AI-Augmented Impact Frames: A Constrained, Closed-Loop Architecture for Purpose-Aligned Decision Learning

AI-Augmented Impact Frames is a formally specified, constrained, closed-loop architecture for studying how organisational decision systems learn over time. The framework treats the decision system—rather than individual decisions or decision-makers—as the primary unit of analysis. The architecture combines: • a formal Operating Spine linking purpose, capabilities, value drivers, strategy, portfolios, and signals• constrained AI-supported interpretation that preserves human decision authority• psychometrically grounded longitudinal measurement using Item Response Theory (IRT) Rather than optimising or automating decisions, the framework is designed to make decision logic explicit, measurable, and falsifiable. AI functions only as interpretive support and is structurally prohibited from recommendations or decision authority. This work defines the architectural and methodological foundation of the Impact Frames research programme. It specifies boundary conditions, failure modes, and falsifiability criteria for empirical evaluation of decision-system learning under conditions of delayed feedback and high uncertainty. Version 1.01 clarifies terminology, strengthens falsifiability statements, and improves conceptual definitions without changing the underlying architecture. No empirical datasets are associated with this version. Measurement instruments, code scaffolds, and empirical studies will be released in subsequent linked records. Version 1.03 update: This version restores the intended architectural manuscript file. Version 1.02 contained a mistakenly uploaded file that did not reflect substantive changes to this record. No conceptual, methodological, or structural content has been altered relative to Version 1.01.