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Developer focused how-tos, use cases and solutions on Microsoft Azure
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Platform Engineering for the Agentic AI era
For the last decade, platform engineering has relied on explicit API interaction layers: CLIs, SDKs, pipelines, wrappers, and UI workflows that translate human ...
Context-Driven Development: Agent Skills for Microsoft Foundry and Azure
Code will be generated, not written. Most enterprise AI workloads are net-new microservices. Modular, greenfield work. Perfect for coding agents. The catch? ...
Claude Code + Microsoft Foundry: Enterprise AI Coding Agent Setup
This guide covers setting up Claude Code CLI and VS Code extension with Microsoft Foundry, configuring CLAUDE.md for project context, integrating Spec Kit for s...
Visualizing GitHub Audit Log in Microsoft Defender
Key Observability Trends Around GitHub Security Modern enterprises are increasingly adopting DevSecOps practices, integrating security into every phase...
Codex Azure OpenAI Integration: Fast & Secure Code Development
Introduction You can now enjoy the same Codex experience in CLI or VS Code with Azure OpenAI support. We've contributed the following five pull requests to mak...
How to develop AI Apps and Agents in Azure – A Visual Guide
As organizations explore new AI-powered experiences and automated workflows, there's a growing need to move beyond experiments and proofs-of-concept to producti...
Latest posts
Project Nighthawk: A Research Agent Built for Field Engineering
If you work in field engineering, you know the scenario. A customer is deploying AKS in a regulated environment. They hit an issue during node bootstrapping. They want to know exactly what happens when a node joins the cluster, which components run in which order, and whether the behaviour they're seeing is expected. The question sounds simple. The answer is not. The answer is spread across half a dozen places at once. It's in the source code: AgentBaker, the node controller, cloud-provider-azure. It's in a Microsoft Learn article that's technically correct but three levels of abstraction above what actually r...
Building with Azure Skills
Part 3 of the Azure Skills Plugin series Previously: How to Install the Azure Plugin You've installed the Azure Skills Plugin. The Azure MCP server is running. You have a huge collection of tools and skills at your disposal. So, now what do you actually say to it? This post is a prompt cookbook. Every example below is a real prompt you can type into Copilot Chat (or Copilot CLI, or Claude Code) with the Azure Plugin installed. Each one triggers a specific skill and produces a concrete, actionable result - not generic advice. A note on scope: Azure is huge, and the examples below reflect th...
Your Entire Engineering Floor Just Stopped Coding
And the developers running Claude Code and GitHub Copilot CLI didn't notice... Status Page Says 'Operational.' Your Subagents Say Otherwise. If you're running autonomous agents in any serious capacity, you've experienced this: model provider outages aren't edge cases — they're part of the operating environment. Anthropic has had outages. OpenAI has had outages. Google has had outages. Every major model provider has had the kind of degraded performance that doesn't trigger a status page alert but absolutely kills an agentic coding session. The traditional answer is "wait it out." But if you're a solution...
Agentic Platform Engineering with GitHub Copilot
We've talked about the human scale problem and what happens when infrastructure scales but understanding doesn't. If you've been following along, you know the thesis: our tools have outpaced our ability to operate them, and platform engineering is how we're fighting back. But here's the thing - we've been fighting with one hand tied behind our backs. We've been encoding knowledge into runbooks that go stale, documentation that drifts, and tribal expertise that walks out the door when someone takes a new job. What if the platform itself could think alongside us? That's what we mean by agentic platform engine...
When Infrastructure Scales But Understanding Doesn’t
We all know this, even if we don't like to admit it: modern infrastructure can scale infinitely, but human understanding doesn't. We've all seen it happen - organizations going from managing dozens of servers to thousands of containers, from deploying weekly to deploying hundreds of times per day, from serving thousands of users to millions. The technology handled the scale beautifully. The humans? Not so much. This is the first industry issue that platform engineering should be addressing: how do we manage infrastructure complexity that has outgrown not just individual cognitive capacity, but our collectiv...
