The Semantic Kernel tag gathers articles comparing its performance and async behavior to Python‑based AI libraries, and exploring how it enables agentic AI on .NET and Azure. Dissecting latency trade‑offs and async patterns, these posts guide choosing the right framework for low‑latency inference and scalable agent orchestration. The material assists engineers in balancing speed, scalability, and…
19 articles since Aug 2026
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 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 design, test, and operate MCP server versioning and backward compatibility at scale, with concrete patterns, CI/CD pipelines, and real‑world case studies.
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.
Explore practical tokenization and BPE techniques for .NET developers building LLM apps, with Azure OpenAI integration, performance tricks, and production‑ready patterns.
Compare Azure AI Foundry and Azure OpenAI Service on model management, agent orchestration, cost, security, and .NET integration—what you need to choose for production workloads.
Learn how .NET teams can slash LLM inference spend with KV‑cache tricks and smart batching. Real‑world code, Azure OpenAI tips, and production‑ready patterns.
Explore how MCP Server and Azure OpenAI function calling reshape .NET AI integrations, from latency and state handling to multi‑agent orchestration and cost optimization.
Explore the Semantic Kernel vs LangChain latency and throughput benchmarks with real‑world data, code snippets, and actionable guidance for senior engineers building agentic AI services.
Learn how to engineer advanced BPE tokenizer customization for domain‑specific corpora, integrate it into .NET LLM pipelines, and optimize performance for production workloads.
Learn how to instrument, monitor, and troubleshoot MCP server tool calls with OpenTelemetry, Azure Monitor, and Semantic Kernel for reliable, production‑grade observability.
Learn how to architect, implement, and operate a production‑grade agent harness in ASP.NET Core using Semantic Kernel, MCP, and Azure services – the five‑layer approach that scales.
Learn how to embed evaluator‑driven release gates into your .NET CI/CD flow, ensuring LLM updates meet accuracy, safety, and latency standards before they hit production.
Learn a practical decision framework for .NET teams to pick fine‑tuning, prompt engineering, or Retrieval Augmented Generation. Reduce risk, control cost, and hit latency targets.
Discover production‑ready patterns for Semantic Kernel in Python—prompt caching, context engineering, and hybrid integration with LangChain—to cut costs and boost reliability.
Explore the architectural trade‑offs, performance nuances, and .NET integration strategies of Semantic Kernel vs LangChain to make informed engineering decisions.