Workflow-Orchestration

TL;DR for operators An AI agent can select the correct function and still fail the overall workflow because an earlier result was lost, a prerequisite was skipped, or the wrong patient or treatment-plan identifier was passed downstream. RadOnc-Agent makes these risks measurable. In matched synthetic tests, preserving longitudinal workflow state increased completion from 84.00% to 96.50%. A separate validation experiment found that deterministic checks prevented all deliberately mismatched backend dispatches, whereas bypassing those checks allowed 95.28% to proceed through restricted replay/test interfaces. ...

A composite professional football club redesigns its between-match preparation cycle around six coordinated agents that retrieve, reconcile, and refresh evidence while coaches and clinicians retain all consequential decisions.

A metropolitan funeral home redesigns a fragile, coordinator-dependent case process around specialized AI agents while retaining human control over every family, legal, ceremonial, and religious decision.

An illustrative design explores how AI could structure receipt intake, approval routing, and draft accounting work while preserving human review.