The article examines Human-in-the-Loop (HITL) architectures for automated decision-making systems deployed in enterprise operations and regulated domains. The topic’s relevance follows from the rapid adoption of high-capacity models alongside stricter requirements for accountability, traceability, explainability, and risk control. The paper’s novelty lies in formalizing a taxonomy of intervention modes and linking engineering choices to operational metrics rather than to model accuracy alone. The study identifies four recurring intervention patterns—pre-emptive review, confidence-based routing, asynchronous audit, and exception handling—and specifies their placement within the decision pipeline. The analytical basis relies on a comparative synthesis of documented production deployments in finance, healthcare, and corporate operations, focusing on throughput, decision quality, latency, and per-case processing cost. The results indicate a non-linear trade-off between automation rate and decision quality and show that optimal thresholding depends on risk asymmetry and governance constraints. Practical recommendations address uncertainty calibration, reviewer interface design, and closed-loop feedback capture for continuous improvement. The overall objective is to provide a deployment-oriented framework for selecting HITL patterns and tuning escalation thresholds in high-stakes settings.

human-in-the-loop, automated decision-making, uncertainty calibration, confidence-based routing, auditability, explainable AI, governance, active learning, operational metrics, high-stakes systems

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