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Welcome to LangGraph Components Part 8 – Agents in LangGraph! 🚀 In this video, you’ll learn how to build intelligent AI agents that can think, reason, and use tools dynamically using LangGraph and LangChain. Agents are at the heart of Agentic AI — systems that can plan, call tools, and return contextual answers by following structured reasoning steps. 💡 What You’ll Learn ✅ What are Agents in LangGraph ✅ How Agents differ from Chains ✅ Connecting an LLM with Tools using LangGraph ✅ Building Autonomous AI Workflows ✅ Agent Execution Flow and Reasoning Path ⚙️ Key Concepts Covered LLM as the Agent’s brain 🧠 Tool calling inside LangGraph Decision nodes and conditional routing Looping through actions until a final answer is reached Combining LangChain Tools + LangGraph Graphs 🧰 Tech Stack LangGraph LangChain Core Groq / OpenAI LLMs RunnableLambda / RunnableParallel Python 3.10+ 📌 GitHub Repository (Code + Notes): 👉 https://github.com/dearnidhi/Agentic-AI-HandsOn-Bootcamp 📩 Connect with Me: ✉️ Email: nidhiyachouhan12@gmail.com 📸 Instagram: @codenidhi | @dear_nidhi 💼 LinkedIn: https://www.linkedin.com/in/nidhi-chouhan-544650b4/ ✨ Don’t forget to LIKE 👍, SHARE 📢 & SUBSCRIBE 🔔 for more LangGraph & Agentic AI tutorials! LangGraph Agents, LangGraph AI Agent, LangGraph Components, LangGraph Agent Tutorial, LangGraph Autonomous AI, LangGraph Decision Nodes, LangGraph Tool Integration, LangGraph LangChain Agent, LangGraph Agent Example, Agentic AI Bootcamp, LangGraph Workflow, LangGraph for AI Agents, LangGraph Code Example, CodeNidhi, Nidhi Chouhan LangGraph #LangGraph #LangChain #AI #AgenticAI #LangGraphAgents #LangGraphTutorial #LangGraphComponents #LangGraphWorkflow #AIAgents #MachineLearning #ArtificialIntelligence #CodeNidhi #NidhiChouhan #GenAI #Python