AI
How Uno Platform uses .NET, MCP, and AI to build high quality apps
How Uno Platform built two Model Context Protocol servers in C# so AI agents can ground themselves in real documentation and then see and drive a running cross-platform .NET app.
New Azure SQL Foundations video series with GitHub samples
Bob Ward and I recently released a series of videos, read more in the original blog post or go directly to the series on YouTube. The Azure SQL Database Foundations series are four videos that take you from your first Hyperscale database to AI features running against your own operational data. We also included how to assess and migrate (with AI and skills!) to Hyperscale in the first place, and the common optimizations you should consider. Every episode ships with a repo, so you can follow along in your own environment instead of watching someone else’s terminal.
From dotnet run to Foundry Hosted Agent in 3 lines of C#
You built an agent with Microsoft Agent Framework and it works great on your machine. Now what? Turns out deploying it to production takes 1 NuGet package, 3 lines of C#, and 2 commands. Let's do it.
Outgrowing SQL Server Express? Upgrade to Azure SQL Database Free Tier in 3 Steps
If you’ve been building local prototypes, internal tools, web backends, or lightweight services on SQL Server Express, you know the routine: it’s free, familiar, and gets the job done – until you hit hardware limits or spend weekends manual backup scripts and handling OS patches. Learn how to upgrade from SQL Server Express to Azure SQL Database free tier in 3 simple steps. Whether you're running SQL Server 2022 (or older) and hitting the hard 10 GB storage wall, or using SQL Server 2025 and realizing 50 GB of disk space doesn't solve the strict 1.4 GB memory bottleneck, staying on Express limits your applica...
Azure Cosmos DB in the Agentic Era: Data Tools for Developers and AI Agents
Anthropic's 2026 Agentic Coding Trends Report captures an important tension in how developers use AI. Engineers report using AI in roughly 60% of their work, yet say they can fully delegate only 0–20% of their tasks. AI is becoming a constant collaborator, but it still needs setup, supervision, validation, and human judgment. That gap is especially relevant when an agent starts working with a database. Database requests often sound easy. “Show me the failed orders from last week.” “Why is this query expensive?” “Does this container have the field I need?” For a developer, answering those question...
From single call to agents: five new Claude capabilities now available in Microsoft Foundry
Structured outputs, web search, web fetch, MCP connector, and tool search are now available for Claude models hosted on Azure in Microsoft Foundry, turning a model endpoint into a production agent platform.
Beyond Vector Indexes: Azure SQL Brings Optimizer Intelligence to Vector Search
Why production AI retrieval depends on more than vector index performance. It's easy to think of AI retrieval as a vector search problem. For many developers, the first conversation starts with vector index benchmarks: How many queries per second can it handle? What's the latency? What's the recall? How well does it scale? Those metrics are critical, but production AI retrieval rarely consists of vector similarity alone. Equally important is what happens when vector search becomes part of a real application workload, one that includes filters, joins, security policies, ranking logic, and live operational d...
Routing and Failover for Microsoft.Extensions.AI
Route requests across models and providers natively in Microsoft.Extensions.AI with RoutingChatClient, SemanticRoutingChatClient, and FailoverChatClient — new experimental primitives for content-based routing, failover, and custom routing policies.
From Sync APIs to support for the GPT-5 model series and agentic workflows: What’s new in Azure Content Understanding – August 2026
Enterprise content is no longer just something people read. AI apps and agents are only as useful as the information they can understand, yet much of the world’s enterprise knowledge is locked in documents, forms, tables, images, audio, and video. The latest Azure Content Understanding updates help developers turn that content into structured, grounded data with less custom processing. This release brings broader support for the GPT-5 model series, lower token usage, improved confidence scoring, new synchronous APIs for Read and Layout, advanced contextualization for higher-quality extraction, new tax-focused pre...
Azure Content Understanding GPT-5 Series Guide: Model Selection, Grounding Improvements, and Confidence Enhancements
Enterprise content is no longer just something people consume. As organizations increasingly rely on AI to extract and act on information from documents, images, audio, and video, Azure Content Understanding is expanding support for the GPT-5 series and improving grounding and confidence to deliver greater flexibility, efficiency, and quality. This expanded model catalog enables organizations to choose the right level of intelligence for each workload, helping reduce costs for high-volume processing while preserving access to advanced reasoning capabilities where needed. It also provides optimized pipelines tu...
Instructions Hygiene – What Frontier Models Still Need You to Say
Get the most out of the latest models by providing the right level of instructions
Build Locally, Ship to Cloud for $0: Azure SQL for Modern App Developers
As app developers, our primary focus is building great user experiences, designing APIs, and shipping features quickly. What we don't want is database friction—wasting hours configuring local DB instances, writing verbose migration scripts by hand, or worrying about unexpected cloud bills while prototyping. Today, we are making database development fast, intelligent, and completely free. By combining the Azure SQL Database container for local offline development, AI Agent Skills to automate your ORM and data layer, and the Azure SQL Database Free Tier, you can build locally and ship to production in minutes fo...
Beyond Chat: live Speech-to-Text with Foundry Local and C#
Build a live speech-to-text app in C# with Foundry Local, a compact Nemotron model, and local model lifecycle management.
From generated code to trusted code with a unit-test agent
Meet the open-source polyglot testing agent that learns from a repository, writes unit tests, and checks that they build and pass.
Announcing v2.0 of the official MCP C# SDK
MCP C# SDK v2.0 implements the 2026-07-28 specification with a stateless-first protocol, standardized HTTP headers, and Multi Round-Trip Requests for interactive tools, all while staying backward compatible.
Analyze MSBuild Binary Logs with Copilot in VS Code
Meet the MSBuild Binlog Analyzer for VS Code - a Copilot-powered way to read MSBuild binary logs, explain and fix build failures with one click, compare builds, and catch regressions, backed by the Microsoft.AITools.BinlogMcp MCP server.
Native Agent Memory for Microsoft Agent Framework, Powered by Azure Cosmos DB
Recently we introduced the Agent Memory Toolkit and the Agentic Retrieval Toolkit for Azure Cosmos DB. The Agent Memory Toolkit gives your agents durable, Cosmos-backed memory: it stores raw conversation turns and then distills them into higher-value derived memories (thread summaries, extracted facts, and cross-thread user profiles), all searchable with vector, full-text, and hybrid search in the one database you already use. Today we're taking the next step. With the latest release of Microsoft Agent Framework, you can now drop that memory into an agent with a single object: the , shipped in the new package f...
Introducing the ETW MCP: AI-assisted ETL trace analysis, headless and in your terminal
If you’ve ever opened an ETL trace with hundreds of data tables and wondered “where do I even start?” This post is for you. We’re releasing an early preview of the ETW MCP, a Model Context Protocol server that lets GitHub Copilot, or any MCP-aware AI assistant, read, query, and reason over Event Tracing for Windows (ETW) traces the same way an experienced engineer would. No UI required. This is a companion to the WPA MCP, which brings the same AI-assisted experience inside Windows Performance Analyzer. The ETW MCP server is the headless sibling, same data layer, no WPA needed, works anywhere you can run Copilo...
How to test agent experience changes without shipping them
Most changes you think will improve AI agent behavior won't. We tested a dozen hypotheses on a real project upgrade scenario and the majority failed. Learn how to emulate documentation, API, and MCP server changes locally so you can validate what works before shipping anything to production.
