Why one developer builds audit trails into AI workflows before trusting automation

A developer writing for DEV Community argues that observability — not prompt quality or output — should be the first thing engineers trust in AI workflows. They propose a structured logging contract that captures key fields such as workflow name, execution surface, risk level, human approval status, decision made, and estimated cost. The approach addresses common operational failures like invisible cost growth, scattered logs, and the inability to trace how context led to an automated action. A key insight is that human review outcomes — whether a suggestion was approved, edited, or rejected — are the most valuable signals for turning logs into an evaluation loop. The author also recommends routing trace payloads through an internal bridge rather than embedding observability credentials directly in workflow files, to reduce secret sprawl.