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Six acceptance tests every AI workflow should pass before going live

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A tutorial published on DEV Community outlines a structured method for testing AI workflows before deployment, arguing that a convincing demo is not the same as a production-ready system. The approach asks developers to define five fields upfront — workflow name, desired outcome, human approval point, side effect, and evidence reference — forming a testable contract. Six failure scenarios are then used to validate the workflow: malformed input handling, idempotency under duplicate events, dependency failure recording, enforcement of human approval gates, evidence-backed completion, and a passing happy-path run. The tutorial emphasizes that natural-language quality claims are insufficient and that each test must produce machine-readable, observable evidence rather than an optimistic status message. A browser-based tool called Acceptance Workbench is mentioned as a lightweight, no-login aid for exporting these contracts and validating run logs.

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Six acceptance tests every AI workflow should pass before going live · ShortSingh