As AI agents become increasingly autonomous, human oversight remains critical for ensuring accuracy in complex workflows. This article demonstrates how to build human-in-the-loop (HITL) agentic workflows using LangGraph, a low-level orchestration framework that gives developers fine-grained control over AI agent execution. The tutorial focuses on creating a social media content generation pipeline that pauses for human review before publishing.

Unlike high-level abstractions like LangChain's HumanInTheLoopMiddleware, LangGraph exposes the underlying mechanics of agent workflows. The framework uses interrupts—dynamic breakpoints that can be placed anywhere in the code—to pause execution and collect human input. These interrupts work with Command objects to update state, specify next steps, or resume execution. The article builds a Bluesky posting workflow where users review generated content before it goes live, demonstrating practical HITL implementation.

Key concepts include checkpoints for persisting state across interruptions, thread IDs for maintaining session continuity, and the distinction between agentic workflows and autonomous AI agents. The tutorial provides complete code examples using SQLite for state persistence and includes a GitHub repository with the full implementation. By combining LangGraph's flexibility with human judgment at critical decision points, developers can create more reliable agentic systems for tasks where automated verification falls short.