What Is an AI Agent?

An AI agent is an AI system that does not just respond to one question, it takes a series of actions to complete a goal.

The difference is important. When you ask ChatGPT a question, it answers and stops. An AI agent given the same goal might search the web for information, open a document, write a draft, check it against a set of criteria, revise it, and send it, without you intervening at each step.

Agents have the ability to use tools: search engines, databases, APIs, email systems, calendars. They can make decisions about which tool to use next based on what they find. They work in loops, act, observe the result, decide what to do next, until the goal is reached or they get stuck.

A real-world example

A sales research agent given a list of target companies might search each company's website, find the relevant decision-maker on LinkedIn, check recent news about the company, and produce a one-page briefing for the sales team. Tasks that might take a human analyst two hours per company, completed in minutes.

Where we are right now

AI agents are genuinely useful for well-defined, repeatable research and data tasks. They are less reliable for tasks that require subtle judgment, access to systems without APIs, or handling unexpected situations gracefully.

The technology is moving fast. Agents that would have been unreliable 12 months ago are now production-ready for specific use cases. The businesses that understand how to scope and deploy agents well are getting significant productivity advantages.

The honest caveat

Agents need supervision. They can make mistakes and, unlike a single AI response, those mistakes can compound across multiple steps. The practical approach right now is human-in-the-loop: the agent does the work, a human reviews before anything consequential happens.