ai governance for agentic workflows in regulated environments. what actually works in production?

mapping out the production architecture for an ai agent system in a heavily regulated environment (compliance-heavy, structured reporting requirements). the agent operates in a high-stakes workflow, so every automated suggestion or flag needs manual expert verification to stay compliant. the problem is false positives. even a moderate false-positive rate adds cognitive load instead of removing it, and users start reflexively overriding or dismissing findings without reading them. we're debating whether to surface raw confidence scores or go further - saliency maps, logic logs streamed into the viewport. raw scores feel insufficient, but anything more complex risks becoming another thing users ignore. what do you think?