A combination of real world experience and new research initiatives will open up the universe of prognostic and diagnostic algorithms that can be created in the future. This presents the challenge of creating a system architecture that enables effective support of an infinite set of future algorithms even before they have been conceived, designed, implemented, tested, and approved for use. The Arbor architecture enables five critical elements to meet this challenge: (1) clean integration between legacy and new software, (2) remote, over the air provisioning of algorithms, (3) flexible data structures capable of evolving, (4) control points for the algorithm to report findings to in-vehicle occupants, and (4) a data collection strategy for failure incident reporting. Many algorithms are impossible to develop until we collect real world performance and failure information from on the vehicle. The Arbor system collects this information and feeds it off-board for analysis. Researchers analyze the data and develop diagnostic or prognostic algorithms that can then be deployed to a single vehicle experiencing odd behaviors or to an entire fleet, preemptively. A prognostic algorithm written, or modified, as an Arbor application can define its own outputs, which are then visible to the vehicle operator. These same outputs can be broadcast to a service technician with a diagnostic scan tool or to a remote operational command site, contingent on available communications links. Effective deployment of prognostic algorithms enables costly failures to be predicted ahead of time, thereby improving safety, reducing costs, and minimizing down time for equipment in order to effect more efficient fleet operations.