Developing AI Agents with LangChain

Artificial intelligence is shifting from passive chat interfaces to active agents that can reason, make decisions, and execute tasks. To build these autonomous systems, developers need to understand how to connect large language models to external data sources, APIs, and computational tools. This text-based course guides you through the foundational concepts and practical architectures required to design and deploy modern AI agents. You will transition from writing basic prompts to building self-correcting, tool-using agents that can solve multi-step problems. By understanding how to structure agentic workflows, you will be able to automate complex business processes and data analysis tasks with confidence. What you'll learn: - Understand the core architecture of agentic AI and how agents differ from standard LLM applications - Implement tool calling to connect models to external search engines, databases, and custom APIs - Master LangChain Expression Language (LCEL) to build flexible, custom runnables and chains - Configure memory states to help agents maintain context across complex, multi-turn interactions - Build autonomous agents that plan, execute, and self-correct when encountering errors - Practice modern prompt engineering patterns specifically optimized for agentic reasoning This course begins with essential terminology, exploring the transition from simple semantic search to fully autonomous reasoning loops. You will then progress through step-by-step written tutorials covering tool definition, schema validation, state management, and debugging. Every concept is reinforced with clear, readable code examples and structural explanations. This course is designed for software developers, data professionals, and tech enthusiasts who want to learn agentic AI. No prior experience with AI agents or LangChain is required, though basic familiarity with Python programming is highly recommended. Start reading today to master the next generation of autonomous AI development.