Catch Tool Calls That Invent Missing Arguments

Agents fail quietly when they fill omitted tool arguments instead of refusing, and fluency-based evals often reward that invention. A compact negative golden set, scored by argument-diff rather than prose quality, catches those silent substitutions before they reach production traces. This article treats that failure as a testable contract, not as a prompt-tuning anecdote, and it stays useful without any vendor product. Recent developer discussion around agent workflows keeps returning to one operational surprise that chat logs tend to hide. Models do not only choose the wrong tool; they complete incomplete requests by guessing identifiers, dates, and scopes that nobody supplied. That behavior looks like initiative in a chat log, yet it resembles a clerk forging a zip code to stamp the form complete. The package then leaves the dock with valid-looking paperwork and the wrong city printed on the label. A conventional golden-answer harness scores the final sentence, which is the wrong surface for tool-using agents. The dangerous artifact is the tool payload, because downstream systems will execute invented primary keys with perfect syntax. If your eval suite only checks that a transfer looks helpful, it will greenlight a call that moved the wrong account. The pattern below is a proposal you can run locally, and it does not claim production metrics. It also does not depend on a particular model family or on a hosted evaluation service. You should treat every numeric threshold in the grader as a starting point rather than a published benchmark. Negative goldens assert a hole, not a pretty answer A positive golden case says the model should produce a known good action given a complete request. A negative golden case says the opposite: given a hole in the input, the model must not paper over that hole. The assertion is closer to a check constraint than to a writing rubric, because the failure is an illegal completion. Fluency still matters for users, but it is a poor prox