Foundations of AI Agents: Architecture and Implementation

Autonomous AI systems are changing how software solves complex multi-step problems, moving beyond simple chat interfaces into goal-driven task execution. This text-based course guides you through the fundamental building blocks of AI agents, equipping you with the practical understanding needed to construct reliable agentic workflows. You will explore how modern agents reason, maintain contextual memory, leverage external tools and APIs, and collaborate across specialized roles. Through clear written explanations and modular code walkthroughs, you will develop a structured understanding of agent system design from first principles. What you'll learn: - Understand core agent concepts, terminology, reasoning loops, and decision-making frameworks - Configure prompt-based planning strategies, including task decomposition and reflection - Implement tool calling and function integration to allow agents to interact with external environments - Apply short-term and long-term memory architectures using vector storage and context management - Design multi-agent collaboration patterns to tackle complex, domain-specific tasks - Evaluate agent reliability, error recovery strategies, and security guardrails The course begins with foundational definitions and architectural patterns before guiding you step-by-step through concrete text-based coding examples and practical implementation challenges. Designed for curious developers and tech enthusiasts, no prior AI agent experience is required—basic familiarity with Python is sufficient. Start reading today to build your own functional AI agent workflows.