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Approval flows work when reviewers get concise context, not raw transcripts.
Governance · 2026/5/19 · 6 min read
Human-in-the-loop systems stall when reviewers receive low-signal context. Build approval packets with intent, key evidence, confidence signals, and suggested action.
Good packets include links to sources and prior attempts. Reviewers can resolve exceptions quickly without rebuilding the task mentally.
Store reviewer outcomes as structured feedback. This data improves triage, model routing, and policy tuning over time.
AI economics depends on adoption curves, reviewer load, and workflow coverage, not just automation potential in a spreadsheet.
AI economicsAgentic workflows need unit economics that include model spend, tool calls, review effort, exception handling, and avoided operating cost.
Platform operationsRuntime incidents need triage paths that distinguish provider outage, quality regression, policy breach, tool failure, and cost runaway.
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