Zephyr: Adaptive Token Routing Agent

## Zephyr: Adaptive Token Routing Agent Zephyr is a hybrid AI routing agent built to solve tasks with the lowest possible Fireworks AI token consumption while maintaining high accuracy. Instead of sending every request directly to a cloud language model, Zephyr evaluates each task and selects the most efficient execution path. The routing process follows a hierarchical strategy. First, Zephyr attempts to solve structured problems using deterministic algorithms such as mathematical computation, data processing, parsing, and other rule-based techniques that require no language model. If a deterministic solution is not possible, the request is forwarded to a lightweight local language model running with Ollama. Local inference provides high-quality responses without consuming any Fireworks tokens. Only when the local model cannot confidently answer a task does Zephyr escalate the request to the most suitable Fireworks model. To maximize efficiency, Zephyr combines task classification, confidence estimation, response validation, semantic caching, prompt compression, and adaptive model selection. Every routing decision is designed to balance accuracy, latency, and token cost. Responses generated locally are verified before being returned, while cloud inference is treated as a last resort rather than the default approach. The system also records routing decisions, token usage, response times, and validation results, making it easy to evaluate performance and continuously improve routing policies. This architecture demonstrates that intelligent orchestration can dramatically reduce inference costs while preserving answer quality. Zephyr is designed specifically for the Hybrid Token-Efficient Routing Agent challenge, showcasing how deterministic computation, local inference, and selective cloud reasoning can work together to build practical, scalable, and cost-efficient AI systems.