What is an AI agent?

An AI agent is an AI that can take actions, not just answer questions. Instead of responding to a single prompt and stopping, an agent can plan a series of steps, use tools, and work toward a goal autonomously.

A basic AI chatbot waits for you to ask something, responds, and stops. An AI agent goes further. You give it a goal (“research this topic and write a report”, “find the bug in this codebase”, “book me a flight”) and it figures out how to get there on its own. It might search the web, run code, read files, call APIs, and loop through multiple steps before it’s done.

What makes agents different is the ability to use tools. An agent connected to a web search tool can look things up. One connected to a code interpreter can write and run code. One with access to your calendar can schedule meetings. The AI model at the center is the brain, but tools are what give it hands.

Agents also tend to be iterative. They check their own output, decide if it’s good enough, and either continue or revise. This is sometimes called a reasoning loop or an “agentic loop.” It’s what lets an agent recover from mistakes without you having to intervene.

Claude Code is a good example of an AI agent. You give it a task, and it reads files, writes code, runs tests, and makes decisions until the task is done. MCP (Model Context Protocol) is a standard that makes it easier to connect agents to external tools and data sources.

The field is moving fast. Agents are getting more capable, more reliable, and more integrated into real workflows. A lot of what people call “vibe coding” today is really just working with agents.

My first real experience with an AI agent was Claude Code. Before that I was using Copilot for simple autocomplete and function writing. Claude Code was different. It could explore an entire codebase, plan an implementation, write code, run tests, and fix issues without me directing each step. That felt like a genuine leap. I’ve found agents most valuable for tedious, time-consuming work where the path is clear but the execution is slow. Refactoring, boilerplate, linting fixes across a codebase. Things that would take me hours, an agent handles in minutes.