Policy-as-Code for AI Agent Workflows
CTOs and platform engineers evaluating policy-as-code approaches to govern AI agent actions in enterprise workflows.
The answer
Policy-as-code for AI agents means expressing business rules, compliance constraints, and escalation thresholds as version-controlled, machine-executable code — and evaluating every AI-proposed action against that code at runtime, before execution. The key properties that make this work: (1) determinism — the same input and policy version always produce the same output; (2) compile-time validation — policy expressions are validated when they are authored, not when they evaluate a real decision; (3) parameter separation — policy logic and tenant-specific values are stored separately, so changing a discount cap does not require a code change; (4) version control — every published policy set is versioned, and historical decisions remain linked to the version that governed them. Corules uses CEL (Common Expression Language) — an open-source Google standard — as the policy expression language, providing all four properties.
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.
// Policy-as-code: version-controlled, compile-time validated, parameterized
// Logic stays constant; parameters change when business rules change
// Gate 1: what may the AI propose? (evaluated before AI reasoning)
POST /v1/constraints
→ { max_discount: 0.25, required_fields: ["customer_id", "deal_value"] }
// Gate 2: is this specific proposal valid? (evaluated before execution)
POST /v1/validate
Body: { discount_pct: 0.20, customer_tier: "premium" }
→ { outcome: "ALLOW", audit_id: "aud_01J..." }Frequently Asked Questions
What is the difference between policy-as-code and prompt-based enforcement?
Prompt-based enforcement instructs an LLM to follow rules in natural language — output is probabilistic and subject to prompt injection. Policy-as-code compiles rules into deterministic expressions that evaluate to the same result every time — independent of the AI model, user inputs, and context variations.
How do non-engineers author policies?
CEL expressions are structured and readable. Corules provides a policy authoring interface where business users define rules in a guided format. The platform compiles and validates expressions before publishing — catching errors before they reach production decisions.
What does policy versioning mean in practice?
Each time you publish a policy change, a new version is created. Existing decisions reference the version that was active when they were evaluated. The new version activates atomically — in-flight decisions complete against the previous version. You can compare outcome distributions across versions before and after a change.
See it working in your environment
Start free — no credit card, no sales call. Evaluate up to 1,000 decisions per month.
Get started free