An AI agent is a software system that uses one or more models to pursue a goal through multiple steps. It may use tools to search, read files, call applications, or perform other permitted actions. A model answering one question is not necessarily an agent: the distinguishing feature is the system’s ability to choose and carry out steps toward a task within defined limits.
How does an AI agent work?
The agent receives a goal, instructions, and permissions. It examines available information, decides whether a tool is needed, uses that tool, checks the result, and either continues, stops, or asks a person for help. The model helps make decisions; the tools allow the system to retrieve information or act on another service.
A simple example
Suppose an agent is asked to summarize three documents. It can open the files, extract relevant passages, compare them, and draft a summary with references. If it must send that summary or change records, its permissions and any required human approval should be specified before the action.
Agent or chatbot?
A chatbot may answer a single message directly. An agent manages a sequence of decisions and tool calls to achieve a goal. However, vendors use the word “agent” differently, so assess what the system actually does rather than relying on its label.
What are the risks?
An agent can misunderstand a goal, choose the wrong tool, or misinterpret a result. Its access may affect real files and services. Limited permissions, output checks, and human review before consequential actions help contain these risks.
In short
An agent is not necessarily a new model. It is a system that combines a model, instructions, and tools to complete multi-step work under clear controls.
Sources: OpenAI: A practical guide to building agents, Google Cloud: What are AI agents?, and OpenAI: Governing agentic AI systems.