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.
Your dashboards are green. Your agent approved 17 wrong purchase orders overnight. Traditional O&M answers "is it running?" Agentic O&M must answer "is it behaving correctly?" These are different questions. They require different instruments.
Your agent passed every security check. The tools your team used were built for a different system. The frameworks that cover agentic AI are months old, the enterprise adoption cycle is 12 to 18 months long, and the models getting better at finding your gaps ship faster than your procurement cycle.
LLMs are getting faster, cheaper, and better every week. The comparison charts prove it. They also prove nothing about who wins. I've watched six technology wars end. The best product lost every time. The winner owned the right layer of the stack.
Utah just became the first US state to let an AI autonomously renew prescriptions. The legal debate is real. The design question nobody is asking is more important: who designed the rungs on the autonomy ladder before the climb began?
Most PMs raising agentic AI concerns are on the wrong side of the room. The question is not whether to speak up. It is what to put on the table. Four moves that trade gut-feeling objections for artifacts, unit economics, and the question that aligns the whole room.
Most agentic AI failures happen before a line of code is written. The use case was wrong. The cost model was missing. Two frameworks most teams skip, and pay for later.
Your AI worked in the lab. Your users bypassed it in the real world. The gap isn’t the model, it’s the environment. Without the right incentives, workflows, and accountability, even the best-designed AI gets reduced to output generation. The system around it determines everything.
A developer, an accountant, a graphic designer, a film director, a composer, and a product manager all use the exact same interface to communicate with AI: a text box. That has never been true of mature technology. It will not stay true for this one.
The people most affected by your AI product are almost never in the room when you build it. They don't appear in your analytics. They weren't in the meeting when the roadmap was approved. They will never give you feedback. You owe them something anyway
AI compresses knowledge. It does not compress experience. The junior PM in your next interview has access to the same tools you do. What separates you now is judgment. There is no course for that. There never was.
Healthcare AI job postings keep asking for unicorns: 14 years of experience in a field that is three years old, deep AI expertise, deep clinical expertise, deep product expertise. That person does not exist. And even if they did, they would not fix the real problem.