Frugal Router, a Token-Efficient AI Agent

Track 1 rewards one thing above all, passing the accuracy gate on the fewest Fireworks tokens. Frugal Router treats that as a confidence problem, not a routing problem. Every scored answer has to come from a Fireworks call, so the real question is not whether to call the API but how cheap each call can be. A free local model runs first. It classifies each task into one of the eight categories, drafts an answer, and measures its own confidence using self-consistency voting and a pessimistic logprob signal. High confidence lets the agent send a compact confirmation prompt. Low confidence triggers a full remote solve, with reasoning only where a category needs it. Capping reasoning effort cut completion tokens by roughly a third in testing with no loss of accuracy. The agent is built around the harness contract. It reads tasks from the input file, writes a valid results file after every task so a timeout never zeroes the run, and always exits cleanly. Remote calls run in parallel so a large batch finishes inside the ten minute budget. Answers are shaped for the intent judge, so a sentiment reply carries its justification and a debugging reply carries the corrected code. On a local evaluation across all eight categories the agent reached full accuracy while spending under two hundred tokens per task. Model ids are read from the environment at run time, nothing is cached or hardcoded, and no credentials ship in the image.