Decision trace capture
Exposed as a scoped capability with its own evidence trail, not a hidden setting.
Knowledge & context · Concept
A decision observability system that records how human and AI decisions were formed, authorized, executed and evaluated.
DecisionTracing is built for AI governance teams, compliance officers, platform engineers, enterprise operators.
DecisionTracing replaces disconnected logs, incomplete audit trails and retrospective manual decision reconstruction.
A decision observability system that records how human and AI decisions were formed, authorized, executed and evaluated.
Work stays inside an explicit scope: named sources, defined permissions, bounded execution, retained evidence.
Five contracts the product is specified around. Each one is a boundary as much as a feature.
Exposed as a scoped capability with its own evidence trail, not a hidden setting.
Available through the product surface and the machine interfaces, with permission attached.
Defined as an explicit contract so operators can review it before it runs.
Available through the product surface and the machine interfaces, with permission attached.
Exposed as a scoped capability with its own evidence trail, not a hidden setting.
The same three steps apply whether a person or an agent is driving.
Start from the boundary DecisionTracing is allowed to act inside: the workspace, the sources, the budget, the authority.
Expose the same capability through the interface a human uses and the interface an agent calls, with one permission model.
Every action leaves a record that can be inspected, replayed and challenged before the next one runs.
DecisionTracing is documented as a concept: requirements, interfaces and evidence expectations. It is not presented as a live service.