AI Architecture Layering

Layering AI capabilities requires defining strict boundaries between the AI Engine and AI Apps to ensure clear ownership. This approach enforces architectural standards by guiding capability placement and cross-team handoffs.

AI architecture handoff rules are boundary definitions that trigger delegation to specialized skills like ai-teams-expert or ai-service-expert. You need them during cross-team collaboration when a capability exceeds the current architectural layer's scope.

Yes, you can enforce AI governance across multiple teams by applying these layering standards. It guides architecture decisions for cross-team collaboration scenarios by defining boundaries and explicitly excluding specific team implementation details.

The difference between AI Engine and AI Apps layers is functional ownership: the engine layer provides core capabilities while the apps layer implements specific applications. Architecture standards define this boundary to prevent capability overlap.

No, this architecture layering approach explicitly excludes implementation details for specific teams. It focuses solely on capability placement decisions, boundary definitions, and governance rules across the AI Engine and AI Apps layers.