Make AI Reliable Enough for Business-Critical Decisions

The answer

AI reliability for business-critical decisions requires three properties that probabilistic AI lacks on its own: determinism (same policy, same result), accountability (every decision attributed to an auditable actor), and reversibility (decisions can be replayed, contested, and corrected). Corules provides all three as infrastructure. The AI model provides reasoning capability. Corules provides enforcement integrity. Together, the system is reliable enough for consequential decisions.

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.

// The three reliability properties in practice:

// 1. Determinism: same input + same policy → same outcome
// 2. Accountability: every decision has actor_id from signed claims
// 3. Reversibility: replay API returns identical result for any past decision

// No probabilistic component in the enforcement path.

Frequently Asked Questions