explain

To audit agent decisions and trace how conclusions are drawn, you need a decision-auditing mechanism that provides causal context and provenance data. This skill generates human-readable rationale and rule traces to clarify reasoning chains for governance reviews.

Explaining graph analysis results for governance reviews requires surfacing upstream and downstream causal chains. This skill traces graph nodes and generates concise, human-friendly rationale with provenance data to support compliance checks.

Generating human-readable rationale for automated reasoning outputs involves exposing decision traces and confidence factors. This skill uses an internal ExplanationGenerator to transform complex reasoning logic into clear, auditable explanations for compliance reviews.

Decision traceability works with provenance data for compliance auditing by exposing tracing APIs for decisions and graphs. This skill surfaces audit trails and causal context, ensuring that governance reviews have full visibility into rule traces and confidence factors.

You need explainability and causal context for graph results when you must validate decision logic for governance or compliance. This skill provides traceable clarity by surfacing upstream and downstream explanations for specific graph nodes and rule traces.