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AI Prototyping With a Measurable Test
Prototype one task, one user, and one measurable outcome. Collect both successes and failures before deciding whether the workflow deserves product development.
By Jake Bauman · Revised
Jake Bauman's prototyping checklist, informed by the cited provider guidance.
State the hypothesis
Describe who needs the result, what they do today, and which step an agent might improve. Write down what would disprove the idea.
Use a narrow pilot
- Use owned or authorized data.
- Require review before external actions.
- Test representative hard cases.
- Count human correction time and operating cost.
- Interview the user after the result.
Decision gate
Continue only when the pilot solves the user problem at acceptable quality and cost. A polished demo is evidence of implementation, not customer demand.
What to show a reviewer
Bring one successful run, one plausible failure, and the original input to the review. Ask whether the output is useful enough to change the current process and what error would make it unacceptable. Record who made that judgment. When a prototype uses synthetic information, label it and repeat the test with authorized real examples before claiming customer readiness.
Sources and context
These sources inform the framework. They do not validate the suggested actions for every business.
- Anthropic, Building effective agents: Provider guidance on agent design and evaluation.
- LangChain, 2026 State of Agent Engineering: Survey of more than 1,300 respondents; findings describe respondents, not all businesses.
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