All things Azure
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
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...