Role-Based Access Control for AI Agent Platforms

Not everyone in your organization should have the same access to AI agents. The intern should not configure production agents. The marketing team should not see engineering costs. The contractor should not approve deployments.

Role-Based Access Control (RBAC) ensures each person has exactly the permissions they need — no more, no less. For AI agent platforms, this means controlling who can create agents, who can approve actions, who can view costs, and who can activate the kill-switch.

3-Level Hierarchy

• Platform level — global admins who manage the entire deployment, set platform-wide policies, and handle billing
• Organization level — org owners who manage their organization, configure SSO, set org-wide budgets, and manage the gateway
• Tenant level — workspace users with 6 granular roles: Owner, Admin, Developer, Operator, Member, Viewer

The 6 Tenant Roles

• Owner — full control, billing, can delete the workspace
• Admin — manage agents, policies, team members. Cannot delete workspace
• Developer — create tasks, configure agents, view costs. Cannot manage team
• Operator — approve/reject agent actions, monitor activity. Cannot configure
• Member — create tasks, use agents. Limited visibility
• Viewer — read-only access to dashboards and reports

Why RBAC Matters for SOC 2

SOC 2 Trust Service Criteria require that access to systems is restricted based on roles. Auditors will ask: Who has admin access? How are permissions assigned? What happens when someone leaves? A well-designed RBAC system answers all three questions automatically.