Code Compass

Introduction An AI agent is a smart program that can think, make decisions, and do tasks. Sometimes it works alone, and sometimes it works with people or other agents. The main difference between an agent and a regular assistant is that agents can do things on their own. They don’t just help—you can give them a goal, and they’ll try to reach it. Every AI agent has three main parts: Model : This is the brain that helps the agent understand language and think. Instructions : These tell the agent what it should do and what rules to follow. Tools : These help the agent find information or take action. Agents can take input like a message or a prompt and respond with answers or actions.  For example, they might look something up or start a process based on what you asked. Azure AI Foundry is a platform that brings all these things together; so you can build, train, and manage AI agents easily. References What is Azure AI Foundry Agent Service? - Azure AI Foundry | Microsoft Learn Unders...

Introduction Model Context Protocol (MCP) is an open standard that makes it easier for applications to provide information to large language models (LLMs). You can think of MCP like a USB-C port for AI —just as USB-C offers a common way to connect devices, MCP provides a standard way to link AI models with different data sources and tools. MCP uses a client-server setup , where different parts work together to share data: MCP Hosts – Programs like Claude Desktop, IDEs, or AI tools that need access to data. MCP Clients – Connect directly to servers to request and send data. MCP Servers – Small programs that provide specific functions through MCP. For this proof of concept (PoC) , I used two key parts of MCP: Resources – A way for MCP servers to share data, which clients can read and use to give better responses. Tools – Functions that let servers do tasks, such as pulling data from other systems or running commands. I built both the MCP server and client inside Cursor , writing all...

Introduction Software development has been around since the 1940s. We started with punch cards, then machine language, followed by assembly, high-level programming languages, low code, no code, and now AI-assisted coding. Along the way, several tools have been developed to make programmers' jobs easier, from card sorters and verifiers to debuggers and IDEs. Now, with the advent of AI, we have large language models (LLMs) writing code for us, but I don't think it's quite there yet. In this article we'll see how AI assists developers, what it can do for us today, its limitations, and where it's headed. The concept of AI began in the 1950s when researchers tried to imbue machines with the magic to think. Early systems followed set rules, but as computers improved and data became more available, smarter methods emerged, such as machine learning, natural language processing, and neural networks. Large Language Models (LLMs) grew from these advances, using huge amounts of...