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Currently Playing: Human in the loop (HITL) using LangGraph | CampusX

Code - https://github.com/campusx-official/langgraph-tutorials In this video, we explore Human-in-the-Loop (HITL)—one of the most important concepts for building safe, reliable, and production-ready agentic AI systems using LangGraph. We begin with a clear, intuitive explanation of what HITL is and why fully autonomous AI systems are not enough in real-world applications. You’ll understand where AI agents struggle today—ambiguity, hallucinations, financial actions, and ethical judgment—and why human intervention is critical at key decision points. The video covers: Why autonomy alone is risky in agentic AI systems The two core reasons HITL exists: system reliability and accountability How HITL improves accuracy, safety, and user trust Common HITL patterns such as: Action approval Output review and editing Ambiguity clarification Escalation to human agents From there, we shift to LangGraph’s implementation of HITL, explaining the concepts of interrupt, pause-and-resume execution, checkpoints, and commands in a simple, intuitive way. 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at support@campusx.in ⌚Chapters⌚ 00:00:00 - Introduction: What is Human-in-the-Loop (HITL)? 00:01:14 - Why Agentic AI Needs Human Oversight 00:03:44 - HITL Explained with Real-World Examples 00:04:22 - Core Reasons for HITL: Accuracy & Accountability 00:08:46 - Benefits of HITL in Agentic Systems 00:11:36 - Common HITL Patterns in AI Systems 00:14:21 - HITL from a LangGraph Perspective 00:16:30 - Conceptual Workflow: HITL with Interrupts 00:22:49 - Basic HITL Code Example in LangGraph 00:31:22 - Advanced Example: HITL in a Tool-Using Chatbot 00:39:42 - Key Takeaways and Final Thoughts