A chatbot answers a question. An agent takes a goal, decides which steps are needed and does the work itself by using tools.
The difference between a chatbot and an agent
If you ask a chatbot "where is last month’s sales report?", it writes you an answer. Give an agent the same job and it finds the report, pulls the numbers, summarizes them and emails the right person.
The difference is between a language model that only produces text and one that can use tools. An agent has access to tools such as database queries, API calls or file reading, and decides for itself in which order to use them.
The parts of an agent
- Language model: The part that plans and decides
- Tools: The connections that let the agent act in the outside world
- Memory: Remembering earlier steps and the conversation
- Boundaries: What it is allowed to do and where it must ask a human
Where is it useful?
Jobs with known steps that still need small decisions each time suit agents well: classifying incoming requests and routing them to the right team, preparing quotes, entering invoice details into a system, compiling the weekly report.
What to watch for
When an agent makes a mistake, you do not see it as a chat reply; a wrong email has gone out or the wrong record has changed. So permissions should be kept narrow, human approval should be required before irreversible steps, and every step should be logged.
A well-designed agent is not the one that does everything alone, but the one that knows what to do and what to ask.