As agile teams increasingly rely on AI-generated outputs, organizations tend to prioritize explainability improvements to achieve effective governance outcomes. This paper argues that such responses are misdirected and reframes AI governance in agile delivery as an interpretive practice. Enterprise AI governance typically focuses on allocative governance practices, including decision rights, policy constraints, and audit mechanisms, leaving the interpretive governance, constituted through collective sensemaking, unaddressed. The study applies fuzzy-set Qualitative Comparative Analysis (fsQCA) on data from 143 agile practitioners to test three structural tensions that specify distinct governance challenges posed by AI-generated signals. The study identifies the deliberative team core, a set of conditions forming the primary sufficient configuration for distributing governance and sustaining adaptive capacity, with psychological safety as the most calibration-stable constituent; its absence could drive governance failure. The paper presents interpretive governance as a theoretically distinct concept and provides the initial empirical anchors for a multi-regime research program.