If you've used a tool like ChatGPT, you've already interacted with a sophisticated AI. But what’s happening under the hood is more than just a simple question-and-answer program. Let's break down how a single request reveals the core components of a modern AI Agent.
Imagine you ask an AI assistant: "What’s the latest news on the Voyager 1 spacecraft?" The way it answers isn't magic; it's a sequence of actions performed by an AI Agent. Here are the parts:
"...has a user interface (app or website) to receive input."
This is how the agent takes in information from its environment. Your text prompt is its "sight" and "hearing"—the raw data it needs to start working.
"...utilizes its general purpose LLM..."
This is the "brain." A Large Language Model (LLM) such as ChatGPT or Gemini analyzes your request, understands the intent ("latest news," "Voyager 1"), and creates a plan to find the answer.
"...may access current publicly available data sources..."
This is the key agentic part. The system autonomously decides to use a tool (like a web search) to act on the world and gather new data that it doesn't already have.
"...to complete its task."
The agent's actions aren't random. They are driven by the specific goal of fulfilling your request accurately and efficiently, from the initial plan to the final answer.
An AI Agent is a system designed to perceive its environment, decide on the best course of action, and act autonomously to achieve a specific goal. This combination of perception, reasoning, and action is what separates a simple program from a true agent. These agents fall into two primary categories based on the scope of their capabilities:
AI Agents represent the most advanced stage in how we use data. It's helpful to see where they fit on the spectrum of data intelligence:
Now that you understand the core concepts, see how they come to life in a business journey.
Read: Adopting AI in Business →