AI is rapidly moving beyond simple chatbots. The next generation of applications is powered by autonomous agents that reason, collaborate, and interact with real-world systems.
In this hands-on workshop, you’ll learn how to design and orchestrate intelligent AI systems using LangGraph and modern agentic patterns. Through practical exercises and real-world examples, you’ll discover how to transform large language models into powerful, composable systems that automate complex workflows.
💡 What you’ll learn
How LangGraph workflows work — nodes, edges, routing, and execution
The fundamentals of AI agent architecture including prompts, tools, and context
How to design multi-agent systems that collaborate to solve complex problems
How to extend agents using the Model Context Protocol (MCP) to connect with data and services
🛠️ Hands-on sessions included
You’ll build:
A LangGraph workflow from scratch
A tool-enabled AI agent
A multi-agent system for a real-world use case
An MCP-powered agent integration
👩💻 Who should attend
Developers building AI-powered applications
ML/AI engineers exploring agentic architectures
Platform and backend engineers integrating LLMs with real-world systems
Tech leads and architects designing automation platforms and multi-agent systems
Innovation and R&D teams experimenting with AI agents and orchestration frameworks
📋 Prerequisites
Laptop with Python & Git installed
GitHub account
API keys for models (e.g., Gemini or GPT)
Familiarity with an IDE such as VS Code or PyCharm
🎯 Key takeaways By the end of the workshop you will:
Understand LangGraph’s execution model
Build reasoning-driven AI agents
Design scalable multi-agent architectures
Extend agents using MCP-based integrations
⏱ Workshop Agenda
Foundations of LangGraph
Introduction to AI Agents
Multi-Agent Systems
Model Context Protocol (MCP)
Q&A and open discussion
🎥 This workshop expands on the ideas presented in this talk:
https://youtu.be/zWWWi5tfkn4?si=SXbpzCFN62GDcAxa
📌 **Seats are limited — register early to secure your spot!
