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The paper analyzes how PMs should adapt by engaging AI agents across four stages—goal definition, training supervision, output evaluation, and continuous feedback iteration. 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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 demonstrateshow PM feedback can directly influence the accuracy of outputs, agent maturity, alignment with company vision, and responsible outcomes.\u003Cbr>KEYWORDS: Product Management (PM), Artificial Intelligence (AI), AI Agents, Human-in-the-Loop (HITL) Systems, Agile Workflows, Agent Training, Ethical Governance\u003Cbr>I. INTRODUCTION\u003Cbr>Artificial intelligence refers to the capability of computational systems to perform complex tasks that are usually associated with human intelligence and are done by human reasoning, logic, and decision-making [1] . Taking a step further, AI agents are entities or intelligent systems that can perform many tasks without human intervention. It perceives the environment, takes actions, and can autonomously achieve goals while learning and improving itself using new data and machine learning [2]. The role of product management requires decision-making through human reasoning and data analysis, both of which are apt for takeover by artificial intelligence. Still, AI agents are transforming the responsibilities of Product Managers instead of obliterating them. The primary changes triggered by AI agents are the automation of repetitive and manual processes like data analysis or roadmap planning so product managers can focus more on highvalue activities [3], increase in focus on strategic thinking and visionary work because AI handles the operational workload, opportunity to be more data-oriented with AI results relying on high-quality and domain-specific data as well as the development of new skills and competencies such as systems thinking, AI concepts, and even ethical governance. This is becoming increasingly important because of how quickly AI agents such as Copilot, Devin, or AutoGPT are being developed and deployed, bringing a paradigm shift in how software is being ideated, designed, built, and iterated. These agents are not passive anymore; they actively make decisions, take actions, and generate reports. With organizations integrating such agents into their development pipelines, there is an increased need for human guidance, added guardrails, ethical oversight, and contextual grounding. Product Managers, because of their deep understanding of customer needs, domain knowledge, business priorities, and software development workflows, are the perfect candidates and are uniquely positioned to train, prompt, and provide oversight to AI agents. The involvement of product managers ensures the outputs are aligned with customer expect","cbCaivrzazTNoff7","https://ap.wps.com/l/cbCaivrzazTNoff7","pdf",1526205,7,"English","# Abstract\n# I. Introduction\n# II. Related Work\n## 2.1 AI Agents in SDLC","[{\"question\":\"How do AI agents affect day-to-day product management tasks?\",\"answer\":\"AI agents increasingly automate repetitive and manual tasks such as user story creation, sprint planning, and backlog grooming. This reduces operational workload so PMs can focus more on high-value strategic activities.\"},{\"question\":\"What framework does the paper propose for PMs working with AI agents?\",\"answer\":\"The paper describes four stages: goal definition, training supervision, output evaluation, and continuous feedback iteration. This structure supports accuracy improvement and ethical control.\"},{\"question\":\"Why is human guidance necessary when AI agents become autonomous?\",\"answer\":\"Autonomous agents introduce risks around output quality, alignment with team goals, and user-centric design. Human oversight adds guardrails, contextual grounding, and ethical governance while ensuring outputs meet customer and business needs.\"}]","AI in Product Management: How Product Managers Can Guide and Train AI Agents - Journal Article | PDF",1789344816]