Foundations of AI Agent Development

Autonomous AI agents are transforming how we build software, shifting from simple chatbots to systems that can plan, use tools, and solve complex tasks. Understanding how to design and orchestrate these agentic workflows is becoming an essential skill for modern developers and technical builders. This text-based course guides you through the foundational concepts of AI agent architecture, taking you from basic LLM interactions to conceptualizing systems that can autonomously reason and execute actions. What you'll learn: Understand core AI agent architectures, including planning, memory, and execution loops; Configure tool-use and function-calling capabilities to let agents interact with external APIs; Apply modern prompt engineering techniques to guide agent reasoning and decision-making; Implement memory systems to help agents retain context across multi-step interactions; Explore retrieval-augmented generation (RAG) patterns to ground agent responses in external data; Design basic collaborative multi-agent workflows for complex problem-solving. You will begin with key terminology and the anatomy of an agent before progressing to practical text-based explanations of planning, tool integration, and memory management. Each module combines clear conceptual breakdowns with written code snippets to solidify your understanding. This course is designed for software developers, technical product builders, and curious engineers who are new to AI agent development. No prior experience with AI orchestration frameworks is required, though a basic familiarity with programming concepts is helpful. Start reading today to build your first autonomous AI agent.