How to Make AI Follow Company Policy
Enterprise teams looking for technical solutions to enforce business rules in AI agents without retraining models.
El problema
The core problem is that large language models are probabilistic — they approximate the right answer rather than guaranteeing it. Company policies are deterministic — they have exact thresholds, eligibility rules, and required sign-offs. Corules bridges this gap by sitting between AI output and execution. AI proposes a decision; Corules evaluates it against your structured policy (expressed in CEL) before any action is taken. Compliant decisions proceed. Non-compliant decisions are blocked or escalated. The model never needs retraining when policy changes — only the parameters update.
Cómo lo resuelve Corules
Corules's policy runtime evaluates structured context against compiled CEL expressions — returning ALLOW, BLOCK, or ESCALATE with a reason and audit ID.
Ejemplo de política
// Example: enforce discount policy at runtime
// Policy logic doesn't change when rates change — only params update
discount_pct <= params.max_discount_by_tier[context.customer_tier]
&& (deal_value * (1 - discount_pct)) >= params.margin_floorFrequently Asked Questions
Do I need to retrain the AI model when policy changes?
No. Corules separates policy logic from model logic. When your discount cap changes, you update a parameter — not the model. The CEL expression stays the same.
Does this work with any AI model?
Yes. Corules evaluates the structured output of any AI model. The model produces a proposed decision; Corules validates it. Model-agnostic by design.
What if the AI generates an output Corules doesn't recognize?
Unknown or malformed decision payloads are rejected. Missing required fields escalate. Safe defaults: on any failure, block or escalate, never silently allow.
How long does evaluation take?
Policy evaluation is deterministic CEL compiled at publish time. Evaluation latency is typically sub-10ms — fast enough for synchronous workflow integration.