"The classic approach to writing agile user stories starts with documenting who the end user is, the user's objective, and why it's important to them. You'll often see user stories follow the format, "As a user type, I want to be able to complete a task so that I can achieve a specific outcome." The product owner helps the development team understand when the user story is done by providing a list of pass-fail acceptance criteria."
"AI agents are multifaceted, including application, automation, data, API, and AI components, so there's undoubtedly a need to express their nonfunctional requirements. User stories for AI agents should have the following types of acceptance criteria: Functional requirements that focus on what the agent will do and where humans-in-the-middle will provide oversight. A set of nonfunctional requirements focusing on areas of performance, compliance, security, observability, and other operational requirements, just as they would for APIs and automations."
Agile user stories for AI agents should mirror traditional stories by specifying user role, objective, and acceptance criteria while adding technical and data-focused NFRs. Acceptance criteria must include functional requirements describing agent behaviors and human-in-the-middle oversight. Nonfunctional operational requirements should address performance, compliance, security, observability, and maintainability similar to APIs and automations. Data-specific NFRs must cover data quality, governance, bias mitigation, and model maintenance. Technical leads, architects, security specialists, and DevOps should contribute these NFRs to ensure deployable, auditable, and safe AI agent behavior. NFRs should be granular and guide incremental delivery of atomic capabilities.
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