An AI agent, from an academic perspective, is a computational entity that perceives its environment through sensors and acts upon that environment through actuators, guided by some form of intelligent decision-making process.
Core Academic Definition:
An AI agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators to achieve certain goals.
— Russell & Norvig, “Artificial Intelligence: A Modern Approach”
Key Components of an AI Agent:
- Perception
Gathers information about the environment (e.g., via sensors, data inputs, APIs). - Decision-Making (Agent Function)
Applies logic, rules, machine learning, planning, or heuristics to determine the next action. - Action (Actuation)
Executes an action that changes the state of the environment or the agent itself. - Goal or Utility Function
The agent is typically goal-driven (e.g., maximizing reward, minimizing error, solving a task).
Types of AI Agents (Theoretical Classification):
- Simple Reflex Agents
Act solely based on current perception — no memory or learning (e.g., rule-based systems). - Model-Based Reflex Agents
Use internal models to handle partial observability of the environment. - Goal-Based Agents
Take actions based on achieving specific goals, using search and planning. - Utility-Based Agents
Go beyond goals, aiming to maximize a utility function (how “good” a state is). - Learning Agents
Improve their performance over time through experience (e.g., reinforcement learning agents).