Agentic AI for Busy Product Managers
Everything you need to design AI agents that actually work in production: the design decisions your frameworks miss, how to build for a system that acts on its own, and what you owe the people it will affect. Free guide, no fluff.
A practitioner's guide for PMs shipping agentic AI in enterprise. Not a primer or a framework book; it's the artifacts I actually use in design and governance reviews: Consequence Classification, the When-Wrong Spec, Policy-as-Product, and a cost table that forces honesty about when an agent actually beats an RPA flow or a well-tooled human.
Core argument: AI governance is architecture, embedded in the product at runtime, not a committee artifact that lives in a slide deck. The chapter on the semantic backbone (knowledge graph plus hybrid retrieval) will feel familiar to anyone who's lived the enterprise data problem, but pushes on who owns it.
Physician-to-PM lens runs throughout, because healthcare has been running this experiment, under real stakes and regulation, for decades. Closing field manual is two pages and the highest-leverage part of the book.
