Azure, AI, Microsoft, and Everything In Between
Most AI applications today use RAG (Retrieval-Augmented Generation) to work with private data. The typical approach? Convert text into vectors and search for similar content. It works well, but it has a big limitation: it can’t see how different pieces of information connect to each other. Think about it like this – traditional RAG (sometimes … Read more
Background In the last month, Microsoft released the MSSQL MCP Server (Preview), a bridge that lets AI applications talk to SQL Server using Model Context Protocol. The announcement (see official post) sparked my curiosity, I wanted to test-drive natural-language querying against my own databases. While kicking the tires I discovered a limitation: the preview Node.js … Read more
For organizations leveraging OpenAI’s Agent SDK, one of the most important metrics to track is token usage. Tokens are the fundamental units of text processing in large language models, and they directly impact both performance and cost. This post explores how to track token usage in applications built with the OpenAI Agent SDK discusses the … Read more
Agents are everywhere these days, and yes it’s the new buzzword! Everyone is trying to build one, either to explore what’s possible or to solve real business problems. And with so many models and frameworks out there, it can get overwhelming. The OpenAI Agent SDK makes it easier to build agents, but not many developers … Read more
When I first started exploring AI-driven applications and experimenting with different emerging agent frameworks, one basic question consistently puzzled me: If we can implement the entire domain using a single agent, why complicate things by creating multiple agents to achieve same result? What additional value do multi-agent architectures offer? In this post, I’ll share insights … Read more