Lecture 24: Introduction to Agentic AI & LangGraph — State, Nodes, Edges, Tools & Workflows

Description

Welcome to Lecture 24 of the Gen AI Series, where we dive deep into Agentic AI and begin building intelligent, autonomous applications using LangGraph — the most powerful (and flexible) framework for creating LLM-powered agents. In this session, we cover: ✅ What Agentic AI really means — giving LLMs "hands and legs" ✅ Core components of agentic systems: Memory, Tools, State, MCP, Workflow & Autonomous Decision-Making ✅ LangGraph fundamentals: State, Nodes, Edges, Checkpointing ✅ How to define state schemas using TypedDict and Pydantic ✅ Building simple workflows: sequential execution, function-as-nodes, edge connections ✅ Real-world analogies & practical examples: product naming → ad copy generation ✅ Inbuilt tools (Bing, Gmail, GitHub, Wikipedia, etc.) + how to create custom tools ✅ How message history & context persistence work in chatbots using state + checkpointing ✅ Preview of upcoming topics: Conditional edges, parallel workflows, RAG-agents, fine-tuning & Cloud Code (Vibe Coding) 🔔 Next class: We’ll build a working chatbot with memory & tools — and start coding actual LangGraph applications! 📌 Resources mentioned: • LangGraph Docs: https://langchain-ai.github.io/langgraph/ • LangChain, AutoGen, CrewAI, Google ADK — other agent frameworks • Tools: DuckDuckGo, Jira, Yahoo Finance, Code Interpreter, etc. 🛠️ Perfect for learners aiming for Gen AI roles — this lecture bridges theory & implementation, preparing you for real-world agent development & technical interviews. 👍 Like, Subscribe & hit the bell for more hands-on Gen AI content! 💬 Questions? Drop them below — we’ll answer in the next session. #GenAI #LangGraph #AgenticAI #LLM #LangChain #AIEngineering #DataScience #MachineLearning