AI evaluation

How should an AI workflow be tested before release?

An AI workflow should be tested against the work it is meant to perform, the failures that matter, the policies it must respect, the tools it may use, and the escalation behavior required before release.

By Christopher Petrino | Published July 22, 2026

Test one workflow before release

Direct answer

An AI workflow should be tested against the work it is meant to perform, the failures that matter, the policies it must respect, the tools it may use, and the escalation behavior required before release.

A demo can look useful while the workflow still fails under realistic inputs, edge cases, policy constraints, tool boundaries, or regression after changes.

Practical framework

Use this as the decision model.

  1. Define the intended work and the release decision the test supports.
  2. Build realistic success cases from actual user or operator contexts.
  3. Build failure cases around sensitive data, missing evidence, ambiguity, and policy conflict.
  4. Test tool use, grounding, refusal, escalation, and human handoff behavior.
  5. Set release thresholds before reviewing results.
  6. Turn production failures into regression checks.

Examples

How the issue shows up.

A claims-like summary may pass routine cases while failing sensitive or ambiguous cases.

The decision turns on ownership, evidence, consequence, and the conditions for release.

A document workflow may use the wrong tool correctly formatted, which is still a release failure.

The decision turns on ownership, evidence, consequence, and the conditions for release.

Decision criteria

Questions that make the next action clearer.

  • Do the cases resemble real work rather than prompt demos?
  • Can failures be classified by consequence?
  • Would the evidence be credible enough for the person approving release?

Common errors

What to avoid.

  • Testing only happy paths.
  • Using a generic benchmark instead of workflow-specific cases.
  • Changing prompts or tools without rerunning regression checks.

Sources and related content

This article uses first-hand operating judgment.

This framework is based on Christopher Petrino's product, data, AI, and technology operating experience.

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