Underneath every AI agent capable of real action is a technical mechanism called tool use: the ability for the model to recognise when an external action is needed and to call it with the right parameters, instead of merely describing it in words.
How it works in practice
The model is given a list of available tools, each with a precise description of what it does and what data it needs. During the conversation, when it recognises that a request matches one of them, the model generates a structured call with the necessary parameters, which the system then actually executes.
The action-observation loop
After execution, the result goes back to the model, which uses it to decide the next step: if data was missing, ask for it; if the operation succeeded, confirm it to the user; if it failed, handle the error openly. That loop — act, observe, decide next — is what distinguishes an agent from a single isolated answer.
Why the tool descriptions matter so much
The quality of an agent depends largely on how clearly the tools available to it are described: an ambiguous tool leads to wrong calls or needless hesitation. Getting that part right, invisible though it is to the end user, is what makes an agent dependable rather than unpredictable.