Create a Test Set Table for Triage and Routing Outcomes
Explore how to build and use a test set table to evaluate triage and routing outcomes in AI workflows. Learn to define precise expected outputs, implement rerun protocols, and cover key failure modes to prevent silent drifts and maintain workflow reliability.
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Every workflow built so far has been checked one run at a time; trace one email, fix one failure, confirm one fix worked. This chapter turns to a different question: how do we know the workflow still behaves correctly after something changes? A support inbox workflow can look stable while it quietly drifts. A small prompt tweak can flip one message from escalate to reply without any errors, because the output is still valid JSON but the route is wrong. This lesson builds the smallest useful defense against that: a coverage scheme that targets specific failure modes, strict expected-output rules that make pass/fail unambiguous, and a rerun protocol that turns any future change into a checkable comparison.
A compact test set table prevents that drift by pinning each representative input message to an expected triage shape and an expected route, so reruns reveal changes.
The row stays stable when it asserts category, priority, next_action, and needs_human, and it stays flexible when it treats any human-facing draft or summary as non-binding.