Building AI Agents Two Ways: LangGraph and Standard SDKs

As artificial intelligence shifts from simple chatbots to autonomous systems, developers are faced with choosing the right framework for their projects. Understanding the differences between standard agent software development kits (SDKs) and state-based orchestration tools is crucial for building reliable AI applications. This course demystifies agentic AI by taking a hands-on, comparative approach. You will learn foundational AI terminology, understand how large language models (LLMs) make decisions, and discover how to structure agent workflows. By building the exact same agent using two different approaches—a standard AI Agent SDK and LangGraph—you will gain a practical understanding of when to use straightforward function calling versus complex, graph-based state management. What you'll learn: • Understand fundamental agentic AI concepts, including reasoning loops, tool use, and basic prompt engineering. • Build a foundational AI agent using a standard SDK approach. • Construct the exact same agent using LangGraph to manage complex state transitions. • Compare the strengths, trade-offs, and scaling capabilities of both architectural patterns. • Implement basic Retrieval-Augmented Generation (RAG) patterns to give your agents access to external knowledge. • Configure modern execution environments and manage application secrets securely. The course begins with a thorough introduction to AI agent terminology and core concepts before moving into written code walkthroughs. You will follow step-by-step text instructions to implement, test, and evaluate both versions of your agent application. Designed for beginner developers and technical enthusiasts, this course requires no prior experience with AI frameworks—just a basic understanding of programming logic. Start reading today to master the tools powering the next generation of intelligent applications.