In this guide, we’ll walk through building a fully end-to-end multi-agent workflow using LangGraph, featuring a supervisor agent and human-in-the-loop (HITL) review for enhanced control and quality assurance. This architecture is ideal for real-world GenAI applications where accuracy, consistency, and oversight are critical. The objective is to create a system that processes raw user-submitted product reviews, evaluates their quality, fills in missing product information, and rewrites the content into polished, publishable form. The workflow incorporates specialized agents, a central supervisor to manage decision paths, and a human review checkpoint before final publication. We begin by defining individual agents, each responsible for a distinct subtask. One agent handles quality assessment—checking for clarity, relevance, and tone. Another retrieves missing product details from a knowledge base using retrieval-augmented generation (RAG). A third agent rewrites the review to meet editorial standards, ensuring consistency and professionalism. These agents operate independently but are orchestrated within a single workflow using LangGraph. The supervisor agent acts as the central decision-maker, receiving outputs from the subagents and determining the next step. It evaluates whether the review is ready for publication, requires additional enrichment, or needs human input. LangGraph enables conditional routing: based on the supervisor’s judgment, the workflow can branch to different paths—such as triggering a rewrite, fetching more data, or pausing for human review. This flexibility makes the system highly modular and adaptable to different content types or business rules. The human-in-the-loop component is implemented via a checkpoint. When the supervisor identifies a review that requires manual validation—due to ambiguity, potential bias, or sensitive content—the workflow pauses and routes the task to a human reviewer. The reviewer can approve, reject, or suggest edits. Once feedback is provided, the workflow resumes seamlessly. LangGraph supports interruptible workflows, allowing for pause, resume, and state persistence. This is essential for HITL integration, as it ensures that human reviewers can engage at their own pace without losing context. By combining specialized agents, intelligent supervision, and human oversight, this architecture delivers a robust, scalable solution for content moderation and enhancement in user-generated environments—such as e-commerce platforms, community forums, or social media. This implementation demonstrates key skills highly valued in GenAI interviews: workflow orchestration, agent design, conditional logic, and real-world integration of human judgment. Adding this project to your resume showcases not just technical proficiency, but also an understanding of responsible AI deployment and production-grade system design.