OpenAI has released a GitHub repository showcasing a powerful demo of an AI-powered customer service agent built using its Agents SDK. The project presents a fully functional, web-based interface for an airline customer service system, designed to handle complex, real-world interactions with a high degree of reliability and security. At the heart of the demo is a multi-agent architecture featuring five AI agents working in concert, led by a central Triage Agent. This lead agent evaluates incoming user requests, determines the appropriate course of action, and delegates tasks to four specialized sub-agents—such as one for seat changes, another for flight status checks, and a third for handling cancellations and FAQs. The system is built to manage context and memory, allowing for natural, ongoing conversations that feel seamless and intelligent. A key strength of the demo is its built-in guardrails, which enforce both relevance and security. These prevent off-topic discussions, guard against potential jailbreak attempts, and ensure sensitive data isn’t exposed. The system also includes real-time tracking of active agents, visible in the UI’s Available Agents section, which is especially useful for developers during debugging and performance monitoring. The OpenAI Agents SDK, evolved from the earlier Swarm framework, is designed to be lightweight and flexible, offering minimal abstractions to support rapid prototyping and smooth scaling into production environments. The demo’s architecture supports end-to-end tracing, logging every step of a conversation for transparency and ease of optimization. To illustrate its capabilities, the demo includes pre-built workflows for common customer service scenarios. For example, if a user requests a seat change on a morning flight, the Triage Agent first verifies the user’s identity using a mock database tool, then hands the request to the Seat Change Agent. That agent checks availability and confirms the swap, while guardrails validate the request’s legitimacy and safety. The entire process is logged, enabling developers to review and refine the system. The setup is straightforward. Users can run the demo locally on macOS by first creating a virtual environment, cloning the repository, and installing backend and frontend dependencies. The backend, built with Python and Uvicorn, runs on port 8000, while the frontend, built with React, starts on port 3000. After setting the OpenAI API key in both the backend and frontend environments, users can launch both services in separate terminal tabs to access the full interface. This project is more than just a code sample—it’s a practical blueprint for developers looking to build robust, scalable AI applications. As AI agents become central to customer operations, this kind of multi-agent system offers a model for delivering faster, smarter, and more consistent support across industries. Developers are encouraged to fork the repository, customize the workflows, and apply the framework to their own use cases.