AI Decision Reproducibility and Testing

Das Problem

Corules decisions are reproducible by design. Each evaluation stores a policy set version and a normalized input hash. Given these two values, the exact evaluation can be reproduced at any future point — even after policy updates — by specifying the historical policy version. This enables: regression testing when policy changes (run historical cases against new policy), fairness testing (replay a sample of decisions across demographic segments), and regulatory examination (prove that the policy in effect at time T produced outcome O for input I).

So löst Corules es

Corules's policy runtime evaluates structured context against compiled CEL expressions — returning ALLOW, BLOCK, or ESCALATE with a reason and audit ID.

Richtlinienbeispiel

// Replay any historical decision:
POST /v1/validate/replay
{
  "audit_id": "aud_01J...",
  "policy_set_version": "pset_v2.1.0"  // historical version
}
// Returns identical result as original evaluation

Frequently Asked Questions