The example methodology below illustrates how an organization might evaluate AI agent accountability. It is conceptual, not an official standard or live rating.
| Dimension | What it evaluates | Weight |
|---|---|---|
| Ownership clarity | Whether each agent has a named owner, purpose, and business function | 15% |
| Permission scope | Whether access to tools, systems, and data is limited to the agent's defined role | 15% |
| Human approval | Whether sensitive or high-impact actions require confirmation | 15% |
| Activity logging | Whether agent actions are captured in a usable audit trail | 15% |
| Decision traceability | Whether outputs and actions can be connected to prompts, policies, data sources, or workflow steps | 15% |
| Failure escalation | Whether uncertainty, policy conflicts, or errors are routed to humans | 15% |
| Review cadence | Whether agent behavior is periodically evaluated, tested, and updated | 10% |
Weights shown are illustrative defaults. A real implementation would tune them per risk tier, regulatory context, and agent autonomy level.
Agents are tested informally with limited documentation. Owners may be implicit.
Agents have stated purposes and known owners. Basic inventory exists.
Agents have defined permissions, approval rules, and policy guardrails.
Agent activity is logged, reviewed, and traceable end-to-end.
Agent systems are continuously evaluated under formal governance standards.
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 dimensions, weights, score bands, or maturity levels under their own brand.