This video explains how to build front-door model routing for AI agents using Jev. It covers creating model lanes with descriptive criteria, routing on confidence thresholds, handling multi-query task profiling, choosing between sticky and turn-by-turn routing, and establishing fallback mechanisms when network calls fail.
Original by AI News Today | Julian Goldie PodcastAgent workflowsIntermediate12 min 37 secPublished
Define routing lanes using specific descriptive criteria sentences with concrete examples rather than brittle if-then logic.
Evaluate multiple questions simultaneously in one call to build a safety profile alongside the model selection decision.
Implement fail-safe fallbacks to a default model and recognize that schema adherence does not guarantee correct routing judgment.
Worth knowing
Guaranteed output schema adherence does not guarantee correct routing decisions, and confidence calibration applies across aggregate batches rather than individual predictions.