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AMDA — Verify Before You Pay
Most "token-efficient routing agents" are really just classifiers wearing a trench coat: guess the task type, call one paid model, hope for the best. AMDA doesn't do that. AMDA runs a local model first — free, unscored, zero token cost — and only calls a paid remote model when it genuinely can't trust its own work. The key is how it decides "trust": not vibes, not a second LLM asking itself "are you sure?", but actual execution-grounded verification. Generated code gets run against spec-derived assertions pulled straight from the prompt. Math answers get re-derived with a program-aided check instead of just eyeballed. Logic puzzles get solved independently with a constraint solver and cross-checked against the model's own answer. Only when local genuinely fails that bar does AMDA escalate to remote — and even then it throttles and caps what it spends. The result: most tasks resolve at zero scored tokens. The ones that do escalate spend as few tokens as possible, because by the time a task reaches the remote call, AMDA already knows exactly what failed and why. We stress-tested this on a 228-task internal benchmark across 8 categories — code generation/debugging, math, logic puzzles, NER, sentiment, summarization, factual QA — and caught real bugs along the way, like a code verifier that only checked "does this run" instead of "is this correct," and a logic-puzzle formatter that got the right answer in the wrong label format. We also read actual competitor source code instead of guessing: most either skip verification entirely or fake it by having the same model second-guess itself. We didn't find anyone else doing independent, execution-grounded re-derivation before spending a paid token. Built for AMD Developer Hackathon Act II, Track 1, where the scoreboard rewards being right and being cheap. AMDA bets that verifying first is how you win both.
13 Jul 2026
