Decision Reliability Infrastructure: A Framework for Replayable Governance, Outcome Calibration, and Institutional Trust in Artificial Intelligence Systems

This paper introduces Decision Reliability Infrastructure (DRI), a governance-first framework designed to enable institutions to create replayable, outcome-calibrated, and trust-aware decision systems. While modern organizations possess mature data infrastructures and increasingly sophisticated Artificial Intelligence capabilities, they often lack mechanisms for measuring the long-term reliability of decisions. This work argues that a dedicated Decision Reliability Infrastructure layer is required to bridge the gap between intelligent systems and institutional trust. The paper presents the AICOS architecture, consisting of Evidence, Policy, Intelligence, Decision, Replay, Outcome, Calibration, Trust, and Institutional Memory layers. It further introduces the concepts of the Decision Reliability Index (DRI) and Decision Capital as quantitative mechanisms for evaluating institutional decision quality and accumulated decision value. The framework is intended for applications in banking, government, defense, energy, healthcare, and other mission-critical environments where accountability, transparency, replayability, and continuous learning are essential. Keywords: Decision Reliability, Artificial Intelligence, Institutional AI, Governance, Replayability, Outcome Calibration, Trustworthy AI, Decision Capital, Decision Intelligence, AICOS.