Define who can access what, which models are allowed, and what actions require human approval.
The Policy Engine is the governance backbone of the platform. It lets you define granular rules about who can access what, which models are allowed where, where data can and cannot flow, and what actions require human approval. Policies are evaluated before every agent action — not after the fact, and not as suggestions. They are enforced constraints.
The engine supports two types of guardrails. Hard guardrails block violations pre-flight: PII detection and redaction, data access scope verification, approval gates for sensitive actions, model restrictions, and rate limits. Soft guardrails evaluate actions post-hoc and flag anomalies for review without blocking execution. This dual approach lets you be strict where it matters and flexible where it doesn't.
For compliance teams, the Policy Engine provides the evidence trail auditors need. Every policy evaluation is logged — what was checked, what passed, what was blocked, and why. Policies can be versioned, tested in staging environments, and promoted to production through approval workflows. Policy is the architecture of your AI governance, not a patch applied after the fact.