AI agent is a system that can perceive context, make decisions, and execute actions to reach a goal — autonomously or with light human oversight. It doesn’t only answer: it chains steps, uses tools, and adjusts as results come in.
It builds on artificial intelligence (often a language model underneath), plus a layer of reasoning and action: search the web, query a database, call an API, send an email, or update a CRM. A classic chatbot follows a script; fixed automation fires “if X, do Y.” An agent is more flexible: you give it an objective and it figures out how to get there. That usually starts with a clear prompt or goal statement.
A real-life example of an AI agent
You ask: “Review today’s contact forms, flag the ones that qualify, and schedule a meeting.” A chatbot would explain what a lead is. An agent can read the messages, filter them, write to the CRM, and propose times — with human in the loop review when it matters.
Customer support, sales, and operations already run real cases. Agents get more useful when they can reach live tools through standards like MCP. For the full picture, see AI agents: what they are.