Hospital operations run on a web of concurrent signals. Theatre lists change throughout the day. PACU bays fill and empty. Sterile tray queues build up. Discharge blockers cascade into bed shortages. None of these individually defines a risk (it's the combination that matters), and the window to act is often under an hour.
A traditional response is a coordinator checking spreadsheets, chasing phone calls, and making judgement calls with incomplete information. A common response to using AI for this kind of scenario, would be to route the problem through a chat assistant and hope the prompt captures enough context. In this kind of operational workflow, that is not enough on its own: the system needs an audit trail, grounding in historical outcomes, and a clear boundary between what it can decide autonomously and what needs a human to approve.
I wanted to see if a different approach was feasible:
One where AI agents can produce evidence-backed recommendations grounded in historical patterns, high-impact actions always require human approval, every decision is recorded for audit and replay, and the detection logic is deterministic and testable (not buried in a prompt).
This post covers the Proof of Technology I built to validate an Agentic Operations Lakehouse style pattern _(and to be frank, it was a good chance for some fun, tieing these technologies together).
