Semantic Kernel for Enterprise AI: Architecting Production-Grade LLM Integration in .NET

Semantic Kernel for Enterprise AI: Architecting Production-Grade LLM Integration in .NET — Implementation & Observability — Part 2 This is Part 2 of the series. Part 1 covered the foundational architecture of Semantic Kernel — plugins, planners, memory, and filters — along with the FinOps cost model and SRE failure taxonomy. In this part, we move from architecture to implementation: building the async-first parallel orchestration engine, the Redis-backed semantic cache, and the complete production filter pipeline with token metering. I. Recap and What This Part Covers Part 1 established that the gap between LLM demo and production system is architectural. Semantic Kernel closes that gap through four composable primitives — plugins, planners, memory, and filters — wrapped in a resilience and observability model that matches enterprise operational standards. Part 2 builds on that foundation with concrete, production-ready .NET 9.0 implementations of the three highest-leverage components