AI Agent glossary definition

An AI agent is a software system that can perceive its environment, decide what to do next, and take actions to reach a goal. You can think of it as an operator that works step by step, using data from tools, apps, and user inputs instead of waiting for you to write every command.

In practice, an AI agent pulls information from sources like a database, a web service, or a chat interface. Then it chooses an action, for example, calling an API, drafting a message, updating a record, or scheduling a task. After it acts, the agent checks results and adjusts its next move. That loop between observe, decide, and act makes the system feel less like a static chatbot and more like an active problem-solver.

What makes an AI agent different

Many AI tools produce text or predictions. An agent goes further. It plans, uses tools, and keeps working until it hits a target. If you ask a travel assistant to find flights, the agent can search, filter, compare prices, and produce options with the details you asked for. If you change the budget mid-way, it adapts because it controls the workflow.

Common examples

You see AI agents in customer support, where they read a request, identify the issue, and submit a resolution to a ticketing system. You also see them in analytics workflows, where they query datasets and generate next steps based on the numbers. Developers use agents to automate testing, monitor systems, and respond to incidents.

When teams define an AI agent, they usually include three pieces: a goal, a decision process, and tool access. This definition helps you pick the right design, build the right guardrails, and test behavior under real conditions. If you want to evaluate one, ask how it gathers inputs, how it selects actions, and how it handles failures.

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