Regulators don't ask whether your AI is accurate. They ask: for this decision, why did the agent recommend what it did, what data did it use, and did a human review it? Explainability & Decision Audit has the answer.
Without governed explainability & decision audit, these problems compound silently with every new data source and AI deployment.
SHAP values tell you what the model weighted. They don't tell you what data was consumed, whether guardrails fired, or whether a human reviewed the output.
Agent recommendations are logged. Human reviewer judgement is a checkbox. Both require the same documentation standard in regulated industries.
Decisions scattered across agent logs, review queues, and governance reports. No single repository where an auditor can retrieve complete explainability context.
Every agent decision logged as a structured record — data, governance, reasoning, output
Guardrail events, HITL actions, and quality attestations attached to the decision record
Three-level explainability generated — data, model, and decision level — human-readable
Decision records indexed in a governed, queryable repository — searchable and auditable
Complete evidence package assembled for regulatory examination — on demand, no manual work
Every decision captured as a structured, governed record — data sources, quality attestations, guardrail events, model reasoning, human oversight, outcome.
Data-level: where did information come from. Model-level: why this output. Decision-level: what does this mean for the affected party — human-readable.
Search and retrieve complete decision records by agent, date, outcome, customer, or regulatory criterion — on demand, in seconds.
Decision evidence packaged for regulatory submission — complete, consistent, and traceable without additional manual preparation.
See how Explainability & Decision Audit works within the Tantor governed intelligence platform.