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Product Management for AI Features
Manage an AI feature as a changing workflow: define the job, test output quality, monitor failures, and make the person responsible for release and rollback explicit.
By Jake Bauman · Revised
Jake Bauman's product checklist, informed by provider and survey sources below.
Write a testable requirement
Specify the user, the input, acceptable outputs, unacceptable outputs, and the cost of an error. Include examples from real use with permission or clearly labelled synthetic examples.
Run the quality loop
- Build an evaluation set before launch.
- Review false positives and false negatives.
- Track quality after a model or tool change.
- Provide a human path for consequential decisions.
Release is a decision
A successful build or benchmark is not a release criterion on its own. The product owner should document who reviewed results and what would trigger a rollback.
Questions after launch
When an agent is live, ask which failure cases appeared outside the test set, whether people overrode its output, and whether the intended task changed. Review the same questions after a model update or a new data source. A product requirement should include how the team will recognize drift and who can pause the workflow.
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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