Kaio-Ken Protocol

Kaio-Ken Protocol is a hybrid AI inference framework designed to maximize response quality while minimizing cloud inference costs through intelligent model routing. Inspired by the concept of using additional power only when necessary, the protocol dynamically determines whether a task can be reliably solved using a lightweight local language model or should be escalated to a more powerful Fireworks-hosted model. The system supports a diverse range of tasks, including factual question answering, mathematical reasoning, summarization, named entity recognition, sentiment analysis, code debugging, logic puzzles, and code generation. Every incoming request is first classified into its respective task category. A local language model then generates an initial response and performs a structured self-evaluation, scoring the answer on correctness, completeness, confidence, and hallucination risk. These metrics are combined into a confidence score, which is compared against configurable thresholds tailored to each task type. If the confidence exceeds the required threshold, the local response is returned immediately, reducing API usage and latency. If the confidence falls below the threshold, the request is seamlessly escalated to a Fireworks model to ensure a higher-quality response. Built with a modular Python architecture and packaged as a Docker container, **Kaio-Ken Protocol** is lightweight, portable, and easy to deploy. By combining adaptive task classification, confidence-aware decision making, and hybrid local/cloud inference, the protocol provides an efficient balance between accuracy, performance, and token consumption. This design directly addresses the AMD Developer Hackathon Track 1 objective of building a general-purpose AI agent that delivers strong performance while optimizing Fireworks API token efficiency.