This paper introduces AICOS (AI Cognitive Operating System), a governance-first deterministic decision infrastructure designed to address fundamental limitations in modern artificial intelligence systems. While existing AI approaches focus on prediction and probabilistic inference, AICOS establishes a formal separation between prediction and decision-making, treating decisions as constrained, auditable, and authority-bound processes. The framework integrates irreversibility-aware risk modeling, uncertainty thresholds, Monte Carlo simulation, and deterministic replay to ensure that decisions are reproducible, regulator-ready, and aligned with human-final authority principles. By embedding governance, compliance, and auditability directly into the decision function, AICOS transforms artificial intelligence from a predictive tool into a structured decision infrastructure. Through mathematical formalization, architectural design, and real-world case studies in financial and energy systems, this work demonstrates how decision-centric AI systems can operate reliably under uncertainty and regulatory constraints. The findings suggest that the next generation of artificial intelligence will be defined not by predictive accuracy alone, but by the ability to produce deterministic, transparent, and accountable decisions.
Yasin Kalafatoglu (2026) studied this question.