Confidence Thresholds
Our reliability layer can use thresholds to determine whether a workflow proceeds automatically, receives additional review, requires human approval, or is rejected.
Not one number
Confidence is not treated as one universal scalar. The relevant threshold depends on the task and the consequence of being wrong.
Threshold outcomes
A confidence or quality state can route execution toward automatic continuation, another model review, human approval, or rejection.
- High enough → proceed
- Middle range → additional review
- Below boundary → human review or reject
The signal used for that routing can vary by workload. It may include model confidence where meaningful, deterministic validation results, retrieval completeness, source agreement, review outcomes, or other evidence about the quality of the result.
Consequence-sensitive design
A threshold appropriate for drafting an internal summary may be unacceptable for a consequential financial action. The acceptable error rate, review cost, and reversibility differ.
Threshold design therefore belongs to the workflow rather than to the model globally.
Calibration problem
Model-reported confidence should not automatically be interpreted as calibrated probability.
A system can sound certain while being wrong.
Where confidence values are used, they need to be evaluated against observed workload behavior. In other cases, a rule based on validation state or review outcome may be more reliable than a self-reported probability.
Escalation bands
Thresholds are most useful when they create explicit operating bands rather than a false binary of “AI works” versus “AI fails.” The system can automate the cases that clear a defined boundary, devote additional compute to ambiguous cases, and preserve human authority where consequence or uncertainty warrants it.
The goal is risk-adjusted automation, not maximum autonomy.
Thresholds require evaluation data
A threshold is only meaningful if the signal underneath it correlates with observed correctness or risk. For model-generated confidence, that relationship can be weak or workload-dependent. The threshold therefore needs empirical calibration against representative examples rather than an arbitrary percentage that merely appears conservative.
The same applies to composite quality states. A workflow may combine source agreement, deterministic validation, model review, and task-specific confidence into an operating band. The exact combination should be evaluated against real outcomes.
Thresholds also create a capacity tradeoff: tightening the boundary may reduce automated errors while increasing review volume. The correct setting depends on both the cost of a wrong action and the cost of escalating too much work back to humans.