LangGraph Masterclass: From Beginner to Professional
Build production-ready AI agents in this 6-hour live masterclass led by Google AI engineers using LangGraph, LangChain, and LangSmith.
Modern AI applications require more than prompts. They require reliable, stateful workflows that can scale to production.
Instructors:
Thomas Zettl is an AI Cloud Engineer at Google with a background in data science, machine learning, and production AI systems. He specializes in building scalable AI applications and helping organizations deploy reliable, real-world AI solutions on the cloud.
Before joining Google, Thomas worked extensively in data science and analytics, developing expertise in statistical analysis, databases, and machine learning. He holds degrees from LMU Munich and Stanford University, combining a strong academic foundation with hands-on industry experience in AI engineering.
Leonid Kulign is an AI Engineer at Google with expertise in AI product development, large language models, and production AI systems. He combines a strong background in data and artificial intelligence with hands-on engineering experience to build practical, user-focused AI solutions.
Leonid has extensive experience translating complex business challenges into scalable technical solutions, working closely with engineering teams to develop and deliver AI-powered products. He is passionate about applying modern AI technologies to solve real-world problems and helping developers build reliable, production-ready AI applications.
Who It's For
This masterclass is designed for:
Software engineers building AI-powered applications
AI engineers and developers working with LLMs and agent frameworks
Backend engineers looking to build reliable, stateful AI workflows
Engineering teams moving from prototype AI agents to production deployments
Developers who want practical experience with LangGraph, LangChain, and LangSmith
This workshop is best suited for developers who are comfortable programming in Python and have a basic understanding of large language models. Prior experience with LangGraph is not required.
Before You Start
Participants should have:
Working knowledge of Python
Basic familiarity with APIs and software development workflows
A laptop with Python installed
Basic understanding of large language models and prompt engineering
Participants do not need prior experience with LangGraph, LangChain, or LangSmith.
What You'll Learn
By the end of this masterclass, participants will be able to:
Build AI agents using LangGraph's Graph API and state management model
Design reliable agent workflows using conditional routing and reusable graph patterns
Implement ReAct agents, tool-calling workflows, and reflection loops
Build advanced agents with persistence, interrupts, streaming, and human-in-the-loop capabilities
Design and coordinate multi-agent systems using supervisor and hierarchical architectures
Debug, inspect, and evaluate agent execution using LangSmith
Apply production best practices including observability, fault tolerance, and evaluation
Build, test, and deploy production-ready LangGraph applications with confidence