AI in Product Management: How Product Managers Can Guide and Train AI Agents

With the advent of artificial intelligence, repetitive jobs and standard processes are being automated, and one of the roles impacted is product management. Daily tasks of a product manager, such as creating user stories, sprint planning, or backlog grooming, are now performed by AI agents in many organizations. As these agents increasingly participate in autonomous decision-making and task execution, the responsibilities of a Product Manager are expanding beyond the traditional roadmap planning and more towards training, supervision, and ethical oversight of the autonomous systems. This paper explores how product managers need to adapt to the new role and provides a framework for how PMs can engage with AI agents to improve the accuracy of the outcomes and maintain ethical standards. This is done across four stages: goal definition, training supervision, output evaluation, and continuous feedback iteration. Using principles from Human-in-the-Loop systems and general improvements in agent-based systems, this paper demonstrates how PM feedback can directly influence the accuracy of outputs, agent maturity, alignment with company vision, and responsible outcomes.