IntelliRouter

We built FireworksAgent, a fast and efficient general-purpose AI agent for Track 1. The agent is designed to handle a wide range of natural language tasks, from answering factual questions and solving math problems to summarizing text, classifying sentiment, extracting named entities, debugging code, solving logic puzzles, and generating clean Python functions. The main goal was to create an agent that is not only accurate, but also efficient. Track 1 rewards teams that pass the accuracy threshold while using fewer Fireworks tokens, so FireworksAgent uses a smart hybrid strategy. For simple and predictable tasks, it answers directly using lightweight built-in reasoning. For harder tasks, it routes the request to a Fireworks model and uses focused instructions tailored to the task type. This helps balance quality and token efficiency. A key part of the work was making the agent robust across all eight benchmark categories. We added task-specific behavior for factual Q&A, mathematical reasoning, sentiment classification, summarization, named entity recognition, code debugging, logical reasoning, and code generation. Each category receives a focused answering style, so the agent can produce concise, relevant, judge-friendly responses instead of generic model output. We also improved the model routing strategy after learning more about the scoring system. Since the accuracy gate is 80% and the hidden evaluation contains only 19 tasks, every answer matters. FireworksAgent prioritizes accuracy first, then optimizes for efficiency. It prefers suitable low-cost model choices when possible, avoids unnecessary calls, and can recover if a selected model is unavailable.