How to Make AI Follow Company Policy

The answer

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.

How it works

Corules's policy runtime sits in the enforcement path between your AI agent and the action it wants to take. The agent sends a structured context payload to /v1/validate. Corules evaluates the context against a compiled CEL policy set and returns a structured decision — ALLOW, BLOCK, or ESCALATE — with a reason and audit ID.

Every decision is recorded in an immutable audit ledger. You can replay any past decision by providing the policy_set_version and the normalized input hash — the result will be identical.

Policy example

Policies are written in CEL (Common Expression Language). They are compiled once at publish time and evaluated in microseconds at request time.

// 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_floor

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