From Vibe Coding to Working App: How SRE Agent Completes the Developer Loop
The Most Common Challenge in Modern Cloud Apps There's a category of bugs that drive engineers crazy: multi-layer infrastructure issues. Your app deploys successfully. Every Azure resource shows "Succeeded." But the app fails at runtime with a vague error like Login failed for user ''. Where do you even start? You're checking the Web App, the SQL Server, the VNet, the private endpoint, the DNS zone, the identity configuration... and each one looks fine in isolation. The problem is how they connect and that's invisible in the portal. Networking issues are especially brutal. The error says "Login failed" but the actual causes could be DNS, firewall, identity, or all three. The symptom and the root causes are in completely different resources. Without deep Azure networking knowledge, you're just clicking around hoping something jumps out. Now imagine you vibe coded the infrastructure. You used AI to generate the Bicep, deployed it, and moved on. When it breaks, you're debugging code you didn't write, configuring resources you don't fully understand. This is where I wanted AI to help not just to build, but to debug. Enter SRE Agent + Coding Agent Here's what I used: Layer Tool Purpose Build VS Code Copilot Agent Mode + Claude Opus Generate code, Bicep, deploy Debug Azure SRE Agent Diagnose infrastructure issues and create developer issue with suggested fixes in source code (app code and IaC) Fix GitHub Coding Agent Create PRs with code and IaC fix from Github issue created by SRE Agent Copilot builds. SRE Agent debugs. Coding Agent fixes. What I Built I used VS Code Copilot in Agent Mode with Claude Opus to create a .NET 8 Web App connected to Azure SQL via private endpoint: Private networking (no public exposure) Entra-only authentication Managed identity (no secrets) Deployed with azd up. All green. Then I tested the health endpoint: $ curl https://app-tsdvdfdwo77hc.azurewebsites.net/health/sql {"status":"unhealthy","error":"Login failed for user ''.","errorType":"SqlException"} Deployment succeeded. App failed. One error. How I Fixed It: Step by Step Step 1: Create SRE Agent with Azure Access I created an SRE Agent with read access to my Azure subscription. You can scope it to specific resource groups. The agent builds a knowledge graph of your resources and their dependencies visible in the Resource Mapping view below. Step 2: Connect GitHub to SRE Agent using GitHub MCP server I connected the GitHub MCP server so the agent could read my repository and create issues. Step 3: Create Sub Agent to analyze source code I created a sub-agent for analyzing source code using GitHub mcp tools. this lets SRE Agent understand not just Azure resources, but also the Bicep and source code files that created them. "you are expert in analyzing source code (bicep and app code) from github repos" Step 4: Invoke Sub-Agent to Analyze the Error In the SRE Agent chat, I invoked the sub-agent to diagnose the error I received from my app end point. It correlated the runtime error with the infrastructure configuration Step 5: Watch the SRE Agent Think and Reason SRE Agent analyzed the error by tracing code in Program.cs, Bicep configurations, and Azure resource relationships Web App, SQL Server, VNet, private endpoint, DNS zone, and managed identity. Its reasoning process worked through each layer, eliminating possibilities one by one until it identified the root causes. Step 6: Agent Creates GitHub Issue Based on its analysis, SRE Agent summarized the root causes and suggested fixes in a GitHub issue: Root Causes: Private DNS Zone missing VNet link Managed identity not created as SQL user Suggested Fixes: Add virtualNetworkLinks resource to Bicep Add SQL setup script to create user with db_datareader and db_datawriter roles Step 7: Merge the PR from Coding Agent Assign the Github issue to Coding Agent which then creates a PR with the fixes. I just reviewed the fix. It made sense and I merged it. Redeployed with azd up, ran the SQL script: curl -s https://app-tsdvdfdwo77hc.azurewebsites.net/health/sql | jq . { "status": "healthy", "database": "tododb", "server": "tcp:sql-tsdvdfdwo77hc.database.windows.net,1433", "message": "Successfully connected to SQL Server" } đ From error to fix in minutes without manually debugging