AI agents, meet the Azure Cosmos DB vNext emulator
If you use the Azure Cosmos DB vNext emulator, you probably know the local development loop: start the emulator, connect to it, create some resources, load test data, run queries, and inspect the results. Each step is straightforward, but together they add setup work before you can test the application you are actually building. How agents work with the emulator The emulator includes the Azure Cosmos DB Shell, an open-source CLI for working with databases, containers, and items. It runs inside the emulator container and handles the local endpoint and well-known key, giving developers a direct, scr...
How to test agent skills without hitting real APIs
Your agent skill calls an API. The moment you start evaluating it, every run either costs money or mutates production data. Learn how to mock APIs transparently so you can run evals without changing your skill or hitting real endpoints.
Teaching a Vision Model to See Like a Human Annotator—and Catching It When It Lies
A multimodal LLM enrichment pipeline that extracts structured metadata from visual assets, constrains output to predefined values to minimize hallucinations, and uses a ground truth evaluation template to measure quality—all as a plug-and-play module.
Building AX evals that actually work
This is the eighth and final article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. You've read seven articles about what to measure, why benchmarks don't transfer, and what hidden variables can do to your results. Now you actually have to build the thing. Most teams build an eval, run it, get scores, and feel good about the numbers. The trouble is that these evals produ...
Microsoft Agent Framework for Go public preview
Microsoft Agent Framework for Go is entering public preview, bringing Agent Framework concepts to Go developers building agents and multi-agent workflows.
Modernize .NET applications in the GitHub Copilot app
Modernize .NET applications in the GitHub Copilot app. Follow your upgrade from assessment through execution in an interactive upgrade canvas.
From Noisy Queries to Precise Frames: Query Decomposition for Media Asset Search
How query decomposition separates metadata filters from visual intent to significantly improve media asset retrieval quality.
Building on Vercel’s eve + Azure Cosmos DB: An Agent That Remembers
Most "AI agent" demos forget everything the moment the process exits. That's fine for a toy project, but useless for anything real. An agent that helps you write, triage, or support needs two things a language model alone can't give it: durable state and the ability to recall the right context by meaning. This post shows how to build exactly that by integrating two pieces that fit together surprisingly well: Eve — Vercel's filesystem-first agent platform. Drop a file in agent/tools/, and it becomes a tool the model can call. Azure Cosmos DB JavaScript SDK — the official, promise-based client for Cosmos DB N...
The hidden variables in your agent eval
This is the seventh article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. You build an eval. You run it on your machine. You get a score. Your colleague runs the same eval on their machine and gets a different score. Same scenario, same setup. What changed? In the previous article, we covered why public benchmarks can't tell you which model works best for your stack. T...
Don’t rewrite your CLI for agents
There's advice making the rounds: replace your CLI args with a single payload so agents can use your tool more effectively. The thinking being, that agents already think in structured formats, and nested data maps cleanly to JSON. Flat args on the other hand, force awkward conventions like repeating to delimit multi-value groups, which is inherently ambiguous. Not to mention, that the agent needs to get the types of all values right. It's a reasonable hypothesis, and we wanted to know if it holds up under measurement. The data we collected, showed something interesting. What we tested We built a synthetic CL...
Not all model upgrades are upgrades
A new model drops with lower per-token pricing and better benchmarks. You switch. A week later someone asks why the agent is burning 12x more tokens on the same task while producing worse output. We ran 150 agent tasks across 15 scenarios on two models, Claude Sonnet 4.6 and Claude Sonnet 5, using GitHub Copilot Chat in VS Code on Windows. The scenarios covered two types of work: architecture and design tasks grounded in Microsoft Learn documentation, and SharePoint Framework project upgrades. Sonnet 5 is the newer model with 33% lower per-token pricing across every token category. The assumption we wanted to te...
Enabling MLflow OpenAI Autolog on PySpark Workers
When distributing LLM calls across PySpark workers via mapInPandas, MLflow autolog silently fails. Here is how to fix it.
What AI benchmarks are not telling you
This is the sixth article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. We love benchmarks. A new model drops, the leaderboard says 92% on SWE-bench, and your timeline declares it "the best coding model." You switch to it, run your agent on your codebase, and outcomes are... the same. Maybe worse. The leaderboard said 92%, so what happened? In the previous article, we ...
What’s new across Microsoft SQL in 2026 so far (SQL Server, Azure SQL, and SQL database in Fabric)
We’re halfway through 2026, and Microsoft SQL has not slowed down. Since SQLCon/FabCon in March (where we released a ton of things, and those updates can be found in this updates video), we shipped a wave of updates across SQL Server, Azure SQL, and SQL database in Fabric, with Microsoft Build 2026 as the centerpiece. If you want the details on Build, start with my recap blog, The Era of the Agentic Database Developer. This post collects everything new from mid-March through today, organized by service so you can find what matters to you. Every item is tagged Preview or Generally Available with a link. You can...
MCP Beyond the Chat Window: Build Diagnostics in CI
A practical tour of the Model Context Protocol tools for .NET build diagnostics - the full Binlog MCP toolset, how those tools run inside a GitHub Actions workflow, and what the evaluation data says about the efficiency gains.
Spring AI 2.0 is GA: Vector Search, Memory, and Agents on Azure Cosmos DB
The wait is over. Spring AI 2.0 is generally available, and Azure Cosmos DB is right there with it. With this release, Spring AI graduates into a mature, production-ready framework for building AI applications in Java, and Azure Cosmos DB ships dedicated, vendor-maintained integrations that plug straight into the Spring AI ecosystem. The Spring AI 2.0 GA announcement names Azure Cosmos DB among its vendor-maintained modules, maintained directly by Microsoft rather than the core Spring AI team. This means the integration is built and supported by the engineers who work on Cosmos DB itself, bringing deep, first-ha...
Your agent already has a plan
If an agent isn't doing the right thing, the obvious move is to make the docs clearer. Add a tip, spell out the correct command, describe the right approach more prominently. You do all of that, and the agent still ignores it. It does what it had already decided to do. The tip wasn't ignored because it was unclear, it was ignored because the agent had already made its plan before it read the page. The agent plans before it reads An AI coding agent doesn't arrive at your documentation as a blank slate. The moment you give it a task, it forms a plan based on what it learned during training, before it goes and...
Learn from Microsoft: Transform software development through an agentic platform
See how Microsoft is transforming software development with agentic workflows, AI-powered automation, and specialized agents across the engineering lifecycle.
When the model has never seen your code
This is the fifth article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. Everything we've covered so far assumed the model has some training data about your technology. Maybe it's outdated, maybe it's biased toward a competitor, but there's something in the weights to work with. For proprietary code, internal SDKs, and custom frameworks, there's nothing. In the previous...
Models don’t have preferences, they have context
You open a fresh chat, type "What framework should I use for a web app?", and the model says "React." You screenshot it, share it, and write "Claude prefers React." It gets engagement. People nod along. A few reply with their own results. And now we have a consensus: Claude prefers React. Except it doesn't. The model doesn't prefer anything. You're reading the room, not the mind. The genre There's a whole genre of this. Run N prompts in a bare chat window, tabulate the answers, maybe build a heatmap, publish it as a blog post or a thread. "Which frameworks do LLMs prefer?" "What languages do models recommend mo...
Stop overloading your skills
You built a skill for your technology. API references, authentication flows, SDK patterns, error handling, version info, all packed into one skill. The agent calls it, gets all that context, and generates code. The kicker? You've just wasted a lot of tokens. It already knows Models have ingested your documentation, your Stack Overflow answers, your GitHub repos, your blog posts. The default imports, the standard auth flow, the common CRUD operations: the model already has all of that baked in. When your skill repeats what the model already knows, you're not helping, you're adding weight. Every token your skill...
