AI
Aspire Multi-repo Rollout at Scale with Agentic AI
This is part 2 blog of the windows 365 integration journey with Aspire. This blog focus to show how Windows 365 scaled Aspire adoption with reliability patterns and an agentic AI rollout system across 50+ repos.
How we Decide Between Keyword and Hybrid Search: 5 Enterprise Evaluation Criteria
A data-driven framework we use in enterprise deployments to decide between vector-only keyword and hybrid search, based on five measurable evaluation criteria.
Introducing azure-functions-skills: An AI-Era Workspace for Azure Functions (Preview)
azure-functions-skills gives GitHub Copilot CLI, Claude Code, Codex CLI, and VS Code the skills, MCP configuration, hooks, and instructions needed to create, diagnose, deploy, and validate Azure Functions projects end-to-end.
Announcing the Public Preview of Integrated Embeddings in Azure Cosmos DB: Build AI Apps With Embeddings That Stay in Sync
AI applications built on Azure Cosmos DB depend on embeddings for grounded results. Keeping them in sync with your data is the hard part: it means building and operating a separate data pipeline to track changes, call an embedding model, and write the results back to Azure Cosmos DB. In practice, that pipeline also has to handle failures and retries, throttling, scaling, and monitoring as your data and traffic grow. Integrated Embeddings in Azure Cosmos DB, now in Public Preview, removes that heavy lifting. Azure Cosmos DB automatically generates and maintains the embeddings for you as items are written and upda...
Introducing OmniVec: An Open-Source Embedding Platform for AI Apps on Azure
Today we are open-sourcing OmniVec, a platform for building and operating the embedding pipelines that keep the vector representation of your operational data in sync as it changes. You register data sources, embedding model(s), vector stores (destination), and OmniVec does the rest: initial backfill, change tracking, model invocation to geenrate, and writing them back to your vector store. We are shipping this with support for Azure Cosmos DB, PostgreSQL, SQL Server (source and destination), and Azure Blob Storage (destination). You deploy OmniVec in your own Azure subscription, and use the web UI, CLI, or the ...
Public Preview: AI-powered Azure Cosmos DB Migration Assistant for RDBMS to NoSQL
Today, we are excited to announce the public preview of the Azure Cosmos DB Migration Assistant for RDBMS to NoSQL, now available in the Azure Cosmos DB extension for Visual Studio Code. 📈 Modernize with confidence Why migrate from RDBMS to Azure Cosmos DB? Modernizing relational workloads has traditionally been complex, time-consuming, and risky. This new AI-assisted, phase-based workflow replaces manual analysis with structured recommendations and helps you navigate key design choices such as denormalization, partitioning, and NoSQL data modeling. It helps you move from relational databases (SQL Se...
Azure Cosmos DB MCP Toolkit Is Now Generally Available — Bringing Your Database to AI Agents at Scale
Since we introduced the Azure Cosmos DB MCP Toolkit at Ignite 2025 in preview, the response has been clear: developers want a straightforward way to connect AI agents to their production databases. Customers asked for stability, broader embedding provider support, and a smoother path from experimentation to production. Today, we're announcing the general availability of the Azure Cosmos DB MCP Toolkit (v1.1.2), now with deeper Microsoft Foundry integration, multi-provider embedding support, and the reliability improvements you asked for. The Problem: Getting AI Agents to Talk to Your Data Is Harder Than I...
Announcing the Public Preview of Semantic Reranker in Azure Cosmos DB for NoSQL
Today we’re thrilled to announce the public preview of Semantic Reranker in Azure Cosmos DB for NoSQL,a new AI-powered capability that improves the relevancy of your search results with just a few lines of code. If you’ve ever run a vector, full-text, or hybrid search and wished the most relevant documents bubbled to the very top, this one’s for you. Semantic Reranker uses an AI model to score and reorder the results of any query based on how well each document matches the user’s intent. It’s built right into the Azure Cosmos DB SDKs (Python, .NET, and Java), so you can reorder results from any container with ...
New Toolkits for Agent Memories and Agentic Retrieval in Azure Cosmos DB
Today we’re thrilled to announce the public preview of two new toolkits for Azure Cosmos DB: the Agent Memory Toolkit and the Agentic Retrieval Toolkit. If you’re building AI agents and retrieval-augmented generation (RAG) apps, these toolkits are designed to take you from prototype to production faster, giving your agents durable memory and your RAG pipelines the ability to reason over evidence in multiple passes, all backed by the database you already know and love. Both toolkits build on the same foundation: Azure Cosmos DB for NoSQL as a unified store for documents, vectors, and full-text data, with vector...
From Intent to Insight: AI Meets Azure Cosmos DB in VS Code (Public Preview)
The Problem Every Developer Knows Too Well You're building a feature. You know exactly what data you need all orders from the last week over $500, grouped by region. The logic is clear in your head. But between you and that data sits a query language, a documentation tab (or three), and fifteen minutes of trial and error before you get the syntax right. Now multiply that across a team. New developers ramping up on Cosmos DB spend days learning query patterns. Senior developers’ context-switch between writing application logic and debugging queries. Product managers wait for data answers that a developer mus...
