What Is an AI Agent?

In plain language

A conventional chatbot interaction often looks like this:

You ask one question; it gives one answer.

An agent is designed to work toward a goal. It may decide what to do next, break the work into steps, use available tools and inspect the result before continuing.

OpenAI describes agents as systems that can plan, make decisions and act toward a goal within human-defined guardrails. (OpenAI)

An everyday example

An analogy

An adviser mainly tells you what to do. An assistant may carry out the intermediate steps. This is a useful mental model, not a strict boundary shared by every AI product.

Where you will encounter it

The term appears in coding tools such as Codex and Claude Code, automation systems and AI products that can call tools or complete multi-step work. Product capabilities vary, so look past the label and check what actions a particular agent can actually perform.

Should you care

• General AI users: worth understanding.
• Codex / Claude Code users: an essential concept.
• Automation users: important for judging permissions and oversight.
• Teams: important when deciding which actions require review or approval.

How it differs

Agent vs chatbot: A chatbot is commonly optimized for conversation and answers. An agent is designed to pursue a goal through multiple steps and actions.

Agent vs Skill: The agent does the work; a Skill can give it a reusable method or supporting resources for that work.

Agent vs MCP: The agent plans and acts. MCP is one way an AI application can connect to outside capabilities.

Agent vs API: An API is an interface between software systems. An agent may use a tool that calls an API, but an API is not itself an agent.

What to read next

The Skill vs MCP comparison shows how an agent can combine a working method with an external tool connection.