AI-Powered MSBuild Investigation with the Microsoft Binlog MCP Server
Diagnose MSBuild build failures and performance issues with AI using the new Microsoft Binlog MCP Server - 15 specialized tools that let your AI assistant investigate binary logs.
How to diagnose conflicts between AI agent extensions
Your extension works in isolation. You measured it, confirmed it creates lift. Then a developer installs it alongside 14 other extensions, and outcomes get worse. The symptoms look like a bug in your code, but the problem is elsewhere: extensions fighting each other for the same context window and model attention.
Why AI coding agents keep using deprecated CLIs and SDKs
You deprecated the old CLI and shipped something better. Developers are migrating, but AI coding agents aren't. They keep reaching for the deprecated tool, confidently scaffolding projects with something you sunset months ago. The agent isn't broken, your docs aren't wrong. The model is doing exactly what ten years of training data told it to do, and once you understand why, you can fix it. Training data gravity Models learn from the internet. If your technology has been around for a decade, there are thousands of blog posts, Stack Overflow answers, tutorials, and GitHub repos that document the old way of doi...
GitHub Copilot for JetBrains is moving to Copilot CLI as the default agent harness
Copilot CLI is becoming the default agent harness in GitHub Copilot for JetBrains, and our local harness will be deprecated. This change provides greater consistency across all GitHub Copilot surfaces and is an important step toward faster feature parity and higher-quality results in GitHub Copilot for JetBrains. Copilot CLI sessions run independently in the background on your machine and use the Copilot CLI agent harness, while the IDE starts, monitors, and steers them. This is the same architecture used across GitHub Copilot today and adopting it in JetBrains lets us ship the same capabilities to JetBrain...
Your agent just scaffolded a project from 2020
Your agent ran a scaffold command. Project generated, dependencies resolved, no errors. Everything looks fine. Except it's based on the project structure from 2020, and neither you nor the agent noticed. How npx picks the right-but-wrong version When an agent scaffolds a project or runs a CLI tool, it often reaches for without specifying a version. Something like: Notice, that there's no version pinned anywhere. The agent typed the package name and assumed it'd get the latest. That's where things break. When you run without a version, npm resolves the latest version that's compatible with your current Nod...
How to measure AI agent extension effectiveness
You shipped your skill. Everything looks like it's working. But is the generated code actually better because of your extension? You can't tell without measuring, and measuring agent extension impact is harder than it looks.
Stop skillmaxxing, save your tokens
You built a dozen skills for your technology: authentication, CRUD, error handling, deployment, testing, monitoring. Then you installed a cloud platform bundle with 15 more covering diagnostics, storage, compliance, and cost optimization. A design suite. A marketing pack. Document converters for Word, Excel, PowerPoint, PDF. Fifty skills, all sitting in your workspace. Here is why that's a problem. The tax you pay before typing a single prompt Every skill has metadata: a name, a description, trigger phrases, sometimes parameter schemas. When you start a session, the agent discovers all of them and injects their...
Join us for .NET Day on Agentic Modernization Livestream
Announcing the .NET Day of Agentic Modernization Livestream
Spec-Driven Development: A Spec-First Approach to AI-Native Engineering
AI has made software delivery faster, but speed alone does not guarantee better outcomes. As teams adopt AI-native development, the real challenge is keeping requirements, design, implementation, and validation aligned so the final result still reflects the original intent. Spec-Driven Development (SDD) addresses this by making structured specs the shared source of truth for both humans and AI. Instead of prompting first and aligning later, teams align first and let AI accelerate execution from a clear spec. Why AI-assisted development still breaks down Teams often ship software that works but still misses the ...
Distributed multi-agent systems with Aspire and Microsoft Agent Framework
Learn how Aspire, Microsoft Agent Framework, and Microsoft Foundry model, run, observe, and publish a distributed multi-agent AlpineAI ski resort demo.
Microsoft Build 2026 recap: vision, launches, and top sessions
Catch up on Microsoft Build 2026 with the vision lead-off, top developer announcements, and must-watch sessions across the Microsoft developer ecosystem.
.NET at Microsoft Build 2026: Must watch sessions
Catch up on all the .NET sessions from Microsoft Build 2026 covering .NET 11, union types in C#, AI building blocks, the agentic web, .NET MAUI, and more!
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.
How to improve AI coding agent support for your SDK, API, or CLI
AI coding agents use your SDK, API, or CLI, but often get it wrong. The stack has layers you can't change and one you can: agent extensions. Learn how to measure whether yours are helping or hurting.
Announcing Agent Governance Toolkit MCP Extensions for .NET
Announcing a Public Preview .NET package that adds policy enforcement, startup tool scanning, fallback governance, and response sanitization to MCP servers with a single builder extension.
Improving C# Memory Safety
The `unsafe` keyword is being redesigned to mark caller-facing contracts rather than just syntax. Safety obligations between callers and callees become visible and reviewable. The model is motivated by the rise of AI-assisted code generation and arrives as a preview in .NET 11.
Your dev loop is full of tribal knowledge
Aspire turns a team's scattered tribal knowledge into an explicit, incrementally-adoptable app model that humans, scripts, and AI agents can all use.
Agentic-Agile: Why Agent Development Needs Agile (Not Just Prompts)
"A bad system will beat a good person [or agent] every time" ~Dr. William Edwards Deming (with apologies) I started vibe coding by writing prompts (often dictated into my phone), refining them with an agent in M365 Copilot, and creating handoff files to use with GitHub Copilot CLI. The results were predictably non-deterministic. Prompt-driven development is a typical starting pattern: a developer opens a chat session, writes a prompt, reviews the output, adjusts, re-prompts. Maybe they get something useful. Maybe they spend an afternoon debugging emergent behavior that nobody specified and nobody tested. Then...
Microsoft SQL Security Across the MAESTRO Stack: Building Secure Agentic AI with Defense-in-Depth
Artificial Intelligence is evolving rapidly. What began as simple prompt-and-response systems is now transforming into fully autonomous, agentic AI architectures capable of reasoning, orchestrating tools, interacting with enterprise data, and invoking external systems dynamically. While these capabilities unlock enormous business potential, they also introduce an entirely new category of security challenges. Organizations are no longer asking only: “How do we build AI systems?” They are now asking: “How do we build AI systems securely, responsibly, and with governance built into every layer?” This ...
Introducing WinUI agent plugin for GitHub Copilot and Claude Code
These skills enable your agent to take your ideas to a native Windows app in minutes! They are built around the end-to-end loop: scaffold, build, run, test, iterate. They've been optimized to know how to drive each stage, recognize common failures that get normal agents stuck in loops, and steer towards successful patterns, while also minimizing the number of tokens used by your agent. We hope to continually improve this experience and outcomes as we gather more feedback and examples from our developer community! Try it Open GitHub Copilot CLI and use the following commands: Then ask GitHub Copilot for t...
Foundry Local 1.1: Live Transcription, Embeddings, and Responses API
Foundry Local 1.1 adds live transcription, embeddings, Responses API, WebGPU plugin, and download cancellation.
Least privilege AI agents: A new azd template from Curity and Microsoft
If you ever built an AI agent demo, you probably had this moment. Everything works: the agent interprets natural language, calls the right tools, and returns the right data. Then you start designing for the real users of the app. You think about data boundaries: what if someone tries to get data they're not supposed to see? For example, imagine a customer support app where users can say "give me a markdown report on the last three months of stock transactions and the value of my portfolio." Then an agent does the work. First, you need to design for security so the agent doesn't commingle data from separate cus...
