The AI Architecture tag covers practical patterns and tooling for building, debugging, and scaling AI workloads in .NET and Azure environments. From comparing Semantic Kernel and LangChain async strategies to diagnosing latency in a multi‑provider orchestration stack, and finally outlining a senior engineer’s playbook for moving prototypes into production, it equips developers with actionable guid…
20 articles since Aug 2026
Learn how to design, scale, and monitor ML.NET inference pipelines in .NET Aspire using MCP, Azure OpenAI, Redis caching, and event‑driven patterns for production‑grade AI services.
Explore why Deploying Multi‑Agent RAG on Azure AI Foundry behaves differently in US and India regions, with concrete architecture, code, and production‑ready guidance.
Explore the architectural differences, extensibility patterns, and performance trade‑offs of Claude Agent SDK vs Semantic Kernel—essential reading for senior engineers building production AI agents.
Learn how to migrate a legacy ASP.NET Core REST service to a WebMCP‑backed AI endpoint. This .NET case study covers architecture, code, performance gains, and production pitfalls.
Learn how to build a reliable, scalable AI workflow using Kafka and .NET. This guide covers architecture, code, observability, and real‑world lessons for enterprise AI orchestration.
Learn how to generate, store, and query vector embeddings in .NET at scale. This guide covers model selection, FAISS/.NET integration, Azure Cognitive Search vector, and production‑grade patterns.
Learn how to architect, secure, and optimize a custom .NET MCP server for Claude, Azure OpenAI, and other LLMs. Real‑world code, performance tricks, and production pitfalls included.
Learn how to architect, secure, and scale Deploying Multi‑Agent RAG with Semantic Kernel on Azure AI Foundry for latency‑critical customer support across US and Indian cloud regions.
Learn how to instrument, store, and alert on LLM API usage costs in .NET. Real‑world patterns, code snippets, and production tips keep your AI spend under control.
Learn how to architect, secure, and scale Deploying Multi‑Agent RAG workflows with Semantic Kernel on Azure AI Foundry—complete with code, observability, and production‑ready checklists.
Learn how senior engineers can reliably deploy agentic AI workloads on Azure AI Foundry using Kubernetes, with concrete architecture, code, cost, and failure‑mode guidance.
Explore the real engineering trade‑offs between self‑attention and cross‑attention in .NET Retrieval‑Augmented Generation, with production‑ready patterns, profiling tips, and scaling guidance.
Learn how to engineer advanced BPE tokenizer customization for domain‑specific corpora, integrate it into .NET LLM pipelines, and optimize performance for production workloads.
Explore a production‑ready multi-tenant MCP server design that balances isolation, security, and Azure‑scale for modern SaaS platforms.
Explore the real‑world trade‑offs of Azure OpenAI Service vs GPT‑4 API for .NET microservices, with cost, performance, security, and production‑ready patterns.
Explore the architectural trade‑offs, performance nuances, and .NET integration strategies of Semantic Kernel vs LangChain to make informed engineering decisions.
In thirty-three minutes, MockEvalio went from calling Groq directly from five different services to a centralized orchestrator with versioned prompts and cost-aware multi-provider routing. What built on what, and what's never been touched since.
Discover the concrete architectural, reliability, and cost changes required when moving an AI system from a prototype to a production‑ready .NET service. Learn the hidden pitfalls, scaling patterns, and deployment best practices.