Introduction to LangGraph: Build a Deep Research Agent

As artificial intelligence evolves from answering simple queries to executing complex workflows, agentic AI is becoming an essential skill for modern developers. Understanding how to build autonomous systems that plan and execute tasks opens up entirely new possibilities for automation and data synthesis. In this text-based course, you will learn the foundational concepts of multi-agent systems and build your own deep research agent. Starting with basic terminology and core AI definitions, you will explore the planner-executor architecture and learn how to use LangGraph to orchestrate intelligent workflows that can autonomously gather, analyze, and summarize information. What you'll learn: - Understand the core terminology and concepts behind agentic AI and multi-agent systems. - Design a planner-executor architecture to break down complex research goals into manageable steps. - Apply LangGraph to orchestrate stateful, multi-actor applications and autonomous agents. - Integrate basic retrieval-augmented generation (RAG) patterns and vector databases for efficient data handling. - Practice prompt engineering techniques to guide agent reasoning and improve output reliability. - Build a complete, text-based deep research workflow that autonomously synthesizes information. The course flow begins with essential definitions and foundational AI concepts before moving into practical, step-by-step written exercises. You will progressively construct your agent's logic, learning how to manage state, structure workflows, and handle errors along the way. This foundational course is designed for beginners and developers new to AI agents; no prior experience with LangGraph or multi-agent orchestration is required. Start reading today to build your first autonomous research agent and expand your AI development skills.