From Operation to Interface: Where Harness Changes Enter a Fixed Model’s Tool Calls

Procedures that improve a coding agent without touching its weights edit its prompts, tools and middleware and keep the edits that raise task success, but task success alone does not reveal which aspects of tool use an edit changes. We study gpt-oss-20b under six harnesses on SWE-bench Verified and separate each classifiable tool-call attempt into an operation category (such as search or read) and an invocation interface (shell or API-style). In TOOLS, the layer-20 state at the first generated-token position adds +11.8 accuracy points for the operation but +1.0 for the interface beyond a structured call-history baseline, and an operation decoder transfers across most harnesses and across interfaces. At file-read-matched anchors, TOOLS and SEED differ by +45.8 points in API share, compared with 4.4 points of total variation in the operation distribution, which does not exceed its permutation null. Rewriting past reads between shell and API forms shifts API share by +40.8 and −36.2 points, with smaller operation shifts of 8.6 and 2.5 points, while replacing search hits with no matches shifts the operation by 29.0 points and API share by −6.5. The frozen operation decoder responds most strongly to this result rewrite, but pushing the state along the decoder’s direction changes the decoder readout without establishing targeted control of the next operation. These findings separate changes in operation choice from history-sensitive changes in invocation interface.