  Intelligent services provide the power of AI to developers via simple RESTful
API endpoints, abstracting away many complexities of machine learning. However,
most of these intelligent services-such as computer vision-continually learn
with time. When the internals within the abstracted 'black box' become hidden
and evolve, pitfalls emerge in the robustness of applications that depend on
these evolving services. Without adapting the way developers plan and construct
projects reliant on intelligent services, significant gaps and risks result in
both project planning and development. Therefore, how can software engineers
best mitigate software evolution risk moving forward, thereby ensuring that
their own applications maintain quality? Our proposal is an architectural
tactic designed to improve intelligent service-dependent software robustness.
The tactic involves creating an application-specific benchmark dataset
baselined against an intelligent service, enabling evolutionary behaviour
changes to be mitigated. A technical evaluation of our implementation of this
architecture demonstrates how the tactic can identify 1,054 cases of
substantial confidence evolution and 2,461 cases of substantial changes to
response label sets using a dataset consisting of 331 images that evolve when
sent to a service.
