What are AI agents?

From answering to doing

A normal chat interaction might be: you ask a question, the model answers. An agent-like system adds a loop around the model so it can decide on steps, use tools, observe results and continue until a task is complete or it needs human input.

The model is the decision-making component

The language model can interpret the goal, decide what information is missing, choose an allowed action and generate structured instructions for a tool. The surrounding application decides which tools actually exist and what permissions they have.

Tools let the system affect the outside world

Tools might search the web, query a database, run code, read a calendar, create a file or call another API. Tool access is what turns a purely conversational model into a system that can perform useful steps beyond producing text.

State helps the process continue

Multi-step tasks need some way to track what has happened: what the user asked for, which tool calls succeeded, what data was returned and what still needs to be done. That working state may live in the context, application memory, a database or another orchestration layer.

The loop

1. GoalThe system receives a task.
2. Plan / chooseThe model decides the next useful step.
3. ActA permitted tool is called.
4. ObserveThe result is returned to the system.
5. Continue or stopThe system repeats or delivers the result.

Why guardrails matter

The more actions a system can take, the more important permissions, confirmations, logging and limits become. A useful agent should not automatically receive unlimited access to accounts, money, private data or irreversible actions.

Agents are not one standard thing

Different products use the word “agent” differently. Some are simple tool-calling assistants; others are more autonomous workflows. When evaluating one, look past the label and ask what it can access, what actions it can take, how it checks results and when a human stays in control.