At its core, an AI agent is simply something that can observe its surroundings and take actions to achieve a goal. It’s a useful way to think about how AI works in the real world. A self-driving car is an agent. Its goal is to get from point A to point B safely. A smart thermostat is also an agent, working to keep a room at a comfortable temperature.

Think of it as a continuous loop: the agent perceives the state of its world, thinks about what to do next based on its goals, and then acts. This cycle of perceiving, thinking, and acting is fundamental to all AI agents, from the simplest to the most complex.

For an agent to interact with its world, it needs two key types of hardware or software components: sensors and actuators.

Sensors are how an agent gathers information about its environment. They are the agent's inputs, its senses. A camera is a sensor for a self-driving car, allowing it to "see" other cars and pedestrians. A microphone is the primary sensor for a voice assistant like Siri or Alexa. For a spam filter, the text of an incoming email is its sensory input.

Actuators are what an agent uses to perform actions and affect its environment. They are the agent's outputs, its hands and feet. For the self-driving car, actuators include the steering wheel, accelerator, and brakes. A voice assistant's actuator is its speaker, which produces a spoken response. A spam filter acts by moving an email to the spam folder.

This diagram shows the basic relationship. The agent takes in percepts—its sensory inputs—from the environment. It then uses its actuators to perform an action, which changes the environment in some way. This change is then observed in the next cycle, creating a continuous feedback loop.

So why is this concept of an agent so important? Because it gives us a framework for building intelligent systems. Instead of just writing a program that crunches numbers, we can design an entity that has goals and actively works to achieve them within a specific context or world.

This is where decision-making comes in. The "brain" of the agent is the part that connects perception to action. It contains the logic or model that maps a given set of percepts to a specific action. This mapping is called the agent function.

In this notation, the function $f$ takes a sequence of percepts (represented by $\mathcal{P}^*$) and maps it to an action (represented by $\mathcal{A}$). A rational agent is one that selects an action expected to maximize its performance measure, given the evidence provided by its percept sequence and any built-in knowledge the agent has.

This decision-making process is the heart of artificial intelligence. The goal is to design an agent function that allows the agent to act intelligently and autonomously, learning and adapting to achieve its objectives as effectively as possible.