As artificial intelligence systems increasingly move from experimental settings into real-world production environments, the primary challenge has shifted from model performance in isolation to the reliable governance of AI decisions under uncertainty. While modern AI systems—particularly large language models and agent-based architectures—demonstrate remarkable capabilities, they remain inherently probabilistic, opaque, and sensitive to context. In practice, most AI failures do not arise from the absence of logs or monitoring, but from the inability to interpret massive volumes of operational and decision logs in a way that meaningfully informs evaluation, learning, and control. This research addresses a fundamental gap in current AI operations: the lack of a unified, risk-centered framework for understanding, measuring, and improving AI …