Deterministic vs Probabilistic AI Policy Enforcement

Le problème

Probabilistic AI (LLMs) is excellent at reasoning, drafting, and summarizing. It is structurally unsuitable for policy enforcement because the same input may produce different outputs across runs. Business policies require determinism: if a discount is over 25%, it is always blocked. Corules uses CEL (Common Expression Language), a deterministic expression evaluation engine. Given identical input and policy version, evaluation always returns identical output. This is a fundamental architectural property, not a feature — it is what makes policy enforcement auditable and legally defensible.

Comment Corules le résout

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

Exemple de politique

// CEL evaluation is deterministic:
// Input: { discount_pct: 0.30, customer_tier: "standard" }
// params: { max_discount_by_tier: { standard: 0.25 } }
// Result: ALWAYS BLOCK — every time, without exception
discount_pct <= params.max_discount_by_tier[context.customer_tier]
// → false → BLOCK

Frequently Asked Questions