If you write rules in an AGENTS.md, this one is worth your time.

When a coding agent follows your rule, it may have been going to do that anyway.

Harness-IF separates the two by scoring 256 rules one at a time from execution evidence, then re-running every task with the rule withheld across nine probe builds to find which rules actually oppose the model's defaults.

Across 12 frontier models, raw accuracy runs 72.1 to 85.9%, and Against-Prior Accuracy runs 66.1 to 78.6%. Every model gets worse once coincidence is stripped out, by 3.6 to 7.4 points.

One finding worth flagging. Precedence does not follow prompt depth. System prompts, project files, and user instructions all outrank tool and skill descriptions.

Paper: https://arxiv.org/abs/2608.11727

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