From 150 Unread to Zero Stress: Automating Inbox Triage with MCP and GitHub Copilot
Taming the Noisy Inbox: How I Used MCP to Automate Email and Teams Triage How the Model Context Protocol (MCP) turns your AI coding assistant into a workplace productivity engine — connecting Microsoft 365 data to your terminal workflow. The Problem We All Share If you work in a customer-facing role, you know the feeling. You open your laptop on Monday morning and you’re staring at 150+ unread emails, dozens of Teams threads, and the creeping anxiety that something important is buried in there — a customer escalation, an exec ask, a deadline you forgot about. You start scrolling. You context-switc...
Azure Skills Plugin – Let’s Get Started!
Part 2 of the Azure Skills Plugin series Previously: Announcing the Azure Skills Plugin This post is all about getting you up and running. I won't go deep on capabilities, architecture or anything like that here - that's coming in future posts in this series. The goal is to get the plugin installed, verified, and ready so you can start exploring right away. The Azure Skills Plugin works across many agent hosts including GitHub Copilot CLI, VS Code, and Claude Code. Each install takes under 60 seconds. This post covers all three, what gets installed, and how to verify it's working. 👉 Plugin repo:...
The Human Scale Problem in Platform Engineering
We keep doing this thing where we solve a problem, celebrate the victory, then realize we've created three new problems we didn't even know existed. Remember when manually configuring servers was the bottleneck? So we built containers. Great! Now we're orchestrating thousands of them. Remember when monolithic deployments were too slow? So we built microservices. Fantastic! Now we're drowning in distributed system complexity. We solved manual infrastructure provisioning with infrastructure as code. Perfect! Now we're coordinating dozens of Terraform modules across environments and wondering how we got here. ...
Announcing the Azure Skills Plugin
Part 1 of the Azure Skills Plugin series Coding agents like GitHub Copilot and Claude Code are great at code, but getting your app to production on Azure is not just about writing code. Really, it is about making the right calls. Which service fits this app? Which SKU fits this workload? Should this be App Service, Container Apps, Functions, AKS, or something else entirely? What needs to be validated before deploy? Which permissions, quotas, and guardrails matter? That is exactly why skills are taking off: they give agents practical knowledge on demand instead of forcing them to guess. Want to try t...
Platform Engineering for the Agentic AI era
For the last decade, platform engineering has relied on explicit API interaction layers: CLIs, SDKs, pipelines, wrappers, and UI workflows that translate human intent into machine‑safe API calls. AI agents are now short‑circuiting much of that stack. By combining natural language understanding, reasoning, and direct access to API specifications and control schemas, agents can convert human intent directly into validated platform actions, often without a bespoke interaction layer in between. Nowhere is this shift more visible than in Infrastructure as Code (IaC) and pipeline workflows, where agents are increasi...
Measuring actual AI Impact for Engineering with Apache DevLake
If you want to skip the explain and get started super quick with adoption + impact insights, use gh-devlake to deploy a GitHub Copilot impact dashboard in a few CLI commands. So! You've rolled out GitHub Copilot to your engineering teams. You've got the built-in dashboards. You know how many seats are assigned, what the acceptance rates look like, which editors your teams prefer. Maybe you've even pulled the Copilot Metrics API and built some charts. But here's the question your VP of Engineering or CTO is actually asking: "Is GitHub Copilot making us ship faster? Are we more reliable? Is code review g...
The OS for Intelligence: How GitHub Bridges the Fragmented AI Landscape
We are currently living through the "fragmentation phase" of the AI revolution. If you’re a developer, you know the drill: You have Claude Code open for reasoning. You have ChatGPT open for logic checks. Then you drop into your terminal to actually build the thing—manually copy-pasting context between three different windows. We call this the "fragmentation tax." It kills momentum, breaks your flow, and frankly, it’s a waste of cognitive energy. For engineering leaders, it’s even worse. It’s a governance nightmare and a silent killer of velocity. GitHub’s answer isn't just another tool; it’s an Op...
