Hybrid Token-Efficient AI Routing Agent

Hybrid Token-Efficient AI Routing Agent is an intelligent multi-model routing system designed to optimize large language model (LLM) inference by dynamically selecting the most appropriate AI model for each user prompt. Instead of sending every request to a large, expensive model, the system analyzes the prompt's intent and complexity, estimates the required reasoning capability, and routes it to the most cost-effective model available. It incorporates confidence scoring and quality validation, automatically escalating complex or low-confidence responses to more capable models when necessary. Built with FastAPI, React, and TypeScript, the application integrates with Fireworks AI to access powerful open-source models such as GPT-OSS-120B, enabling scalable and efficient AI inference on AMD infrastructure. The system also provides token usage analytics, routing metrics, and model selection transparency through an interactive web interface. By reducing unnecessary token consumption, lowering inference latency, and optimizing API costs while maintaining response quality, the Hybrid Token-Efficient AI Routing Agent demonstrates an efficient approach to deploying AI applications in production. This project showcases intelligent AI orchestration, dynamic model routing, and practical optimization techniques for real-world generative AI systems.