Build Powerful Multi-Agent AI Workflows with LangGraph - (Theory)

Description

In this lecture, we dive into Multi-Agent Systems in LangGraph using Large Language Models (LLMs). 🚀 We explore how traditional static workflows evolved into intelligent reflection workflows, and now into powerful multi-agent architectures where different AI agents handle specialized tasks like research, writing, critique, and refinement. 🔹 Learn the difference between static workflows, reflection agents, and multi-agent systems 🔹 Understand why specialization and subject matter expertise matter in AI workflows 🔹 See how different LLMs like Grok, Gemini, and Llama can work together in LangGraph 🔹 Discover how to initialize and invoke multiple LLMs in different workflow nodes using LangChain and LangGraph This session builds the foundation for creating scalable, flexible, and production-ready AI agent systems. 📌 Next Lecture: Full practical implementation of a Multi-Agent System in Python using LangGraph and Jupyter Notebook.