AI Agents in Artificial Intelligence: Core Concepts and Architectures

Understanding how intelligent systems interact with their environments is the cornerstone of artificial intelligence. This text-based course guides you through the fundamental theory of AI agents, from basic definitions to complex decision-making architectures. You will transition from a beginner to confidently analyzing and classifying agent-based systems. Through clear written explanations, you will learn to evaluate environmental properties, map agent structures, and understand how modern autonomous systems operate in academic and practical scenarios. What you'll learn: - Define key terminology including agents, sensors, actuators, and rational behavior. - Classify different types of environments based on observability, determinism, and dynamism. - Analyze core agent architectures including reflex, model-based, goal-based, and utility-based models. - Understand the structure of learning agents and how they optimize performance over time. - Explore modern agent paradigms, including basic multi-agent systems and autonomous decision-making flows. The course begins with essential definitions of rationality and agent-environment interaction before moving systematically through classic agent taxonomies. You will wrap up by exploring how these academic concepts translate to modern AI developments through structured written exercises. This course is designed for computer science students, exam candidates, and beginners looking for a solid theoretical introduction to AI agents with no prior programming prerequisites. Start reading today to build a strong theoretical foundation in artificial intelligence.