AI support systems often use a single model for all queries, which can lead to inefficiencies such as overuse of expensive models for simple tasks and inadequate handling of complex queries. A more efficient approach involves routing simple queries to fast models and escalating complex ones to reasoning models, coupled with infrastructure that includes flow control, durable execution, and streaming capabilities for real-time responses. This setup, illustrated using Inngest, NextJS, and OpenAI APIs, prevents cost overruns and infrastructure bottlenecks by employing concurrency keys and throttling to manage resources and costs effectively. The architecture also supports scalability and reliability by separating fast and reasoning agents, each with its own flow control settings, and uses event-driven design for independent scaling and priority routing. Additionally, the system leverages a structured database schema to track customer interactions, enabling precise cost management and performance analysis. This solution provides a robust framework for building scalable and efficient AI support systems without the need for extensive custom infrastructure development.