Agents that call tools are graded step by step by a second model, a large language model (LLM) judge, which reads a text transcript built from the recorded run. If the builder drops content, the judge grades a step it cannot see.
The team compared transcripts with source records, fed broken inputs to the two automatic checkers, and put a stand-in where the judge sits to see what bytes reach it.
Every machine-readable tool call was dropped, so nearly all judged steps looked blank although fingerprint hashes (which only prove bytes did not change) matched. Restoring calls cut, from 13 to 2, how many more wrong steps than correct ones one judge flagged after later context was added.
