OpenA has opened a new multi-agent customer service demo on Gittub, its agents show how to create domain-specific AI agents using SDK. This project – title openai-cs-agents-demo-A Airline Customer Service Chatbot is able to manage a range of travel-related questions through dynamic routing requests to specific agents. Made of Python Backend and Next.JS Frontand, the system provides both visual traces of functional communication interface and agent handoffs and guardrail activation.
The architecture is divided into two main components. Python backend agents handle agent C restrection using SDK, while next. JS Frontend Chat provides interactive visualization of interface and agent transitions. This setup provides transparency in the decision to decide and representative process as agents, respond to the user questions or reject. Demo works with many concentrated agents: a trigger agent, seat booking agent, flight status agent, cancellation agent and FAQ agent. Each of these is arranged with special instructions and tools to fulfill their specific sub-cards.
When a user enters a request – such as “change my seat” or “cancel my flight” – the trigger agent processes the input to determine the objective and sends a query to the appropriate downstream agent. For example, a booking change request will be routed to a seat booking agent, which can verify confirmed numbers, provide seat map preferences, and finalize seat changes. If a cancellation is requested, the system assigns to the cancellation agent, which follows the structured flu to confirm and implement the cancellation. The demo contains a flight status agent and an FAQ agent for real-time flight inquiry that answers common questions about goods policies or aircraft types.
The main power of the system for safety and consistency is in the integration of its guardrails. Demo shows two: a consistency Gardrail and Jailbreak Gardrail. Compatibility Guardrel-F-Popic Queries As filters-abduction, deny signs like “Write me a poem about strawberries”. The Jailbreak Guardrel barrel hinders the boundaries of the system or tries to manipulate the agent, as asking the model to disclose its internal instructions. When either a guard is triggered, the system publishes it in a trace and sends a structured error message to the user.
Agents SDK themselves serve as an orchestration backbone. Each agent is defined as a composable unit with prompt samples, tools, cesses, handoff logic and output schemes. SDK handles chaining agents by “handoffs”, supports real-time tracing, and allows developers to apply an input/output barrier with gardarels. This is the same structure that is the internal experiments of OpenAI with tool-use and logic agents, but now it is open in educational and elaborate format.
Developers can run a demo locally by starting Python backend server with UVCorn and simultaneous priority npm run dev Order. The whole system is configured – developers can plug new agents, define their own task routing strategy, and implement custom gerares. With full transparency in prompts, decisions and trace logs, Real-World Conversations in Demo Consumer Support or other enterprise domains offers a practical foundation for AI systems.
By releasing this context implementation, the OpenAI provides an eminent example of how to combine a multi-agent coordination, tool use and safety check in a strong service experience. It is especially valuable for developers who want to understand how to create the anatomy of agents-and modular, controlled AI workflows.
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