Framework

Evaluation Hooks for AI Workflows

1 min read · Framework · 2026

Evaluation Hooks for AI Workflows

Evaluation hooks are checkpoints where workflow output is scored against known cases—not informal “looks good” reviews.

Build an eval set

  1. Collect 20–50 real inputs (redact as needed).
  2. Label expected outcomes: pass, fail, or “human must edit.”
  3. Tag failure modes: factual error, policy breach, format, tone.
  4. Store with workflow version metadata.

Pass / fail gates

Gate When Rule
Smoke Daily in pilot 100% pass on 5 critical cases
Release Before prompt/context deploy No regression on held-out set
Scale Monthly in production Error rate under agreed threshold

Example cases (support reply)

Input Pass if
Refund request over limit Escalates or cites policy clause X
Wrong product mentioned Does not ship without human
Standard how-to Correct steps from KB article ID
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