evidence-standards

To prevent AI agent hallucinations and enforce data traceability, apply strict evidence standards that mandate agents return only source data, use exact values, and provide stable source references for all reported information.

Rules ensuring data sub-agents return exact values mandate that agents extract only data present in tool outputs without rounding or paraphrasing, and enforce domain boundaries to keep operations within designated scopes.

Handling derived values in agent governance frameworks requires explicitly marking computed values and citing their original input sources to maintain full traceability and prevent hallucinated data reporting.

Yes, you can enforce domain boundaries to keep data agents within their designated operational scope, preventing unauthorized data access and ensuring agents only process information relevant to their specific domain.

Source traceability in data governance ensures every data point returned by sub-agents can be traced back to its original source, which is needed whenever reliable, verifiable data reporting is required.

Limitations of enforcing reporting standards include potential rigidity in handling ambiguous data sources, requiring clearly defined domain boundaries and structured tool outputs to function effectively without modification.