This research explores the concept of Institutional Decision Reliability Infrastructure as an emerging approach for trustworthy artificial intelligence adoption. The study focuses on the relationship between evidence quality, governance mechanisms, decision traceability, replay capability and outcome calibration. AICOS is designed as a framework for organizations that require accountable and reviewable decision processes in complex operational environments. The proposed architecture emphasizes:- evidence-based decision formation,- reproducible decision analysis,- audit-oriented governance,- continuous learning through outcome feedback. The research distinguishes between artificial intelligence model performance and institutional decision reliability, proposing that reliable decision systems require both analytical capability and governance infrastructure. Future work includes empirical validation, domain-specific implementations and comparative evaluation across different institutional environments.

This paper introduces AICOS (AI-enabled Institutional Decision Reliability Infrastructure), a governance-oriented framework designed to improve the reliability, transparency and reproducibility of institutional decision processes. The proposed architecture connects evidence management, policy alignment, intelligence processing, decision execution, replay mechanisms, outcome evaluation and calibration within a structured lifecycle. Unlike approaches focused only on prediction performance, AICOS emphasizes the complete decision lifecycle by preserving relationships between evidence, decisions and measurable outcomes. The framework introduces concepts including replayable decisions, evidence-based governance, decision reliability measurement and outcome calibration. This research presents an architectural and conceptual framework. Further empirical validation across different institutional environments is required to evaluate practical performance and scalability.