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Modern AI Agents are becoming increasingly powerful, but their true potential is unlocked when they can interact with external systems. In my previous post, I discussed how to create MCP (Model Context Protocol) servers and the advantages of using MCP to connect AI models to external tools and data sources vs. traditional function callings. In this follow-up post, I will dive into using MCP tools with Azure AI Foundry Agents.

AI coding assistants have become an essential part of my daily workflow. Among the various many available, GitHub Copilot stands out as the most powerful and versatile. GitHub Copilot integrates with Visual Studio Code and other popular IDEs, providing context-aware suggestions that help me write code faster and smarter. In this post, I will share my personal workflow with GitHub Copilot — why it has become such a big productivity boost, and how it goes far beyond simple code completion to support many other tasks.

While tools extend GenAI application capabilities by allowing models to call external functions they are typically scoped to a specific model or framework. MCP solves this by providing a standard interface that simplifies integration across models and tools. In this post, we will see how easy it is to set up a local MCP server and interact with it directly from GitHub Copilot Chat in VS Code.

While a single agent can effectively handle tasks within a specific domain, it often falls short when dealing with scenarios that involve multiple data sources, actions, or decision points. In this post, I will explore the building blocks of multi-agent solutions and why they’re important for creating smarter, and more scalable AI workflows.

Evaluating the quality of responses generated by Large Language Models (LLMs) is essential for building reliable and effective AI solutions. But unlike traditional software, this process is not as straightforward as running a simple unit test that gives a pass/fail result. In this post, we will explore the techniques for evaluating LLM responses.