An AI agent is a system that does more than answer one prompt at a time. It can interpret a goal, decide on next steps, use tools or APIs, observe results, and keep going until it completes a task or reaches a stopping point. In practice, an agent is usually built from several pieces: a model, instructions, memory or state, tool access, and rules about what actions are allowed.
How AI Agents Work
A common agent loop is simple: understand the goal, plan, act, observe, and revise. For example, an agent helping with travel might search flight data, compare options, ask a clarifying question, and format an itinerary. The model supplies reasoning and language ability, while tools provide access to live information and actions.
This is why agents are closely tied to tool use, function calling, system prompts, and guardrails. The model may propose actions, but the surrounding application decides what actions are actually permitted.
What Agents Are Good For
Agents are useful when a task involves multiple steps, external systems, or changing state. Good examples include customer support workflows, scheduling, research assistance, code operations, document processing, and internal business automation. They can reduce repetitive work and make software feel more adaptive.
But agents also introduce risk. If the planning is weak, the available tools are poorly defined, or the system lacks strong constraints, an agent can waste time, call the wrong service, or confidently do the wrong thing. That is why grounded inputs, permission boundaries, and human review paths matter so much in agent design.
Related concepts: Tool Use, Function Calling, System Prompt, Guardrails, and Grounding.