The example framework below illustrates how organizations might structure oversight for AI agents. It is a concept, not an official standard or certification program.
| Pillar | What it covers |
|---|---|
| Identity | Every agent has a clear owner, purpose, and scope. Anonymous or undocumented agents are not in scope for production use. |
| Permissions | Agents only access tools, systems, and data required for their stated role. Permissions are reviewed on cadence, not granted indefinitely. |
| Human approval | Sensitive or high-impact actions require explicit review or confirmation. Thresholds are documented, not improvised at runtime. |
| Logging | Agent activity is recorded in a usable audit trail — prompts, tool calls, outputs, and outcomes — durable enough for review. |
| Evaluation | Agents are tested for reliability, safety, and policy compliance before deployment and on an ongoing schedule. |
| Escalation | Failures, uncertainty, and policy conflicts route to a human owner. The agent does not silently absorb the problem. |
The six pillars are illustrative. Real implementations might split or combine them based on sector, risk tier, and existing controls.
Agents are in pilot use with limited scope. Owners may be implicit, controls are informal.
Agents have stated purposes, named owners, and basic permission boundaries.
Agent activity is logged and reviewed. Approval rules exist for sensitive actions.
Formal controls and escalation paths are in place. Evaluation runs on a documented schedule.
Agents are continuously evaluated under organizational standards, with independent review.
The domain and the source for this concept site are available as a clean transfer. The framework above is illustrative — the next owner is free to redefine pillars, maturity levels, or scoring under their own brand.