Validation Engine
Every agent output is checked before it becomes an action.
A deterministic verification layer that sits between model output and the systems of record, rejecting anything it cannot prove.
Why it exists
Language models are probabilistic by construction. In reconciliation, claims adjudication, or network remediation, a plausible-but-wrong answer is worse than no answer at all. Accuracy in production does not come from a better model, it comes from refusing to act on outputs that cannot be independently verified.
How it works
Claim extraction
Every agent response is decomposed into discrete, checkable claims: values, references, entities, and the proposed action. Free-form prose is never passed downstream as-is.
Independent verification
Each claim is checked against a source that is not the model, a database row, an API response, a document span, a business rule, or a deterministic calculation. Verification never asks the model whether it was right.
Adjudication and escalation
Claims that verify proceed. Claims that fail are either repaired through a bounded retry with the failing evidence attached, or escalated to a human with the exact reason for the failure. Silent pass-through is not a possible outcome.
Confidence as a gate, not a label
Thresholds are set per workflow and per blast radius. A read-only summary and a payment release do not share an acceptance bar.
What it gives you
- Claim-level decomposition of every agent output
- Source-of-truth checks against systems, documents, and rules
- Bounded self-repair with failure evidence in context
- Per-workflow confidence thresholds tied to blast radius
- Deterministic human escalation with machine-readable reasons