Scalable AI with Azure Cosmos DB: Bringing Generative AI to Enterprise Scale with Super Insight by AVASOFT
Azure Cosmos DB enables scalable AI-driven document processing, addressing one of the biggest barriers to operational scale in today’s enterprise AI landscape. Organizations continue to manage massive volumes of structured and unstructured content—contracts, regulatory filings, operational records, images, and field documentation—yet many workflows remain fragmented, manual, and slow. Every month, the Scalable AI in Action with Azure Cosmos DB series brings the community together with Microsoft partners who are building real, production AI systems — not slides, not demos built for the occasion, but live walkth...
SQL query generation from natural language
Evaluating AI agents for NL-to-SQL generation across Azure Databricks AI/BI Genie, GitHub Copilot CLI, and Microsoft Agent Framework. We achieved ~75% accuracy with schema documentation and runtime validation, while discovering that business logic errors represent a fundamental limitation requiring domain expertise.
Durable Workflows in the Microsoft Agent Framework
Build durable AI agent workflows with the Microsoft Agent Framework. Start with in-process console apps, add durability with the Durable Task runtime, scale with parallel AI agents, and host on Azure Functions for serverless execution.
Announcing the Public Preview of Azure Cosmos DB Shell: Open-Source Power Meets AI-Driven Database Automation
Today, we're thrilled to announce the public preview of Azure Cosmos DB Shell – a powerful, open-source command-line interface that revolutionizes how developers interact with their Azure Cosmos DB databases. With integrated Model Context Protocol (MCP) server support, native agentic capabilities, and bash-like syntax, Azure Cosmos DB Shell transforms database management into an intelligent, developer-friendly experience. From Friction to Flow: Why Cosmos DB Shell, and Why Open Source If you have ever bounced between a portal tab, an SDK sample, and a half-finished script just to answer one database quest...
Microsoft Agent Framework – Building Blocks for AI Part 3
Build intelligent AI agents in .NET with the Microsoft Agent Framework. Learn how to create agents with tools, multi-turn conversations, memory, and graph-based workflows that bring together the building blocks from Parts 1 and 2.
Introducing langchain-azure-cosmosdb: Build Agentic Apps and RAG with One Database
Build AI Agents and RAG Applications with the New LangChain + LangGraph Connector for Azure Cosmos DB Building AI agents and RAG applications today means stitching together half a dozen services, a vector database, a chat history store, a checkpointer for agent state, a semantic cache, a long-term memory layer. Each adds operational overhead, latency, and technical debt. langchain-azure-cosmosdb collapses that stack. It's a Python LangChain and LangGraph connector that turns Azure Cosmos DB for NoSQL into the single persistence layer for all of your agentic app scenarios. Azure Cosmos DB for NoSQL natively...
Building an AI-Powered Conference App with .NET’s Composable AI Stack
Learn how we built ConferencePulse, an AI-powered conference assistant, using .NET's composable AI stack including Microsoft.Extensions.AI, DataIngestion, VectorData, Model Context Protocol, and Microsoft Agent Framework.
Governing MCP tool calls in .NET with the Agent Governance Toolkit
Learn how to govern MCP tool calls in .NET with the Agent Governance Toolkit—policy-based access control, security scanning, and response sanitization to reduce risk in AI agent systems.
Azure MCP Server now available as an MCP Bundle (.mcpb)
Azure MCP Server is now available as an MCP Bundle (.mcpb), enabling one-click installation into Claude Desktop and other MCP-compatible clients.
Chat History Storage Patterns in Microsoft Agent Framework
When people talk about building AI agents, they usually focus on models, tools, and prompts. In practice, one of the most important architectural decisions is much simpler: where does the conversation history live? Imagine a user asks your agent a complex question, clicks “try again,” explores two different answers in parallel, and then comes back tomorrow expecting the agent to remember everything. Whether that experience is possible depends on the answer to this question. Your choice affects cost, privacy, portability, and the kinds of user experiences you can build. It also determines whether your applic...
LangChain.js for Beginners: A Free Course to Build Agentic AI Apps with JavaScript
Want to build AI agents with JavaScript that go beyond basic chat completions? Agents that reason, call tools, and pull from knowledge bases on their own? We put together a free, open source course to help you get there. LangChain.js for Beginners is 8 chapters and 70+ runnable TypeScript examples. Clone the repo, add your API key to a .env file, and start building. Why LangChain.js? If you already know Node.js, npm, TypeScript, and async/await, you don't need to switch to Python to build AI apps. LangChain.js gives you components for chat models, tools, agents, retrieval, and more so you're not wiring every...
Lessons Learned from Building a Well-Matching Intelligence Layer
Key insights from the EDA and Ground Truth journey for solving well identity resolution in the Energy industry—challenges that extend to any entity matching problem across distributed systems.
Windows App Development CLI v0.3: new run and ui commands, plus dotnet run support for packaged apps
Windows App Development CLI v0.3 is here! This release brings some of our best features yet including a full run-and-debug experience outside Visual Studio and built-in UI Automation from the command line. With v0.3, we've unlocked a whole class of agentic and automation scenarios. Agents or a script can now run, debug, see, and interact with a running Windows app — not just build it. Whether you're building with WinUI, WPF, WinForms, C++, Electron, Rust, Tauri, Flutter, or Avalonia — the Windows App Development CLI is for you. It provides the tooling to package, run, add Windows App SDK support, and mo...
Announcing Azure MCP Server 2.0 Stable Release for Self-Hosted Agentic Cloud Automation
Azure MCP Server 2.0 is now generally available, delivering first-class self-hosting, stronger security hardening, and a faster foundation for agentic workflows across Azure.
Foundry Local is now Generally Available
Ship local AI to millions of devices - fast, private on-device inference with no per-token costs.
MCP as Easy as 1-2-3: Introducing the Fluent API for MCP Apps
Build MCP tools with rich UI experiences using the new Fluent API for MCP Apps in the .NET isolated worker. Configure views, permissions, and security policies with just a few lines of code.
Agentic development aspirations: build, run, observe — without more Markdown
AI agents can write code but struggle to run and test it. Aspire gives your agent a codified app model to build, run, and observe your whole stack.
Aspire Docs in Your Terminal (and Your AI’s Brain)
Aspire 13.2 ships aspire docs, a CLI for browsing, searching, and reading official aspire.dev docs from your terminal, with the same source of truth available to AI skills and automation.
Generative AI for Beginners .NET: Version 2 on .NET 10
Announcement of Version 2 of Generative AI for Beginners .NET, a free course rebuilt for .NET 10 with Microsoft.Extensions.AI, updated RAG patterns, and new agent framework content across five structured lessons for building production-ready AI apps.
Announcing Aspire 13.2
Aspire 13.2 brings TypeScript AppHost authoring, an agent-native CLI, Foundry integration, and more.
Ten Months with Copilot Coding Agent in dotnet/runtime
After ten months using GitHub Copilot Coding Agent (CCA) in dotnet/runtime, the .NET team shares data-driven lessons on cloud-AI-assisted development.
Accelerating .NET MAUI Development with AI Agents
Learn how custom-built AI agents are dramatically improving the .NET MAUI contribution workflow, reducing issue resolution time by 50-70% while increasing test coverage and code quality.
RT.Assistant: A Multi-Agent Voice Bot Using .NET and OpenAI
Guest blog post on building a real time assistant using OpenAI Realtime API using .NET, F#, Microsoft.Extensions.AI and .NET MAUI.
Introducing the Azure Cosmos DB Plugin for Cursor
We're excited to announce the Cursor plugin for Azure Cosmos DB bringing AI-powered database expertise, best practices guidance, and live database connectivity directly into your coding workflow. Whether you're designing a new data model, optimizing queries, or building a RAG application with vector search, the Cursor plugin gives your AI assistant deep knowledge of Azure Cosmos DB so you can write better code, faster. What's in the Plugin The Azure Cosmos DB plugin bundles three capabilities: Finding and Installing the Plugin From the Cursor Marketplace That's it. The rules ar...
