1. What is the Agent‑to‑Agent (A2A) protocol?

Originating from Google’s open specification, A2A defines a lean JSON‑RPC envelope that lets autonomous agents share intents, stream partial results and report progress in real time. By speaking A2A, you can mix and match agents written in any language or framework without inventing yet another bespoke API.

2. Why pair A2A with Microsoft Semantic Kernel?

Semantic Kernel (SK) gives C# developers a first‑class toolkit for wrapping LLM prompts as skills, composing them into pipelines and injecting traditional .NET code where it still shines. When you bolt A2A on top, each SK instance becomes a portable micro‑agent whose skills are instantly discoverable and callable by its peers.

3. Why discovery matters

(...)
[Continue Reading]

1. Why wire AI agents straight into Azure DevOps?

Manual DevOps and CI/CD hand-offs slow down modern teams. By exposing Azure DevOps through the open Model Context Protocol (MCP), your LLM agents can raise work items, queue builds or approve releases on your behalf—no brittle REST glue or browser automation required.

2. What the server gives you today

The repository contains thin .NET libraries for every Azure DevOps “hub”, all surfaced as discoverable MCP tools:

• Boards – create Epics, User Stories and Tasks
• Repos – open PRs, add reviewers, merge or abandon
• Pipelines – queue, cancel, retry and fetch logs
• Artifacts – publish or yank packages
• Test Plans – manage plans, suites and cases
• Wiki – create pages or entire wikis

(...)
[Continue Reading]

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

(...)
[Continue Reading]

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)

(...)
[Continue Reading]

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.

(...)
[Continue Reading]