Context-Driven Development: Agent Skills for Microsoft Foundry and Azure
Code will be generated, not written. Most enterprise AI workloads are net-new microservices. Modular, greenfield work. Perfect for coding agents. The catch? Out-of-the-box agents lack domain knowledge about your SDKs and patterns. But frontier LLMs are extraordinarily sample efficient. The patterns you need are already encoded in their latent space from pretraining. All you need is the right activation context. That is what skills do, and Agent Skills ships 126 of them for Azure and Microsoft Foundry development. New: Browse all skills, agents, and documentation at microsoft.github.io/skills. What's in Age...
The Realities of Application Modernization with Agentic AI (Early 2026)
How to read this article This article is a reflection based on hands-on experience and is written for engineers and technical leaders who are facing a new application modernization effort and want to build a realistic mental model before reaching for tools. If you are new to application modernization, I recommend reading the article end to end. The early sections focus on why modernization is hard in practice and which foundations matter before any technical decisions are made. If you are already familiar with the app modernization space and mainly interested in the role of agentic AI, you can skip the intro...
AI Coding Agents and Domain-Specific Languages: Challenges and Practical Mitigation Strategies
1. Introduction AI coding agents/assistants such as GitHub Copilot have become common in modern software engineering workflows. Their strengths—rapid pattern completion, context-aware suggestions, and the ability to learn style from local code—stem from broad training on large corpora of public, general-purpose code. They perform best when the languages, libraries, and idioms requested by developers align with patterns they have seen many times before. Domain-Specific Languages (DSLs) break this assumption. DSLs are deliberately narrow, domain-targeted languages with unique syntax rules, semantics, and ...
Locking Down MCP: Create a Private Registry on Azure API Center and Enforce It in GitHub Copilot And VS Code
Ever since MCP launched, every customer has asked the same thing: “How does a private MCP registry actually work, and how do we configure it for our enterprise?”. So today, on a snowy, freezing Friday in Zurich, I grabbed a coffe, opened the GitHub docs, dove into Azure API Center portal, and decided to write the blog I wish already existed.A few hours (and quite a few sighs) later, here I am. The docs are great but they definitely don’t cover all the tiny quirks, hidden settings, and errors you’ll hit along the way. What did the journey look like? This post is the guide I d...
Claude Code + Microsoft Foundry: Enterprise AI Coding Agent Setup
This guide covers setting up Claude Code CLI and VS Code extension with Microsoft Foundry, configuring CLAUDE.md for project context, integrating Spec Kit for structured development, and running Claude Code in GitHub Actions. Prerequisites Step 1: Deploy Claude Models in Foundry In Microsoft Foundry: Alternative: Model Router Model Router is a Foundry model that intelligently routes each prompt to the best underlying model based on query complexity, cost, and performance. Version supports Claude Haiku 4.5, Sonnet 4.5, and Opus 4.1 alongside GPT, DeepSeek, Llama, and Gro...
Visualizing GitHub Audit Log in Microsoft Defender
Key Observability Trends Around GitHub Security Modern enterprises are increasingly adopting DevSecOps practices, integrating security into every phase of the development lifecycle. Key observability trends include: Challenges in Displaying All Security in One Dashboard Despite GitHub’s robust security features, customers face several challenges: Why Visualizing GitHub Audit Logs in Defender Makes Sense Integrating GitHub audit logs into Microsoft Defender offers several advantages: Microsoft’s solution strategically aligns GitH...
Develop Faster with VS Code for the Web – Azure: Your Browser-Based Dev Environment
Developers can now move from idea to Azure-ready code in minutes with VS Code for the Web – Azure. This browser-based environment removes setup time, reduces friction, and gives you immediate access to pre-configured runtimes, GitHub tools, and Azure integrations. As a result, you can start building, editing, and deploying the moment inspiration strikes. For more info, view our VS Code Docs. Build in the Browser with a Ready-to-Use Azure Environment VS Code for the Web – Azure opens directly in your browser, and it loads a workspace that includes Node.js, Python, Java, C#, Git support, and the Azure Develop...
