HITL Exception Routing — Smart Escalation for AI Edge Cases

I will build an intelligent exception routing system for your AI workflow — automatically detecting when AI outputs fall below confidence thresholds, when inputs are out of distribution, or when business rules flag a decision for mandatory human review, and routing exceptions to the appropriate human reviewer with full context. Exception-based HITL is more efficient than uniform review because it concentrates human oversight where AI reliability is lowest — high-confidence, in-distribution cases flow through without review, while low-confidence and edge cases receive the human attention that their uncertainty warrants.

The system covers confidence threshold monitoring, out-of-distribution input detection, mandatory review rule configuration, smart routing to the appropriate reviewer based on exception type, context packaging for the reviewer, and exception volume tracking.