Awesome GitHub Copilot just got a website, and a learning hub, and plugins!
Back in July, we launched the Awesome GitHub Copilot Customizations repo with a simple goal: give the community a place to share custom instructions, prompts, and chat modes to customize the AI responses from GitHub Copilot. We were hoping for maybe one community contribution per week. That... did not happen. Instead, you all showed up. In a big way. The repo now has 175+ agents, 208+ skills, 176+ instructions, 48+ plugins, 7 agentic workflows, and 3 hooks - all contributed by the community. What started as a curated list has become something much bigger, and we needed to match that ...
Modernize .NET Anywhere with GitHub Copilot
See how the modernize-dotnet agent helps you assess apps, create upgrade plans, and modernize .NET projects from Visual Studio, VS Code, GitHub Copilot CLI, and GitHub.
A Look Ahead at Azure Cosmos DB Conf 2026: From AI Agents to Global Scale
Join us for Azure Cosmos DB Conf 2026, a free global, virtual developer event focused on building modern applications with Azure Cosmos DB. This year, Azure Cosmos DB Conf will feature 21 speakers from across the globe, bringing together Microsoft engineers, community leaders, architects, and developers to share how they are building modern applications with Azure Cosmos DB. Attendees will hear directly from experts using Azure Cosmos DB to power real systems—from AI agent memory architectures and retrieval-augmented generation pipelines to globally distributed event-driven micros...
Aspire Conf is Coming! Join us Live on March 23
Save the date and tune in for our free, livestream event on March 23. Discover Aspire and learn how it can transform the way you build and deploy your distributed apps and agents.
Extend your coding agent with .NET Skills
Introducing the dotnet/skills repository and how .NET agent skills can improve coding agent workflows.
Build a real-world example with Microsoft Agent Framework, Microsoft Foundry, MCP and Aspire
Building AI agents is getting easier. Deploying them as part of a real application, with multiple services, persistent state, and production infrastructure, is where things get complicated. Developers from the .NET community have requested whether a real-world example that shows running on local machine as well as on the cloud in a cloud-native way. We've heard you! We built an open-source Interview Coach sample to show how Microsoft Agent Framework, Microsoft Foundry, Model Context Protocol (MCP), and Aspire fit together in a production-style application. It's a working interview simulator where an AI coach w...
Release v1.0 of the official MCP C# SDK
Discover what’s new in the v1.0 release of the official MCP C# SDK, including enhanced authorization, richer metadata, and powerful patterns for tool calling and long-running requests.
The JavaScript AI Build-a-thon Season 2 starts today!
The JavaScript AI Build‑a‑thon Season 2 starts today! Join a free, four‑week, hands‑on program—from Local AI and RAG pipelines to multi‑agent hackathon—designed specifically for JavaScript/TypeScript developers.
Engineering and algorithmic interventions for multimodal post-training at Microsoft scale
Aditya Challapally leads post-training research and infrastructure for Copilot agent capabilities that process millions of multimodal interactions. This post builds on the diagnostics from Diagnosing instability in production-scale agent reinforcement learning with the engineering and algorithmic interventions we developed to get the best results out of post training at scale. Post-training multimodal agents at scale breaks in ways the literature doesn't prepare you for. Not because the algorithms are wrong, they work as described, but because the failure modes only become visible at production scale, under rea...
Vector Data in .NET – Building Blocks for AI Part 2
Discover how Microsoft.Extensions.VectorData brings unified vector database access to .NET - one interface for semantic search across any vector store with built-in support for embeddings, filtering, and RAG patterns.
The Dongle Died at Midnight – WinForms Agent Saved my German Mom’s Business Trip
Vibe Coding. IntelliSense on Steroids. Intention-Based Development. Call it what makes you happy - it's "just" the next evolutionary step in software development for me. Which let me build my stranded 82-year-old retirement-resistant mom an ersatz time-study app when her dongle died at midnight.
Improved Python (PyPi/uvx) support in Azure MCP Server
Azure MCP Server now offers first-class Python support via PyPI and uvx, making it easier than ever for Python developers to integrate Azure into their agentic workflows.
Use Windows on-device AI in your Electron app
Last year we published AI Dev Gallery, an open-source app full of interactive Windows AI examples. A common follow-up question from Electron developers has been: “How can we build similar on-device AI experiences in our Electron apps?” In this blog, we’ll walk through how we built an Electron app filled with samples of on-device Windows AI, using the Windows App Development CLI and Windows AI projections for JavaScript. Along the way, you’ll see patterns you can reuse to build your own AI experiences in Electron. Electron on Windows Gallery We built an open-source sample app with concrete examples of how to int...
How we built the Microsoft Learn MCP Server
When we launched the Microsoft Learn Model Context Protocol (MCP) Server in June 2025, our goal was simple: make it effortless for AI agents to use trusted, up-to-date Microsoft Learn documentation. GitHub Copilot and other agents are increasingly common, and they need to be able to ground responses just like humans with browsers do. Learn MCP Server is a remote server that exposes agent-friendly tools over Streamable HTTP Transport, backed by the same Learn knowledge service described in How we built “Ask Learn”. Why MCP and why Learn MCP Server? Modern AI agents can discover and use tools dynamically through ...
GitHub Copilot Testing for .NET Brings AI-powered Unit Tests to Visual Studio 2026
GitHub Copilot testing for .NET is now available in Visual Studio 18.3, offering AI-powered tools to quickly create, build, and run unit tests. With flexible prompts and full IDE integration, it supports testing from single methods to entire solutions, helping reduce repetitive tasks and speed up feedback. Try it and share your feedback to shape its future.
Building AI-Powered Apps with Azure Cosmos DB and the Vercel AI SDK
The Vercel AI SDK is an open-source TypeScript toolkit that provides the core building blocks for integrating AI into any JavaScript application. It works with 20+ AI providers out of the box—including OpenAI, Azure OpenAI, Anthropic, Google, Mistral, and more—so you can write your code once and switch providers by changing a single import. At its core, the SDK handles streaming (responses appear word-by-word instead of making users wait), tool calling (allowing the AI to invoke your functions, such as querying a database), and multi-step agentic reasoning, where the AI chains multiple tool calls together to a...
Azure Cosmos DB TV Recap – From Burger to Bots – Agentic Apps with Cosmos DB and LangChain.js | Ep. 111
In Episode 111 of Azure Cosmos DB TV, host Mark Brown is joined by Yohan Lasorsa to explore how developers can build agent-powered applications using a fully serverless architecture. This episode focuses on a practical, end-to-end example that demonstrates how transactional application data and AI-driven experiences can coexist on a single platform without introducing additional infrastructure or operational overhead. The session walks through a sample application inspired by a familiar business scenario: a simple ordering system. What begins as a traditional REST-based business API evolves into an agent-enabl...
Diagnosing instability in production-scale agent reinforcement learning
On January 28, 2026, Hugging Face announced that they have upstreamed the Post-Training Toolkit into TRL as a first-party integration, making these diagnostics directly usable in production RL and agent post-training pipelines. This enables closed-loop monitoring and control patterns that are increasingly necessary for long-running and continuously adapted agent systems. Documentation @ https://huggingface.co/docs/trl/main/en/ptt_integration. Overview In production-scale agent reinforcement learning systems, training runs increasingly operate over long horizons, incorporate external tools, and adapt continuousl...
