Confidence-Based Routing
An AI system architectural pattern in which the confidence score produced by a lightweight model dynamically determines whether a request should be escalated to a more capable model for processing. The approach is designed to balance overall system cost against decision quality.
Timeline (last 90 days)
Confidence routing means placing a classifier or cheap model in front of an Agent to first determine whether the current tool call falls into a risk tier such as readonly or destructive, and then decide whether a frontier model review is needed; its premise is that the cheap layer can return a decidable signal within the latency budget.
The challenge of confidence-based routing lies in threshold calibration: too strict a threshold causes a large number of requests to be escalated, negating the cost advantage, while too loose a threshold allows low-quality decisions to flow into the system.
The verification and calibration components are critical for making a model's confidence levels trustworthy.
All Facts (3)
Confidence routing means placing a classifier or cheap model in front of an Agent to first determine whether the current tool call falls into a risk tier such as readonly or destructive, and then decide whether a frontier model review is needed; its premise is that the cheap layer can return a decidable signal within the latency budget.
50%UnverifiedThe challenge of confidence-based routing lies in threshold calibration: too strict a threshold causes a large number of requests to be escalated, negating the cost advantage, while too loose a threshold allows low-quality decisions to flow into the system.
50%UnverifiedThe verification and calibration components are critical for making a model's confidence levels trustworthy.
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