When AI is your manager: How we integrated an AI agent into our team

AI has learned to turn requirements, meeting notes, and documentation into a ready-to-use backlog complete with epics, acceptance criteria, and estimates. Task creation has sped up dramatically, making tasks look uniform and seemingly more complete. The catch is that "seemingly complete" and "correct" are not the same thing.Using a real-world project as an example, I will show where AI-generated tasks actually help QA and where they slip in made-up requirements, miss business constraints, and mask ambiguities behind confident phrasing.

In this talk: A checklist for verifying AI-generated tasks. A ready-to-use prompt that QAs can use to double-check task descriptions with that same AI. A breakdown of why QA now has to test three levels instead of one: the task against the source material, the acceptance criteria against business risks, and only then the actual implementation against the task.