Azure Cosmos DB Agent Kit now battle tested for GA
Back in January, we shipped the Azure Cosmos DB Agent kit in preview with 45 rules and a hypothesis: if we package Azure Cosmos DB expertise into a format that AI coding agents understand, developers will stop making the same expensive mistakes. That hypothesis held up. What surprised us was how much the rules themselves needed to evolve once we started systematically testing them. Today the Agent Kit is generally available . It now contains 120+ rules across 12 categories. But the number that matters more: we've run over 200 automated test iterations where AI agents build real applications from scratch using ...
Announced at MS Build 2026: Azure Cosmos DB MCP Toolkit, Semantic Reranking, Global Secondary Indexes, and more!
Microsoft Build 2026 has officially started and we’re excited to announce new capabilities for Azure Cosmos DB! We’ve launched new features to help developers build AI-powered applications and agents more easily, improve application resilience, and accelerate developer productivity. These announcements span every stage of the development lifecycle, from local development with the Azure Cosmos DB Linux Emulator, to advanced retrieval and agent memory capabilities for AI applications, to enterprise-grade operational features such as Global Secondary Indexes, Per-Partition Automatic Failover, Distributed Transact...
Azure Translator: Improving Translation Quality with Adaptive Datasets and Few‑Shot Learning
Your healthcare app needs "La médica" not "El médico." Your legal documents need precise terminology, not generic translations. When domain-specific language matters, generic LLM translation falls short. Azure Translator's adaptive translation lets you teach the model your terminology with just a handful of examples—no model training required. In this walkthrough, you'll create an adaptive dataset, compare baseline vs. adapted translations side-by-side, and see exactly how much difference domain context makes. What you build The playground experience can help you evaluate several aspects of translation beh...
Azure Translator API Version 2026‑06‑06 (GA): Supporting Multilingual Applications with Flexible Translation Options
Ship multilingual features in hours, not months Your translation workflow probably looks like this: one vendor for real-time chat, another for document translation, a custom LLM integration for nuanced content, and a spreadsheet tracking which system handles what. Azure Translator's new API (version 2026-06-06, now GA) consolidates all of this—NMT speed, LLM fluency, and adaptive customization—into a single endpoint. Why It Matters: Managing Trade‑offs Translation approaches often involve trade-offs. NMT is commonly used for fast, real‑time scenarios, while LLM-based approaches may provide more fluent ...
Aspire 13.4 is here
Aspire 13.4 is here with TypeScript apphost GA, typed resource commands with results, server-side CLI log and telemetry search, more mature Kubernetes and AKS deployment, and more.
Improve your agentic developer tools by grounding in Microsoft Learn
Development workflows span terminals, IDEs, background agents, and custom assistants. What matters is whether they draw from the same current source. Learn MCP Server gives any MCP-compatible agent direct access to current Microsoft documentation - one endpoint, nothing to install, no authentication required. What does that look like in practice? You give your coding agent the prompt: "create a CLI script to deploy Azure AI Foundry." It reaches for , the Azure ML extension - the right answer a year ago. It hits a Python dependency crash, spends 15 tool calls debugging import paths, and produces a script target...
SQL + AI, hands-on: Join a free workshop near you
If you work with Microsoft SQL regularly, the AI conversation right now probably feels a little exhausting. Every week brings a new platform, a new pattern, a new opinion about how you’re “supposed” to build AI apps. Most of it assumes you’ll start over. You don’t have to. We’re running SQL AI App in a Day workshops with Microsoft partners around the world. They’re free, hands-on, and built for developers who want to add AI to the apps they already own, using the data they already trust. You can browse upcoming sessions and register. Build on Microsoft SQL, not around it The question that keeps coming...
How AI coding agents actually use your technology
You ship an SDK, a CLI, an API, and developers use it. Now AI coding agents use it too, except they use it differently than humans do. Most of the time you have no idea what's actually happening between "developer types a prompt" and "agent generates code with your technology." Is the agent reading your docs? Is it calling your MCP server? Is it ignoring both and guessing from memory? In the previous article, we introduced the AX stack: model, harness, and agent extensions. We talked about what's fixed and what you can influence. This time, let's trace through what actually happens, step by step, when an agent...
Doing More with GitHub Copilot as a .NET Developer
Want to get more out of your GitHub Copilot experience? Here are some easy ways to get started.
The AX stack: what’s fixed, where you can win
AI coding agents promise to make you more productive. On the surface they do, but in practice they fall short: agents generate code that doesn't compile, use a deprecated SDK, or pick the wrong service entirely. Is it you using it wrong? Is it your tech stack? Or is it the tools you haven't configured yet? The stack between a developer's prompt and the generated code has layers. Some of those layers are fixed: you can't change them no matter what you do. But there's one layer where you have all the leverage. And if you don't know which is which, you'll waste time optimizing the wrong thing without seeing any r...