Managing Risk From Autonomous AI Agents in the Enterprise

The answer

Autonomous AI agents create three categories of enterprise risk: execution risk (the agent takes an action outside permitted bounds), audit risk (you cannot reconstruct or defend the decision), and regulatory risk (the action violates a compliance obligation). All three can be addressed at the architecture level. Execution risk is eliminated by a runtime enforcement layer that evaluates every proposed action before it executes. Audit risk is eliminated by an immutable decision log with policy version and actor identity. Regulatory risk is reduced by designing the enforcement layer to satisfy framework requirements (EU AI Act Article 9–14, NIST AI RMF) by construction. Corules addresses all three at the infrastructure level — not as a post-hoc control.

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.

// Autonomous agent cannot act outside policy bounds:
// Agent proposes action → Corules evaluates → only ALLOW proceeds
// BLOCK: action violates policy → agent receives reason, does not execute
// ESCALATE: action requires human authority → routed to human reviewer
// ALLOW: action within bounds → executes, logged to immutable ledger

Frequently Asked Questions