AI governance is not post-deployment oversight. Build data authority, identity, security, evidence and bounded agent controls into the architecture before production AI scales risk.
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Healthcare is letting machines write the evidence that future machines will trust. Without machine-readable provenance, AI-generated documentation becomes indistinguishable from direct observation.
Every EHR vendor now claims an AI strategy. The distinction that matters is architectural — and for most health systems, integration readiness, not vendor selection, decides whether the investment pays off.
Prepared as follow up to our previous article, The AI Agent Graveyard. We argue both sides of the agentic AI debate — the compounding-error case against it and the architectural case for it — then lay out the reference architecture, autonomy model, and governance controls that separate agents that die at month eighteen from agents that run the business.
Most healthcare AI pilots fail within 18 months — not from bad algorithms, but from missing governance, auditability, and deployment discipline. Inside the three silent killers and how to build AI that survives production.