1. Introduction

Semantic Kernel (SK) gives .NET developers an orchestration layer for building intelligent agents—prompt templates, plugins, planning, memory and more. Microsoft.Extensions.AI (ME.AI) sits one level lower, supplying lightweight contracts and a middleware pipeline for talking to any large-language-model back-end. By standing on ME.AI, SK inherits the same design principles that turned ASP.NET Core, Entity Framework Core and the wider Microsoft.Extensions stack into reliable foundations: dependency injection as a first-class citizen, composable middleware and testability out of the box.

2. Layered mental model

If you imagine building a web API, SK is equivalent to the MVC layer (controllers, views, filters) whereas ME.AI resembles the underlying HTTP abstractions such as HttpClientFactory. The following ASCII sketch places each piece in context.

        ┌───────────────────────────────────────────┐
        │          Your Chatbot / Copilot           │
        └──────────────▲────────────────────────────┘
        │ Plans, plugins, vector-memory
        ┌──────────────┴────────────────────────────┐
        │            Semantic Kernel (SK)           │
        │  (prompts, planning, agents, memories)    │
        └──────────────▲────────────────────────────┘
        │ IChatClient, IEmbeddingGenerator
        ┌──────────────┴────────────────────────────┐
        │         Microsoft.Extensions.AI           │
        │ (abstractions, DI, middleware, logging)   │
        └──────────────▲────────────────────────────┘
        │ HTTP / gRPC / WebSockets
        ┌──────────────┴────────────────────────────┐
        │        OpenAI, Azure AI, Ollama…          │
        └───────────────────────────────────────────┘
        

3. Dependency injection fundamentals

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Introduction

In the rapidly evolving landscape of artificial intelligence, the integration of Microsoft's Semantic Kernel (SK) with the Model Context Protocol (MCP) represents a significant advancement in building intelligent, context-aware AI agents. This synergy enables developers to create agents that can seamlessly interact with diverse data sources and tools, enhancing their capabilities and adaptability.

Understanding Semantic Kernel

Semantic Kernel is an open-source SDK developed by Microsoft that facilitates the integration of AI services into applications. It provides a modular and extensible framework for building AI agents capable of performing complex tasks by combining natural language understanding with programmable functions. Key features include:

• Plugins: Modular components that encapsulate specific functionalities, enabling agents to perform a wide range of tasks.
• Memory: Allows agents to retain context over time, improving their ability to handle multi-turn conversations and complex workflows.
• Planning: Enables agents to decompose tasks into sub-tasks and execute them in a structured manner.
• Function Calling: Allows agents to invoke external functions or APIs based on user input or internal logic.

Introducing the Model Context Protocol (MCP)

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What is Semantic Kernel?

Semantic Kernel is a lightweight and modular open-source SDK from Microsoft that helps developers integrate large language models (LLMs) into their applications in a seamless and extensible way. By acting as a bridge between your application logic and the power of modern AI services like OpenAI and Azure OpenAI, it lets you build intelligent agents that understand context, plan actions, and execute real-world functions.

Why Use Semantic Kernel?

While many SDKs enable chat or text generation, Semantic Kernel goes further by offering a framework to define and organise your app's capabilities as plugins, which the AI can invoke. It’s designed with enterprise needs in mind—telemetry, logging, and modular design come built-in. It supports automatic function calling, multi-turn dialogue, and integration with tools like Microsoft 365 Copilot.

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Description

On my previous post I started to explain how to create a full CI/CD DevOps automated deployment cycle for your Smarterasp.net hosting websites defining the build pipeline implementation.

After creating a Build pipeline, we are going to finish it creating a release pipeline.

Requirements

You will need to fulfil some requirements first:

• Previous post regarding build pipeline already done.
• An SmarterAsp web hosting.

Steps to create a release pipeline

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Description

For personal, experimental, prototyping or small side projects lots of ASP.NET developers choose a cheap web hosting supplier like Smarterasp.net. If you are one of these developers using SmarterAsp.net for your projects nothing is stopping you to have a full CI/CD DevOps automated deployment cycle for your websites.

Traditionally the protocol to upload content to this kind of hosting plans is FTP or WebDeploy. Microsoft is offering a great DevOps free tool: Azure Devops. Setting it up is quite easy, just follow the steps after clicking [Start for free] button.

Once you have your Azure Devops project set, you will be able to configure and execute build and release pipelines for free targeting your Smarterasp.net web hosting plan.

Requirements

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