Governed work execution means every business action AI assists with passes through role scope, policy, approvals, governed capabilities, and evidence — before and after execution.
Why it matters
Uncontrolled execution — direct API access, silent permission expansion, or unaudited model actions — creates enterprise risk. Governed execution makes AI-assisted work accountable by design.
Operators experience this as allow/deny on capability requests with explainable reasons. Integrators enable it by publishing certified capabilities; architects define certification policy.
How it works
User + active role
→ Role Assistant (task)
→ Capability request
→ Operational Trust checks (gateway)
→ Execution (CIP / connector)
→ Outcome + Evidence
Each step is verifiable: who acted, which role applied, which capability ran, whether approval was required, and what outcome was recorded.
What governance includes
| Control | Purpose |
|---|---|
| Identity + active role | Who is acting and in which business capacity |
| Dimensions / protection | Which records are in scope |
| Certification state | Whether integration is AI-ready for this capability |
| Policy / approval | Whether execution may proceed now |
| Evidence capture | Proof for audit and learning |
Workers may recommend and request; the platform decides whether execution proceeds.
Limits
- Denied or pending outcomes are normal when policy, certification, or approval rules block execution — operators escalate through governance, not by bypassing the platform.
- This page describes runtime behavior; capability design and upload are covered under Build → Business Entities.
Operator scenarios
Allowed execution — Role, certification, and policy align; outcome recorded in Evidence Fabrix.
Denied execution — Escalate to governance if policy seems wrong; to integrators if certification is stale or capability missing.
Pending approval — Expected path for high-risk capabilities; not a platform error.
See Operator overview.