I’m evaluating a third-party Swift-based “orchestration layer” for enterprise AI workloads on Apple Silicon — it claims to handle job queuing, scheduling, batching, memory management, monitoring, auditing, and failover on top of on-device inference. Given the Core AI framework’s device-specialization step and InferenceFunction pipeline (and MLX’s unified-memory model), how much of this kind of orchestration is already handled natively versus something a developer would still need to build themselves? Specifically: 1. Does Core AI’s inference pipeline provide any built-in job queuing/batching across multiple concurrent requests, or is that entirely app-side? 2. Is there native failover/monitoring tooling for on-device inference, or would a developer need to build that themselves (e.g., via os_log, MetricKit, custom retry logic)? 3. For memory management across CPU/GPU/ANE, does unified memory in MLX/Core AI eliminate most of the manual management a custom orchestration layer would otherwise need to solve? Trying to understand what’s genuinely differentiated in a third-party layer versus what Apple’s stack already provides out of the box. Appreciate any insight from folks who’ve built with Core AI/MLX in production.