What is AI decisioning?
AI decisioning is the architectural pattern that makes agentic AI safe for regulated workflows. The output is a structured decision — outcome, rationale, confidence, citations — instead of free-form text. Every primitive in Vihaya's engine is in service of one outcome: every decision is auditable, escalatable, and integrable.
The decision row
In Vihaya, every agent run produces one row in the decisions table. The row captures: outcome (approve / deny / escalate / partial), rationale (1–3 sentences), confidence (0–1), citation chunk IDs (pointers to the policy text the agent grounded on), the agent identity that produced it, and the run ID that links to the full audit trail.
The row is append-only. It cannot be updated — only superseded by a later decision with a link to the prior. This is what makes the decision trail reconstructable from cold storage years later.
AI decisioning FAQ
What is AI decisioning?
AI decisioning is a production AI pattern where the model's output is a structured decision — typically {outcome, rationale, confidence, citations} — not free-form text. The structure is what makes the output auditable, escalatable, and integrable with downstream systems.
How is decisioning different from chat?
Chat returns prose. Decisioning returns a typed object that a downstream system can act on without parsing English. A claim adjudication system can read 'outcome=approve' and route to payment; it cannot reliably read 'sounds like an approve to me'.
What's a confidence score for?
It's the model's self-reported uncertainty. Paired with a configured threshold (the confidence floor), it lets the system force escalation to humans below the floor — regardless of what the model's verdict was.
Why are citations critical?
Citations make the decision reviewable. A human reviewer or a regulator picking up a decision years later needs to see what the agent grounded on. Without citations, the audit trail records that a decision was made but not why.
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