Assign Roles to the Agent
Go to Administration > RBAC > Role Bindings and create a binding for the agent:
| Field | What it means | Example |
|---|---|---|
| Subject | The agent's identity | ml-training-agent |
| Role | The role you want to assign | data-processor |
| Scope | An optional boundary to limit where the role applies | project:ml-pipeline |
The role defines what the agent is allowed to do. For example, a data-processor role might allow:
data:read— Read training datasetsmodel:write— Write model artifactslogs:write— Write execution logs
warning
Only give the agent the permissions it actually needs. Avoid assigning broad roles like admin to autonomous workloads.
Next Step
Once roles are assigned, configure the allowed scopes on the client to control which permissions the agent can request.