dag-confidence-scorer

Calibrate agent confidence in a DAG pipeline by applying a multi-factor scoring metric that evaluates reasoning quality, source reliability, consistency, completeness, and uncertainty to generate a calibrated score.

Gate DAG outputs using configurable confidence thresholds that trigger automated actions, routing outputs toward accept, review, iterate, or reject decisions based on calibrated scores and historical accuracy.

Multi-factor confidence scoring works by computing weighted scores across reasoning quality, source reliability, internal consistency, completeness, and uncertainty, then calibrating raw scores using historical accuracy and model bias.

Yes, you can adjust source reliability and reasoning quality weights during confidence calibration by configuring the exposed scoring parameters to align with your specific decision-making and risk-aware routing requirements.

You need calibrated confidence scoring for iterative refinement when routing DAG outputs requires risk-aware decisions, using calibrated thresholds to determine whether to accept, review, iterate, or reject generated content.

Inconsistent confidence decisions occur when raw scores lack calibration for task difficulty and model bias; applying calibrated thresholds standardizes the accept, review, iterate, and reject gating process.