Azure DevOps to GitHub migration Playbook: Unlocking Agentic DevOps
Azure DevOps or GitHub Enterprise ? Today, in the Microsoft ecosystem, two Software Development Lifecycle management platforms coexist: - Azure DevOps is designed from the ground up for enterprise with advanced planning features through Azure Boards, build and release automation with Azure Pipelines, and a unique offering for quality teams with Azure Test Plans... - GitHub has evolved with the open-source ecosystem, and its operation is naturally more centered around code repositories, visible by default, and the system of proactive contributions through Forks and Pull Requests. The platform has evolved to...
Tutorial Videos: Setting up GitHub Copilot for your Company
This guide shares a series of videos that walk you through setting up GitHub and GitHub Copilot end-to-end for your company. These are especially useful for organizations starting from scratch who want to leverage GitHub Copilot and are looking to get started. These videos were recorded as of October 2025. This article is intentionally brief—just links and essential references so you can dive straight into the content. The videos cover: Note: Although some experiences may evolve as we streamline our onboarding processes, these videos remain a solid introduction. We will prov...
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,...
Multi-region Expansion for Azure Deployments
Why Financial Services Organizations Should Consider Expanding to Other Azure Regions for New Workloads As a Cloud Architect, I work closely with financial services organizations to help them unlock the full potential of Azure innovations. One of the most impactful strategies I recommend is adopting a multi-region approach for Azure deployments. While many customers have historically concentrated workloads in a single region—often for simplicity or legacy reasons – the landscape is rapidly changing. Here’s why expanding to other Azure regions for new workloads is not just a technical consideration. It is a strat...
Your 6-Step Guide to Deploying a Website with GitHub Codespaces and Copilot agent mode
👋everyone! Shree here. I'm sure many of you have had a similar chat with someone you know. We often hesitate to celebrate our achievements because of social perceptions around self-promotion. I believe it’s time to challenge that mindset. Building a personal site is about owning your narrative and showcasing your work with pride. In this post, I’ll show you how to launch your very first personal site (or, a portfolio piece) using GitHub Pages, a simple way to host a static website directly from GitHub. You’ll learn how to create a remote repository, store your site’s code, and deploy it to the web. Plus...
From Lab to Live: A Blueprint for a Voice-Powered AI Sales Coach
Until recently, building real-time voice AI for production was challenging. Developers faced hurdles such as managing audio streams and ensuring low-latency performance. That landscape is changing. The general availability of the Azure Voice Live API marks a turning point, providing a unified abstraction layer that simplifies the development of real-time voice and avatar experiences. This shift inspired me to build a reference implementation called the AI Sales Coach to demonstrate how these new capabilities can be applied to solve a practical business challenge: skill development. A Practical Appli...
Teaching the LLM Good Habits: How Azure MCP Uses Best-Practice Tools
Why best practices matter (and why many people skip them) Let’s be honest: when you’re in the zone shipping code or wiring up infrastructure, “best practices” can feel like that nagging voice telling you to eat your vegetables. We all know they’re good for us… but in the rush to get things working, they’re often the first thing to fall off the plate. The result? Configurations that work today but explode tomorrow. Pipelines that deploy - until they don’t. Costs that spiral out of control. And sometimes, security gaps big enough to drive a container ship through. That’s why the Azure team invests so heavi...
Build your own Microsoft Docs AI assistant with Azure Container Apps and Azure OpenAI
Learn how to deploy a self-hosted AI assistant that leverages Microsoft Learn content via the Model Context Protocol (MCP) and Azure OpenAI. It’s fast, secure, and ready for developer use in real-world apps. Prerequisite This guide assumes you already have an Azure OpenAI resource provisioned in your subscription, with a deployed model (e.g., gpt-4, gpt-4.1, or gpt-35-turbo). If not, follow the Azure OpenAI Quickstart before proceeding. The goal Imagine being able to ask Microsoft Learn, "How do I monitor AKS workloads?" or "What are best practices for Azure Bicep deployments?" and instantly get a precise, ...