Confidence-Gated Model Invocation: A Decision-Quality Framework for Governing Machine Learning Outputs Within Enterprise Orchestration Pipelines

Abstract

A model's internal confidence score is not, by itself, a decision about what an orchestration pipeline should do with the output that score accompanies. It is a signal. A pipeline that treats every signal identically has made an implicit governance decision without ever examining it.

This article develops a framework for treating the handling of that signal as an explicit architectural control at the orchestration layer, in place of the unexamined default in which any output crossing the pipeline boundary is executed as though it were certain. The research objective is to determine why production incidents attributed to model error are frequently better explained by an absent or poorly designed decision boundary between a model's output and the pipeline's action on it.

The research design synthesises calibration research, human-in-the-loop decision literature, and orchestration resilience patterns, read from the standpoint of an integration architect accountable for pipelines where automated decisions carry contractual or customer-facing consequences. The method derives three governance mechanisms  -  confidence-threshold routing, override logging with an enforced audit trail, and escalation-path design  -  filtered specifically to the orchestration-enforcement layer rather than the model-internal layer.

A controlled experiment reported by Sele and Chugunova supplies the framework's governing caution. Participants preferred an algorithmic recommender over an equally accurate human 66 per cent of the time, and that preference rose by a further seven percentage points once human-in-the-loop review became available  -  yet decisions made under the reviewed condition were significantly less accurate than those made under full delegation [5]. A review step must therefore be governed rather than assumed beneficial by default.

The outcome is a decision-quality framework in which every model output crossing the orchestration boundary is routed, logged, or escalated according to an explicit policy. The conclusion is that this boundary, not the underlying model's own accuracy, determines whether machine-learning-augmented automation is safe to deploy at production scale.