What is an AI agent?
An AI agent is an AI system that, once you tell it the end goal, breaks the work into pieces, picks tools such as search, code execution, or API calls, and carries out many steps in sequence. Unlike an ordinary chatbot that answers once per question, an agent checks its results and decides the next action on its own.
Put simply, it is the shift from an AI that answers to an AI that does the work for you.
How is an AI agent different from a chatbot?
A chatbot ends once it returns an answer. An agent uses that answer to take real actions, such as editing files, booking, ordering, or running code. That is why you also hear the term "agentic AI."
This makes agents more useful, but a wrong decision can lead to real consequences, so permissions and verification safeguards matter.
What kinds of AI agents are there?
By purpose, they include coding agents that work with code, research agents that gather and summarize material, workflow agents that automate repetitive tasks, and multi-agent systems where several agents collaborate.
The overall flow is similar for every type (understand the goal, plan, use tools, check results). They differ in the tools they use and the scope of their permissions.
How does an AI agent work?
Internally, a language model (LLM) handles the reasoning, and a "harness" connects tool calls, memory, and a repeated execution loop to turn that reasoning into real work. Coordinating many tools and steps is called orchestration.
The model is the brain and the harness is the hands and feet. The agent can only finish a goal when the two work together.
How do you choose and use an AI agent?
Rather than looking for a "number one" pick, it is safer to choose by the type of work you will hand off (coding, research, automation), the tools it needs to connect to, the permissions you will allow, and how you will verify results.
In practice, many teams start with a small scope, review results by hand, and widen the agent's autonomy gradually as trust builds.