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R.A.H.S.I. Reconstructable AI™ | Reconstructing Sources, Decisions and Outcomes | R.A.H.S.I. Framework™
Reconstructable AI connects AI traces, audit, retained evidence and evaluation to reconstruct enterprise AI sources, decisions and outcomes.
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R.A.H.S.I. Reconstructable AI™ | Reconstructing Sources, Decisions and Outcomes | R.A.H.S.I. Framework™
An enterprise AI system is not governable simply because its final answer can be reviewed. The harder test is whether the organisation can reconstruct what actually happened.
- Which interaction was retained?
- Which sources were retrieved?
- Which tools were invoked?
- What sequence of actions occurred?
- How was the result evaluated?
- What evidence remains available later?
Microsoft Purview: Preserve the Evidence
Microsoft Purview can retain supported Copilot and AI interactions for compliance, surface retained content through eDiscovery, and provide audit records that help investigators understand who did what, when, and through which workload.
What users can still see in an interface is not the same as the compliance evidence an organisation may need to preserve.
Microsoft Foundry: Reconstruct Execution
Microsoft Foundry tracing adds execution-level evidence. OpenTelemetry-based traces can capture model calls, tool invocations, inputs, outputs, retries, latency, status, and workflow relationships.
Tracing changes the question from:
What answer did the AI produce?
to:
What execution path produced that answer?
Evaluation: Judge the Run
Evaluation adds a separate assurance layer. Built-in evaluators and rubrics can assess groundedness, relevance, task adherence, safety, and tool-use quality.
Tracing explains what happened. Evaluation helps determine whether what happened was acceptable.
Copilot Studio: Operational Context
Copilot Studio analytics and telemetry add engagement, sessions, outcomes, component behaviour, and environment-level traces.
Together, these capabilities form a stronger evidence chain:
Interaction → Identity → Agent → Model → Tool → Result → Evaluation → Audit → Retained Evidence
No single dashboard proves accountability. No trace explains retention. No evaluation score proves what was executed. Assurance comes from connecting the layers.
That is the principle behind R.A.H.S.I. Reconstructable AI™: consequential AI outcomes should be explainable, investigable, and reconstructable from retained evidence—not merely observable after the fact.
Reconstructability turns telemetry into evidence that supports accountable enterprise AI.