Accelerate Your Cosmos DB Infrastructure with GitHub Copilot CLI and Azure Cosmos DB Agent Kit
Modern infrastructure work is increasingly agent driven, but only if your AI actually understands the platform you’re deploying. This guide shows how to turn GitHub Copilot CLI into an Azure Cosmos DB aware infrastructure expert by loading the Azure Cosmos DB Agent Kit. In under a minute, you’ll give Copilot deep, opinionated knowledge of Azure Cosmos DB best practices so it can review, generate, and optimize your Terraform, Bicep, Docker, and CI/CD configurations directly from your terminal. TL;DR — Get Started in 30 Seconds That's it! Copilot CLI now has expert-level Azure Cosmos DB infrastructure knowl...
.NET AI Essentials – The Core Building Blocks Explained
Learn how Microsoft.Extensions.AI provides a unified API for building intelligent .NET applications with any LLM provider - one interface, endless possibilities, with built-in middleware, telemetry, structured outputs and more.
Introducing Budget Bytes: Build Powerful AI Apps for Under $25
When developers hear "cloud" and "AI," their first thought is often about cost. "How much will this cost me to learn? Can I build something meaningful without racking up a surprise bill?" Budget Bytes is a new series is designed to inspire developers to build affordable, production-quality AI applications on Azure with a budget of $25 or less. Yes, you read that right, twenty-five dollars! What is Budget Bytes? Budget Bytes is an episodic video series featuring developers building end-to-end scenarios from scratch. But here's what makes it different: This season c...
Join Microsoft at NDC London 2026 – Let’s Build the Future of .NET Together
Meet the .NET team at NDC London 2026 to explore the latest in .NET 10, Azure, and AI-powered development through sessions and 1:1 meetups.
Azure Cosmos DB TV Recap: Supercharging AI Agents with the Azure Cosmos DB MCP Toolkit (Ep. 110)
In Episode 110 of Azure Cosmos DB TV, host Mark Brown is joined by Sajeetharan Sinnathurai to explore how the Azure Cosmos DB MCP Toolkit is changing the way developers build, deploy, and scale AI agents using real application data. As agentic AI systems evolve, one challenge continues to surface: securely and consistently connecting AI agents to operational data—without rewriting integration logic for every platform. This episode takes a deep dive into how the Model Context Protocol (MCP), along with Azure Azure Cosmos DB’s implementation, addresses that challenge. Why MCP Matters for AI Agents At its cor...
Bridging Local Development and Cloud Evaluation: Using Microsoft Devtunnels with Azure Machine Learning
Learn how to streamline AI development by using Microsoft Devtunnels to connect local services with Azure Machine Learning evaluation pipelines, eliminating deployment delays while maintaining comprehensive cloud-based validation.
Introducing the Azure Cosmos DB Agent Kit: Your AI Pair Programmer Just Got Smarter
The Azure Cosmos DB Agent Kit is an open-source collection of skills that teaches your AI coding assistant (GitHub Copilot, Claude Code, Gemini CLI) expert-level Azure Cosmos DB best practices. Install with one command, get production-ready guidance instantly. The Challenge Every Azure Cosmos DB Developer Faces You're building a new application with Azure Cosmos DB. You've got your data, you've got your queries, but then come the questions that keep you up at night: These aren't trivial questions. Poor decisions here can mean the difference between a blazing-fast application and one that...
Evaluate Small Language Model Function Calling using the Azure AI Evaluation SDK
This blog details how the Azure AI Evaluation SDK can be used to assess the performance of a small language model for function calling, such as Phi-4-mini-instruct, and view the results in Microsoft Foundry.
Adding Aspire to a Python RAG Application
This blog post documents the transformation of the azure-search-openai-demo (a Python/TypeScript RAG application) through the addition of Aspire for local development and observability. It demonstrates how Aspire can enhance polyglot applications without requiring rewrites.
Build AI Tooling in Go with the MCP SDK – Connecting AI Apps to Databases
A hands‑on walkthrough of building MCP servers that can plug AI applications into Azure Cosmos DB The Model Context Protocol (MCP) has established itself as the ubiquitous standard for connecting AI applications to external systems. Since its release, there have been implementations across various programming languages and frameworks, enabling developers to build solutions that expose data sources, tools, and workflows to AI applications. For Go developers, however, the journey to an official MCP SDK took longer (compared to other SDKs like Python and TypeScript). Discussions and design/implementation w...
Generative AI with Large Language Models in C# in 2026
A practical introduction to modern AI for .NET developers.
Top .NET Blog Posts of 2025
Let's look back at the most-read .NET blog posts published in 2025, from .NET 10 to AI, performance, and developer tooling.
Aspire 13.1 – Our holiday gift to you
Aspire 13.1 is here with CLI-based MCP for AI agents, dashboard improvements, Azure updates, and TLS termination support.
Agent Onboarding Process for Agentic Systems: Maintain accuracy at scale
A robust agent onboarding process is the backbone of scalable, reliable multi-agent AI systems—enforcing uniqueness, rigorous evaluation, and responsible AI practices at every step.
Microsoft Learn MCP Server Elevates Development
Explore how the Learn MCP server enhances the developer experience with Copilot, showcase practical examples, and provide straightforward integration instructions for Visual Studio, Visual Studio Code, the Copilot Command Line Interface, and the Copilot Coding Agent
Join us for AI Dev Days – December 10-11
2025 was a year of incredible progress for AI and our industry as a whole. At Microsoft and GitHub, we've innovated beyond isolated chats and prompts to building systems that collaborate with connected workflows and operational intelligence. This shift can be seen across the stack, from Microsoft Foundry, to GitHub Copilot in VS Code and Visual Studio, to the Cloud Agent on GitHub.com. AI is transforming the way we work internally, our customers are working, and developers more broadly are building. With this rapid era of change, we also recognize the vast amount of updates and training needed to keep up. Tha...
.NET Conf 2025 Recap – Celebrating .NET 10, Visual Studio 2026, AI, Community, & More
.NET Conf 2025 is over, but you can catch up with all the announcements and fun with video recordings, slides, demos, and more.
Introducing Data Ingestion Building Blocks (Preview)
Announcing the preview of open, modular data ingestion building blocks in .NET, empowering developers to build scalable AI pipelines with seamless integration, extensibility, and easy getting started experiences across the .NET ecosystem.
The Interaction Changes Everything: Treating AI Agents as Collaborators, Not Automation
Discover how treating AI agents as collaborators, not automation, transforms engineering workflows and accelerates complex projects
Tata Neu delivers personalized shopping experiences for millions of users with Azure DocumentDB
Editor's note (May 2026): This article was published in the Azure Cosmos DB Dev Blog before the launch of the dedicated Azure DocumentDB Dev Blog. Azure DocumentDB now has its own home for product announcements, engineering insights, migration guidance, technical deep dives, and developer tutorials. If you're building with Azure DocumentDB or the open-source DocumentDB project, we recommend bookmarking the Azure DocumentDB Dev Blog for the latest news and technical content. With Azure DocumentDB, Tata Neu delivers seamless authentication for millions of users, accelerates credit card onboarding across partners...
Join the OSS AI Summit: Building with LangChain Event
December 10, 2025 8 am - 10 am Pacific Registration: https://aka.ms/OSSAISummitRegistration Overview AI development is moving at a pace that leaves most teams reacting instead of leading. The next generation of applications will be built by people who understand how to design agents, coordinate tools, and create workflows that can handle real complexity. The OSS AI Summit is designed to help you get ahead of that shift. This event focuses on LangChain v1, Microsoft Foundry, and the systems that make modern AI applications reliable, scalable, and practical to ship. If you want to build smarter agents and u...
