What is an AI agent
An agent is a model that has been given a GOAL and TOOLS to achieve it. A regular assistant simply responds with text. The agent can decide for himself what steps to take, call a tool (for example, search the database, send a request, calculate), get the result and move further towards the goal. That is, the agent not only speaks, but also ACTS through the tools available to him.
How is an agent different from a chatbot?
The chatbot answers one question at a time within a conversation. The agent works on a task in several steps: plans, executes, checks the result, and repeats if necessary. The key difference is autonomy and access to tools. Where the bot needs a human to lead it by the hand, the agent is able to go through the chain of steps himself.
What agents can do and where they are used
Agents are used where the task is divided into steps and requires actions, not just text: collecting and structuring information from different sources, processing the flow of requests with access to systems, performing a multi-step routine. In conjunction with function calling, the agent works with real, fresh data, rather than inventing it.
Boundaries and risks
Agents are powerful, but complexity takes its toll. Cost and delay increase with the number of steps, errors accumulate (a miss at an early step breaks the entire chain), and irreversible actions without verification are dangerous. Therefore, the agent is given a minimum of rights, the irreversible is passed through human confirmation, and his work is logged. Autonomy requires control.
When an agent is not needed
The most common mistake is to build an agent where one good prompt or a simple chain is enough. Each extra agent adds cost, delay, and points of failure. Start with a simple solution and add complexity only when the role or concurrency really requires it. The default is one good agent, not a swarm.