How to Ensure LLM Outputs Comply With Internal Policy

The answer

LLMs are probabilistic — the same prompt can produce different outputs across runs. Internal policies are deterministic — a 25% discount cap means exactly 25%, every time, without exception. You cannot rely on the LLM to self-enforce policy because its outputs are not guaranteed to be consistent. The answer is a runtime enforcement layer that intercepts every LLM-proposed action and evaluates it against compiled policy before execution. Corules sits in this path: the LLM proposes, Corules evaluates, only compliant proposals execute. Policy is expressed in CEL (Common Expression Language), compiled at publish time, and evaluated in milliseconds — no LLM in the enforcement path.

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.

// LLM proposes: { discount_pct: 0.35, customer_tier: "standard" }
// Corules evaluates against compiled policy:
discount_pct <= params.max_discount_by_tier[context.customer_tier]
// → 0.35 <= 0.25 → false → BLOCK
// LLM proposal does not execute.

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