.NET Day on Agentic Modernization Coming Soon
Join us live on December 9 to explore the newest, most practical ways to modernize your .NET apps with Azure, AI, and powerful agentic tooling.
Introducing Multi-Agent Workflows in Foundry Agent Service
Across industries, organizations are moving from experimenting with single agents to running AI at the center of their business operations. While single agents excel at focused tasks, customers quickly discover that real enterprise work stretches across multiple steps, involves different roles, and requires strong governance. To operationalize AI in this environment, teams need a dependable way to coordinate agents, tools, and logic into complete, end-to-end processes. Over the past year, as customers deployed agents into production, the same challenges surfaced across industries: At M...
Translation Customization, A Developer’s Guide to Adaptive Custom Translation
Introduction Translation isn’t just converting words—it’s enabling global communication. Yet for businesses operating worldwide, achieving accuracy, speed, and domain-specific terminology has been a persistent challenge. Market trends show that 70% of consumers prefer content in their native language, and enterprises are under pressure to deliver real-time multilingual experiences without sacrificing quality. That’s why we’re introducing Adaptive Custom Translation (AdaptCT) in Microsoft Foundry Tools—a breakthrough that redefines how translation systems are customized. Instead of retraining models from scr...
Assess Agentic Risks with the AI Red Teaming Agent in Microsoft Foundry
Accelerate your trustworthy AI journey with the enhanced AI Red Teaming Agent in Microsoft Foundry. Empower developers to automate adversarial testing for both models and agentic systems—covering risks like prompt injection, prohibited actions, sensitive data leakage, and task adherence. Integrate red teaming into your CI/CD pipelines using the Foundry SDK and no-code UI wizard, enabling continuous safety evaluation and rapid prototyping. With PyRIT’s open-source attack strategies and customizable risk definitions, you can systematically probe vulnerabilities, benchmark improvements, and ensure robust safeguards ...
Use Azure SRE Agent with Azure Cosmos DB: Smarter Diagnostics for Your Applications
We’re excited to announce the Azure Cosmos DB SRE Agent built on Azure SRE Agent; a new capability designed to simplify troubleshooting and improve reliability for your applications running on Azure Cosmos DB. Some key features of Azure Cosmos DB SRE Agent are: Benefits of using Azure Cosmos DB SRE Agent Managing distributed applications can be complex. The Azure Cosmos DB SRE Agent bridges the gap by: Azure Cosmos DB SRE Agent Scenario Integrating Azure SRE Agent with your Azure Cosmos DB resource can leverage diagnostics to help identify issues or areas for improvement....
Migrating from Microsoft Learn to aspire.dev
Learn how we migrated all Aspire docs from Learn to the new website aspire.dev learn
Supercharge Your Test Coverage with GitHub Copilot Testing for .NET
Boost your testing workflow with GitHub Copilot testing for .NET, available now in Visual Studio. Automatically generate, build, and run high-quality unit tests for files, projects, or entire solutions.
Announced at Ignite 2025: Azure DocumentDB, MCP Toolkit, Fleet Analytics, and more!
Microsoft Ignite 2025 kicked off with a wave of announcements for Azure Cosmos DB and Azure DocumentDB, setting the tone for a week of innovation in cloud databases and AI. Here’s your one-stop summary of everything unveiled—and what to watch for in the sessions ahead. Major Azure Cosmos DB announcements from Ignite 2025 Azure DocumentDB now Generally Available Azure DocumentDB is now generally available, bringing a new level of openness, flexibility, and cost efficiency to MongoDB workloads. Built on the open-source DocumentDB engine – now governed by the Linux Foundation – Azure DocumentDB delivers Mongo...
New T-SQL AI Features are now in Public Preview for Azure SQL and SQL database in Microsoft Fabric
At the start of this year, we released a new set of T-SQL AI features for embedding your relational data for AI applications. Today, we have brought those features to Azure SQL and SQL database in Microsoft Fabric. This post will help you get started using the new AI functions of Azure SQL. Prerequisites Set up your environment The following section guides you through setting up the environment and installing the necessary software and utilities. Set up the database The following section guides you through using the embeddings model to create vector arrays on relation data and use ...
A step-by-step guide to modernizing .NET applications with GitHub Copilot agent mode
Learn how Visual Studio 2026 and GitHub Copilot app modernization upgrade .NET versions and frameworks, fix build issues, and migrate apps to Azure with less manual effort
Introducing the Azure Cosmos DB MCP Toolkit: Empowering AI Agents with Azure Cosmos DB Intelligence
We're excited to announce the Azure Cosmos DB MCP Toolkit in public preview. This open-source implementation of the Model Context Protocol (MCP) gives AI agents and LLMs direct, secure access to Azure Cosmos DB. The toolkit bridges intelligent applications and globally distributed databases, enabling AI agents to query, search, and understand data autonomously. The MCP Toolkit seamlessly integrates with Microsoft Foundry, a platform for building intelligent agents, AI workflows, and deploying AI models at scale. Developers can experiment, orchestrate, and manage AI applications from development to production. ...
SQL Server 2025 Embraces Vectors: setting the foundation for empowering your data with AI
SQL Server 2025 marks a major milestone in database innovation by introducing native support for VECTOR data types and vector functions. These are now fully available in the freshly released SQL Server 2025 RTM. This means you can store and process high-dimensional embeddings directly within your database. It eliminates the need to move data across separate systems. With this integration, your data remains secure, synchronized, and governed by SQL Server’s enterprise-grade compliance and security features. The RTM release includes the VECTOR data type and built-in functions like VECTOR_DISTANCE. This suppor...
What’s New in Semantic Search for Azure Cosmos DB at Ignite 2025
We’re excited to introduce new capabilities and performance improvements for vector and full-text search in Azure Cosmos DB. These updates make it easier for developers and architects to build semantic search experiences and generative AI applications on the world’s most scalable NoSQL database. Note: These new capabilities are actively rolling out. As a result, they may not yet be available in all Azure regions. Float16 Vector Support Save up to 50% on storage while maintaining high recall. You can now store and index vectors in Float16 format, reducing storage costs and memory usage while keeping...
How Kraft Heinz achieved 1,400x faster data lineage with Azure DocumentDB’s DiskANN vector search and hybrid search
This article is authored by Gurvinder Singh, Principal Cloud Engineer at The Kraft Heinz Company. ICYMI: Hear how Kraft Heinz built an AI-powered data lineage solution leveraging Azure DocumentDB hybrid search and graph querying capabilities - watch the Ignite session here: Move fast, save more with MongoDB-compatible workloads on DocumentDB The enterprise-scale challenge: Data lineage across 50+ interconnected systems At Kraft Heinz, we operate one of the world’s largest CPG data ecosystems with more than 200 brands and 50+ interconnected enterprise systems—including SAP, Oracle, Snowflake, and Po...
Introducing Copilot auto model selection (preview)
Faster responses, a lower chance of rate limiting, and 10% off premium requests for paid users - auto picks the best available model for each request based on current capacity and performance. With auto, you don't need to choose a specific model. Copilot automatically selects the best one for your task. Auto model selection in Chat is rolling out in preview to all GitHub Copilot users. How auto model selection works Auto selects the best model to ensure that you get the optimal performance and reduce the likelihood of rate limits. Auto will choose between GPT-5, GPT-5 mini, GPT-4.1, Sonnet 4.5, and Haiku 4.5 an...
Aspire 13 – Aspireify anything
Aspire 13 is here with revamped JS and Python experiences, new AI-focused grations, and a new iteration of deployment tooling.
