the first one you can answer 'yes'.

Anthropic names customer support and software development as suitable areas. What they share are clear success criteria, the ability to verify results and meaningful human oversight. Check for the same three things in your own task. If you cannot recognize a good result, an agent will not help you.

What the EU AI Act asks of you

If people in your company work with AI, Article 4 of the EU AI Act on AI literacy applies to you. It has been in effect since 2 February 2025. Under it, providers and deployers of AI systems must take measures to improve AI knowledge among their staff and other people who operate or use AI systems on their behalf.

The regulation says the measures should take into account people's technical knowledge, experience, education and training, the context in which the system is used, and the persons affected by its use. It also does not require you to guarantee a specific level of literacy for every individual. According to the text, the Commission and the member states are to give particular support to small and medium-sized enterprises.

As an example of national guidance, BusinessInfo.cz, the Czech government's business portal run by the Ministry of Industry and Trade, says that for most smaller firms a targeted, practical training course on the tools they actually use is enough. What matters is that the training is documented.

A four-step approach for your company

BusinessInfo recommends starting with mapping, training and simple record-keeping. When you plan to deploy an agent, add the following:

1. Map what people already use. List the AI tools, including the ones employees found on their own and the AI features hidden inside everyday software.
2. Train people by role. Anyone who will work with the agent must know what it can do, what it cannot do and when not to trust it.
3. Keep a simple list and internal rules. A table is enough: the tool, what it is for, what data it can see, who is responsible for it.
4. Name a responsible person. According to BusinessInfo, this need not be a new position; it is enough to extend the duties of an existing colleague.

Only then choose the first task for an agent. The ideal one has an easily verifiable result, a limited scope, and a human who can approve every important step. Start in a test environment, set a step limit, and evaluate whether the agent really improved the result compared with a simpler solution. Only after that should you give it access to more data and tools.

An illustrative example

Imagine a company that handles product complaints. A chatbot would tell the customer how complaints generally work. A fixed workflow would categorize the complaint and prepare a reply from a template. An agent could look up the order, compare it with the complaint terms, propose a solution and have an employee approve it before anything is sent. This is only an illustration, not a result from a real company. It shows where the difference lies: the agent acts on your data and with your tools, so it needs rules and oversight.

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Prepared by AI from the listed sources and checked automatically against them. Not legal advice.