Agentic AI Session2: Agentic AI Architecture using LangGraph Multi-Agent Systems
Summary : In Session 2 of our Agentic AI curriculum, we move beyond simple reasoning loops and explore how LangGraph enables structured, stateful, and production-ready multi-agent systems. Want to learn by running a Multi-Agent system? Run the Agentic AI Session 2 notebook in Google Colab. In Session 1 , we learned how to make an AI think using the ReAct loop. That was a major milestone. But thinking alone is not enough. When you step into the world of Multi-Agent Systems, structure becomes important. Without architecture, agents quickly turn into tangled scripts that collapse under production pressure. In Session 2, we move from simple Python loops to a structured architectural system using LangGraph. 1. Why LangGraph? Moving Beyond the Loop In the first session , a basic Python for loop was enough to drive reasoning. It worked well for small experiments. But complexity can grow fast. The moment multiple agents must collaborate, retry, or self-correct, simp...