Announcing .NET 10
Announcing the release of .NET 10, the most productive, modern, secure, intelligent, and performant release of .NET yet. With updates across ASP.NET Core, C# 14, .NET MAUI, Aspire, and so much more.
IntelePeer supercharges its agentic AI platform with Azure Cosmos DB
Reducing latency by 50% and scaling intelligent CX for SMBs This article was co-authored by Sergey Galchenko, Chief Technology Officer, IntelePeer, and Subhash Ramamoorthi, Director, IntelePeer AI Hub. ICYMI: Discover how IntelePeer enhances agent intelligence and powers their multi-agent applications - watch the Ignite session here: From DEV to PROD: How to build agentic memory with Azure Cosmos DB. You don’t need to be an AI expert, software engineer, or data scientist to understand the importance of system reliability and performance in digital customer service platforms. If you’ve ever tried to...
Roadmap for AI in Visual Studio (November)
Today, we’re excited to share our public roadmap, which outlines the next steps in evolving Visual Studio with AI-powered agentic experiences. With every month, we aim to deliver smarter, faster, and more intuitive tools that enhance your coding experience. Disclaimer: The items outlined here represent ongoing work for the month. They are not commitments or guarantees for delivery within the current month. Upvote the features you or your organization care about most, so we know what to prioritize. With that said, here is what we are working on! New Agents We’re streamlining how you find and switch between mo...
Powering Distributed AI/ML at Scale with Azure and Anyscale
The path from prototype to production for AI/ML workloads is rarely straightforward. As data pipelines expand and model complexity grows, teams can find themselves spending more time orchestrating distributed compute than building the intelligence that powers their products. Scaling from a laptop experiment to a production-grade workload still feels like reinventing the wheel. What if scaling AI workloads felt as natural as writing in Python itself? That’s the idea behind Ray, the open-source distributed computing framework born at UC Berkeley’s RISELab, and now, it’s coming to Azure in a whole new way. Today,...
Get Ready for .NET Conf 2025!
The biggest .NET event of the year is just one week away! Join us November 11-13 for .NET 10 and Visual Studio 2026, plus a Student Zone on November 14th.
Accelerate Your Growth: Azure Cosmos DB Partner Acceleration Program
Unlock 360° Success with the Cosmos DB Engineering Team Are you ready to elevate your cloud data practice and drive innovation for your customers? The Azure Cosmos DB Partner Acceleration Program—spearheaded by the Cosmos DB Engineering Team—offers a unique, end-to-end journey for partners. This initiative is designed to empower, upskill, and accelerate your business with technical enablement, customer engagement support, and robust Go-To-Market (GTM) opportunities. Please note, it is open to all type of partners, ISVs, GISV, GSIs, SIs or Channel has technical practices. Sign up now: https://aka.ms/cdbpartner...
Behind the Universe demo: From prompt to production with VS Code, GitHub Copilot and the Microsoft Agent Framework
At this year’s GitHub Universe, we set out to answer a simple question: What would it take to go from an idea to production in under 30 minutes – using only natural language, your editor, and AI? On stage, we built a cloud-native, intelligent app from scratch using Visual Studio Code, GitHub Copilot, and the Microsoft Agent Framework. In this post, we’ll walk through the journey step-by-step: From spec to code Modern greenfield apps aren’t just “hosted in the cloud” anymore – they’re born there. They’re expected to be cloud-native and auto-scaling. The table stakes have been raised, and t...
Tuning and Optimization of Speech-to-Text (STT), Text-to-Speech (TTS), and Custom Keyword Recognition in Azure Speech Services
This blog outlines best practices for optimizing Speech-to-Text (STT), Text-to-Speech (TTS), and Custom Keyword Recognition in Azure Speech Services, helping developers build more accurate and responsive voice-enabled applications.
Put your AI to the Test with Microsoft.Extensions.AI.Evaluation
AI is changing how we build software, but it also introduces new challenges: how do you know if your AI application is giving the right answers and producing the right outcomes? Evaluations (often referred to simply as ‘evals’) provide a structured way to measure quality so you can trust your results. In this blog, we’ll introduce the concept of AI evals, show how the Microsoft.Extensions.AI.Evaluation libraries support .NET intelligent app development in Visual Studio, and walk through a simple example of how to use them in your project. Why Evaluate? As developers, we rely on tests to validate our code bef...
Introducing Custom Agents for .NET Developers: C# Expert & WinForms Expert
Introducing C# Expert and WinForms Expert: experimental custom agents that help .NET developers write better code with GitHub Copilot.
Announcing Azure MCP Server 1.0.0 Stable Release – A New Era for Agentic Workflows
Today marks a major milestone for agentic development on Azure: the stable release of the Azure MCP Server 1.0!
Java and AI for Beginners: a practical video series for Java
If you're looking for a clear, no-nonsense path into generative AI on Java, this series is for you. Microsoft's Java and AI for Beginners video series is a set of short tutorials that introduce the concepts, tooling, and patterns you need to get started at a pace that respects your time and experience. What the series covers We help you through foundational ideas first and then move into hands-on examples: Each video is short and focused. Watch them in order if you are new to the space, or skip into the topics that match your immediate needs. Integrations you w...
Upgrading to Microsoft Agent Framework in Your .NET AI Chat App
Step-by-step review on how to upgrade your .NET AI chat app to Microsoft Agent Framework for better architecture, tool integration, and intelligent reasoning.
Introducing Planning in Visual Studio (Public Preview)
Quick prompts are great for small fixes, but they fall short on big projects. You end up rewriting instructions, tweaking them constantly, and hoping the model stays on track. It gives Copilot a visible, structured path to follow, one that updates as it works and keeps you in control at every step. In Agent Mode, Copilot can now create plans that research your codebase, break down big tasks, and run them step by step while iterating along the way. The result is a more predictable and transparent workflow that helps you understand exactly what it’s doing. How It Works Planning uses transparent tool ca...
Easily connect AI workloads to Azure Blob Storage with adlfs
Microsoft works with the fsspec open-source community to enhance adlfs. This update delivers faster file operations and improved reliability for AI workloads that connect to Azure Blob and Data Lake Storage.
Complete Beginner’s Guide to Vibe Coding an App in 5 Minutes
How I built a full-featured podcast analytics dashboard with GitHub Copilot without writing a single line of code myself.
Developer and AI Code Reviewer: Reviewing AI-Generated Code in .NET
Learn how to effectively review AI-generated .NET code with practical strategies for maintaining quality, enforcing standards, and boosting team productivity through thoughtful code review practices.
Introducing Microsoft Agent Framework (Preview): Making AI Agents Simple for Every Developer
Microsoft Agent Framework (Preview) unifies agent creation, orchestration, tooling, hosting, and observability so any .NET developer can ship production AI agents faster.
Announcing Aspire 9.5
Aspire 9.5 adds the preview 'aspire update' command, single-file AppHost, richer CLI and dashboard UX, and new integrations for AI, DevTunnels, and more.
Taming Complexity: Intuitive Evaluation Framework for Agentic Chatbots in Business-Critical Environments
This blog post introduces a comprehensive evaluation framework for enterprise chatbots powered by large language models (LLMs), specifically addressing the challenges of assessing Line of Business (LOB) agents in business-critical environments. The authors tackle the fundamental problem that traditional chatbot evaluation metrics fail to capture the nuanced, non-deterministic performance of modern LLM-based systems, proposing a solution that combines realistic chat simulation using an LLM-powered User Agent, automated ground truth generation at scale, and comprehensive metrics including function call precision, r...
Exploring Text-to-Image capabilities in .NET
Learn how to generate images from natural language prompts using the universal abstractions in Microsoft's extensions for .NET AI.