How to Build Traceable AI Workflows With Retry and DLQ Visibility

This article argues that many AI pipeline “bugs” are not failures but unobserved branching decisions. By treating extraction as a traceable workflow and recording each step as structured trace nodes, developers gain full visibility into inputs, outputs, retries, and branch choices. The result is a deterministic record that enables debugging, replayability, safer caching, and better system reliability.