An AI agent is a fundamental concept in artificial intelligence. It's a software entity that can perceive its environment through sensors and act upon it through actuators to achieve specific goals. The core components include sensors for perception, actuators for action, decision logic for processing, and memory for storing state. The agent follows a continuous perception-action cycle: perceive the environment, process information, make decisions, execute actions, and update internal state.
AI agents can be classified into four main architectural types. Simple reflex agents use condition-action rules with no memory, responding directly to current percepts. Model-based agents maintain an internal world model to track environment state and handle partial observability. Goal-based agents have explicit goals and use search and planning algorithms to achieve them. Utility-based agents use utility functions to optimize performance and handle conflicting goals by maximizing expected utility.
Core programming components for AI agents include three essential elements. First, state management involves creating classes to track agent position, energy, memory, and other internal variables. Second, action selection algorithms use conditional logic to choose appropriate actions based on current state, such as recharging when energy is low or moving toward goals. Third, environment interface methods handle communication between agent and environment through functions like get percepts and execute action, enabling the agent to perceive and act in its world.