The conversation around AI in product management often focuses on our own productivity—using AI to write user stories, summarize research, or analyze data. While useful, this misses a more fundamental shift in the products we are building. We need to ask ourselves: are we building AI features, or are we building AI agents?
An AI feature enhances an existing user workflow, like a spell checker or a recommendation engine. It’s a tool the user actively wields. An AI agent, however, is designed to take over an entire workflow autonomously, acting on the user’s behalf. Think of an agent that not only identifies sales leads but also drafts and sends personalized outreach emails.
This distinction is crucial because it changes our entire product development process. How do you roadmap for an agent where the goal is increasing autonomy, not just shipping sequential features? How do you conduct user research when the core user need is trust and delegation, not just task completion? The success metrics, the MVP scope, and the user relationship all fundamentally change.
How is this ‘feature vs. agent’ distinction changing your approach to roadmapping and defining success for AI-powered products?