Amazon owns the patient at the moment of health decision intent. OpenEvidence owns the physician at the moment of prescribing intent. Both are monetized by the same pharmaceutical industry. The prescription is the handshake between them.
AI was trained on what physicians write. Not on how they reason. The note comes hours after the decision, shaped by billing codes and fatigue. What actually saved the patient was never recorded.
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.
Microsoft just analyzed 500,000 Copilot health conversations. The paper is honest: they observed questions but not outcomes. The physician in me cannot stop thinking about it. In medicine we audit what happened to the patient, not just what we considered. Why should AI be different?
A patient record can be comprehensive and clinically wrong at the same time. The AI reasons from what it receives. The failure is upstream, in the data, not in the model. That is the hallucination type nobody is naming.
Healthcare regulation still assumes a clinician is watching the AI before anything happens. That assumption worked when AI meant a score on a screen. It breaks down when AI runs continuously in a patient's pocket, shaping decisions no one reviews.
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.
Designing agentic systems is like teaching a class of toddlers to behave. You set the rules. You arrange the room. But you cannot test every move. The design problem changed. Most enterprise teams haven't noticed yet.
Your team ran evals. Every test passed. The results look like the QA matrix you've signed off on a hundred times. They're not. Here's what AI evals actually catch, and where they go silent.
After launch, trust builds naturally and supervision erodes naturally. If the product wasn't designed to hold oversight stable, the agent ends up at an autonomy level nobody authorized. You can't measure what you didn't design.
Most change management programs treat agentic AI as an adoption problem. The diagnosis is wrong. Change management is being treated as adoption when the real work is supervision. That is a different skill, and most teams are not designing for it.
Your agent shipped. Your team moved on. Silent degradation is what happens next, and nobody is watching for it. The most-deployed clinical AI in American hospitals ran at half its advertised accuracy for six years before anyone audited it. Three disciplines before the next ship.