production-mlhuman-in-the-loopenterprise-aiworkflow-automation
48-Hour Notice AI Decision Streams: Systematic Human-in-the-Loop Patterns for Production Scale
8 min read · 1,562 wordsBy Orandi Felix
Build your consequence scoring as a separate, independently testable service. I've seen teams bake consequence logic into model pipelines and then spend weeks debugging why a router is behaving unpredictably. When consequence scoring is its own unit with its own test suite, you can audit and update it without touching the model layer.
One pattern I've found useful: run a quarterly "disagreement audit" — pull all cases where the human decision differed from the model recommendation, cluster them by feature patterns, and use those clusters to generate new training examples or update routing thresholds. Your reviewers are doing active learning. Capture it.