AI Agent Architecture: Capabilities, Types, and Decision Models

Artificial intelligence is shifting from passive chat interfaces to active, autonomous agents that can plan, reason, and execute tasks. To build or work with these technologies, you must first understand the structural patterns that make them function. This text-based course guides you through the fundamental building blocks of modern AI agents, helping you grasp how autonomous systems operate in the real world. You will transition from a basic understanding of artificial intelligence to a clear architectural view of how autonomous systems perceive their environments, make decisions, and take action. What you'll learn: - Understand core agent capabilities including perception, reasoning, action execution, and memory. - Compare different agent classifications from simple reflex models to goal-based and utility-based systems. - Explore modern memory architectures, including short-term context and long-term vector database storage. - Analyze decision-making frameworks and planning techniques like chain-of-thought and tree-of-thoughts. - Examine how agents utilize tools, APIs, and external environment integrations to perform real-world tasks. - Learn the foundations of multi-agent collaboration and orchestration patterns. The course begins with essential definitions and foundational terminology before guiding you through cognitive architectures, memory systems, and modern integration patterns. You will read detailed explanations, analyze architectural breakdowns, and explore conceptual scenarios designed to build your intuitive understanding. This course is designed for beginners, aspiring AI product managers, developers, and tech enthusiasts who want a solid conceptual foundation in agentic workflows. No programming background or technical prerequisites are required to get started. Begin your journey into the design and structure of autonomous AI systems today.