Building AI Agents from Scratch

Artificial intelligence is shifting from passive chat interfaces to active, autonomous agents that can plan, use external tools, and solve complex multi-step problems. If you want to move beyond simple prompt engineering and build systems that actually take action, understanding agentic workflows is the essential next step. This course guides you through the fundamental architectures and programming patterns required to create self-directing AI assistants. You will transition from writing basic API calls to engineering complete agentic systems. By reading through structured explanations and analyzing clear code implementations, you will understand how to give language models agency, memory, and the ability to interface with external APIs safely and predictably. What you'll learn: - Understand the core architecture of AI agents, including planning, memory, and tool execution loops - Implement the Reason and Act (ReAct) pattern to help models decompose complex tasks - Configure structured outputs and JSON schemas to ensure reliable communication between LLMs and external APIs - Build persistent memory systems so your agents can maintain context across multiple interactions - Apply basic safety guardrails and rate-limiting patterns to prevent runaway agent execution loops - Explore multi-agent collaboration concepts where specialized agents work together to solve a goal This text-based course starts with the absolute fundamentals, establishing clear definitions of what makes an agent before moving into practical code-based architectures. You will explore step-by-step how to construct planning loops, integrate search and database tools, and handle errors when an agent goes off track. This course is designed for software developers, tech-savvy product managers, and curious builders who have basic Python knowledge and want to learn how to build autonomous systems. No prior experience with AI engineering or machine learning is required. Start reading today to master the architectural patterns behind modern autonomous AI agents.