AI Agents Fundamentals: Build with LangChain and LangGraph

Artificial intelligence is shifting from passive chat interfaces to active, autonomous agents that can plan, use tools, and solve complex problems. Understanding how to design and build these agentic workflows is one of the most valuable skills in modern software development. In this text-based course, you will transition from understanding basic prompts to designing, building, and deploying autonomous AI agents. You will learn how to orchestrate Large Language Models (LLMs) to make decisions, execute tasks, and interact with external systems safely and effectively. What you'll learn: - Understand the core architecture of AI agents, including planning, memory, and tool execution. - Configure Large Language Models to drive decision-making and orchestrate multi-step workflows. - Apply Retrieval-Augmented Generation (RAG) and vector databases to give your agents access to external knowledge. - Build complex, stateful multi-agent systems using LangGraph. - Implement the Model Context Protocol (MCP) to securely connect your agents to local and remote data sources. - Design robust agentic memory systems so your assistants retain context across sessions. You will start with the fundamental terminology, basic concepts, and architectural patterns of agentic AI. From there, you will progress through written explanations and clean code snippets, learning how to connect LLMs to tools, databases, and other agents step by step. This course is designed for beginners and software developers who want a solid foundation in building agentic systems, with no prior AI experience required. Start reading today to build the next generation of intelligent, autonomous software.