Test an AI workflow on representative normal, edge and failure cases. Compare it with the current baseline, record quality and exception rates, include the cost of human review and expand scope only when the evidence supports it.
Choose the smallest sufficient AI layer
Is the task one output, a repeatable sequence or an open-ended objective?
Separates a prompt fix from workflow design and agent use.
What happens when a plausible output or action is wrong?
Sets the required review and human-control boundary.
Must the system read from tools or change external state?
Tests whether agent machinery is necessary at all.
Can a named person inspect success before the result matters?
Separates bounded experimentation from irresponsible delegation.
| Layer | Best fit | Next move |
|---|---|---|
| Test | Representative cases and a baseline | Measure quality time exceptions and review cost |
| Prompt | One inspectable output | Clarify context, constraints and acceptance criteria |
| Workflow | Repeated work with known checkpoints | Measure normal cases, exceptions and review cost |
| Agent | Variable steps with necessary tool use | Bound permissions, budget, logs and approvals |
A cheap evidence test
Write down the present human process and its baseline: time, quality, common exceptions and consequence of error. Run the smallest AI-assisted version on representative cases without letting it make a material external change.
Compare the result with the baseline. Include correction time and review effort rather than counting generation speed alone. Expand only the part that produced repeatable evidence; keep the rest human-led.
Frequently asked questions
Test an AI workflow on representative normal, edge and failure cases. Compare it with the current baseline, record quality and exception rates, include the cost of human review and expand scope only when the evidence supports it.
Usually not. Start with the smallest layer that can prove value: a prompt, then a reviewed workflow, then a bounded agent only if variable tool use is genuinely necessary.
Use representative test results, exception rates, correction time, human review cost and the consequence of failure rather than a polished demonstration.
Clarify the right layer
The short interview uses task shape, consequence, tools, review and evidence to distinguish a prompt fix, workflow, bounded agent or human-led stop.
Source: Narrative Studio Applied AI batch; evidence links support definitions and control boundaries.