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

This article is Part 2 of a series on Semantic Kernel for enterprise AI in .NET, focusing on three production-ready implementations: async-first parallel orchestration to eliminate sequential LLM call anti-patterns, a Redis-backed semantic cache achieving 30–60% token cost reduction, and a complete filter pipeline covering rate limiting, caching, audit logging, and token metering. It demonstrates how to move from architecture to concrete, observable, production-grade LLM integration.