Where does an AI agent's authority to act actually end?
Seven BFSI workflow stages, mapped — where agentic AI runs unattended, and where CBUAE and SAMA expectations put a named human on the hook
Agentic AI has moved from recommending to executing. That changes the governance question. It's no longer whether the system can complete a workflow — it's where your organization is willing to let it run without a human accountable for the outcome. Get that placement wrong once, and it's not a model error. It's an audit finding with your name on it.
What the map covers
- Seven workflow stages, classified — autonomous execution vs. mandatory human checkpoint
- The regulatory logic behind each classification — tied to specific CBUAE and SAMA provisions on consumer protection, AML/CFT, outsourcing, and model governance
- What a checkpoint has to hold up under scrutiny — a named reviewer, a tamper-evident audit trail, a customer-ready explanation, a defined escalation path when human and agent disagree
- A worked example — credit pre-qualification, from agent recommendation to officer sign-off, without adding a bottleneck
Why this matters now
A checkpoint decided at design time is a conversation. The same checkpoint discovered after deployment is a rebuild — usually the week before an examination.
Built for CIOs, CTOs, Chief Digital Officers, and Risk & Compliance technology leads operating agentic AI in UAE and Saudi banking.
See where your build sits on this map — before your next audit does.
