An AI agent is a system where a language model decides, step by step, which tool to call next to reach a goal. Unlike a chat bot that produces one response, an agent runs in a loop: it observes the current state, picks an action (call an API, search the web, write a file), executes it, and feeds the result back into the next decision.
The big shift from chat to agent is state management and tool use. The model needs a structured way to read tools, call them with valid arguments, and recover when something fails. In production, you almost always want a hard cap on the number of loop iterations and explicit success criteria — otherwise agents either give up too early or burn through your token budget chasing a goal they can't reach.
Real-world examples: a coding agent that opens a repo, runs tests, and proposes a PR. A research agent that pulls 10 sources and writes a brief. A support agent that reads a ticket, queries the knowledge base, and drafts a reply for a human to approve.
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