Context Engineering for AI Agents with LangGraph

As AI agents become more autonomous, managing what they remember and how they retrieve information is the key to building reliable applications. Poor context management leads to hallucination, lost state, and inefficient token usage. This text-only course guides you from the fundamental concepts of context engineering to building structured, stateful AI agent workflows. You will understand how to structure prompts, manage conversational memory, and feed the right data to your agents at the right time. What you'll learn: 1. Understand the core terminology of context engineering, agentic state, and cognitive architectures. 2. Design stateful agent workflows using LangGraph to maintain context across multi-step tasks. 3. Implement Retrieval-Augmented Generation (RAG) patterns to dynamically inject relevant external data. 4. Manage token limits and memory retention using windowing and summarization strategies. 5. Structure system prompts and agent instructions to ensure predictable tool usage and decision-making. 6. Debug context-related issues and optimize agent responses through structured written exercises. You will start with the essential definitions of context windows and state machines, then move step-by-step through designing complex agent memory flows. Through clear written explanations and practical code snippets, you will learn to construct robust context pipelines for real-world applications. This course is designed for beginners and AI enthusiasts who want to build smarter agents, with no advanced prerequisites required. Start reading today to master the art of context engineering and build more reliable AI agents.