Calibration
CIRK improves through real-world usage.
Teams should calibrate scoring based on observed execution outcomes.
What to track
Useful feedback signals include:
- Number of iterations before acceptance
- Review time
- Defects found during review
- Incidents after merge or deploy
- Rollback frequency
- Change failure rate by CIRK profile
Calibration goal
The goal is not perfect prediction.
The goal is better alignment between estimated execution complexity and observed reality.
"Consistency matters more than precision."
A stable model that teams trust is more useful than a theoretically perfect model nobody can apply.
What calibration can improve
With enough usage data, teams can:
- Refine what qualifies as C1/C2/C3
- Tighten policy thresholds for R3 and K3
- Improve draft-first triggers for I3 work
- Compare expected vs observed review burden
Long-term direction
Over time, CIRK can support:
- Better execution policy defaults
- Stronger automation boundaries
- More predictable review flows
- Empirical governance improvements for AI delivery