Foundations of AI Agents with LangChain

Large language models are highly capable, but they become truly transformative when they can reason, make decisions, and interact with the physical and digital world. Building AI agents allows you to bridge the gap between static text generation and dynamic, goal-oriented action. This written course guides you through the process of developing autonomous systems that can think, plan, and execute tasks. By following this structured text-only curriculum, you will transition from writing simple prompts to developing functional agentic workflows. You will learn how to connect language models to external tools, manage state, and build robust systems capable of tackling complex, multi-step problems. What you'll learn: - Understand the core architecture of AI agents, decision-making loops, and reasoning frameworks. - Implement tool calling to connect language models with external APIs, calculators, and custom code. - Build multi-step decision chains using LangChain to solve complex, multi-part problems. - Integrate retrieval-augmented generation (RAG) patterns to ground agent decisions in private data sources. - Manage agent memory and state to maintain context across multi-turn interactions. - Apply prompt engineering strategies specifically optimized for agent reasoning and planning. Starting with fundamental definitions and core agent concepts, the course guides you through practical logic design and integration patterns. Each concept is explained through clear, written breakdowns and step-by-step code snippets that you can read, analyze, and apply at your own pace. This course is designed for software developers, tech enthusiasts, and curious beginners who want to build functional AI systems. No prior experience with AI agents is required, though a basic understanding of Python is recommended. Start reading today to build your first autonomous AI agent.