a single Azure resource. Why This Matters If you're a developer building and deploying apps to Azure, SRE Agent changes how you work: You don't need to be a networking expert. SRE Agent understands the relationships between Azure resources private endpoints, DNS zones, VNet links, managed identities. It connects dots you didn't know existed. You don't need to guess. Instead of clicking through the portal hoping something looks wrong, the agent systematically eliminates possibilities like a senior engineer would. You don't break your workflow. SRE Agent suggests fixes in your Bicep and source code not portal changes. Everything stays version controlled. Deployed through pipelines. No hot fixes at 2 AM. You close the loop. AI helps you build fast. Now AI helps you debug fast too. Try It Yourself Do you vibe code your app, your infrastructure, or both? How do you debug when things break? Here's a challenge: Vibe code a todo app with a Web App, VNet, private endpoint, and SQL database. "Forget" to link the DNS zone to the VNet. Deploy it. Watch it fail. Then point SRE Agent at it and see how it identifies the root cause, creates a GitHub issue with the fix, and hands it off to Coding Agent for a PR. Share your experience. I'd love to hear how it goes. Learn More Azure SRE Agent documentation Azure SRE Agent blogs Azure SRE Agent community Azure SRE Agent home page Azure SRE Agent pricingExtend SRE Agent with MCP: Build an Agentic Workflow to Triage Customer Issues
Your inbox is full. GitHub issues piling up. "App not working." "How do I configure alerts?" "Please add dark mode." You open each one, figure out what it is, ask for more info, add labels, route to the right team. An hour later, you're still sorting issues. Sound familiar? The Triage Tax Every L1 support engineer, PM, and on-call developer who's handled customer issues knows this pain. When tickets come in, you're not solving problems, you're sorting them. Read the issue. Is it a bug or a question? Check the docs. Does this feature exist? Ask for more info. Wait two days. Re-triage. Add labels. Route to engineering. It's tedious. It requires judgment, you need to understand the product, know what info is needed, check documentation. And honestly? It's work that nobody volunteers for but someone has to do. In large organizations, it gets even more complex. The issue doesn't just need to be triaged, it needs to be routed to the right engineering team. Is this an auth issue? Frontend? Backend? Infrastructure? A wrong routing decision means delays, re-assignments, and frustrated customers. What if an AI agent could do this for you? Enter Azure SRE Agent + MCP Here's what I built: I gave SRE Agent access to my GitHub and PagerDuty accounts via MCP, uploaded my triage rubric as a markdown file, and set it to run twice a day. No more reading every ticket manually. No more asking the same "please provide more info" questions. No more morning triage sessions. What My Setup Looks Like My app's customer issues come in through GitHub. My team uses PagerDuty to track bugs and incidents. So I connected both via MCP to the SRE Agent. I also uploaded my triage logic as a .md file on how to classify issues, what info is required for each category, which labels to use, which team handles what. And since I didn't want to run this workflow manually, I set up a scheduled task to trigger it twice a day. Now it just runs. I verify its work if I want to. What the Agent Does Fetches all open, unlabeled GitHub issues Reads each issue and classifies it (bug, doc question, feature request) Checks if required info is present Posts a comment asking for details if needed, or acknowledges the issue Adds appropriate labels Creates a PagerDuty incident for bugs ready for engineering Moves to the next issue How I Built It: Step by Step Let me walk you through exactly how I set this up inside SRE Agent. Step 1: Create an SRE Agent I created a new SRE Agent in the Azure portal. Since this workflow triages GitHub issues and not Azure resources, I didn't need to configure any Azure resource groups or subscriptions. Just an agent. Step 2: Connect MCP Servers I added two MCP servers to give the agent access to my tools: GitHub MCPâ Fetch issues, post comments, add labels PagerDuty MCP â Create incidents for bugs that need dev team's attention MCP (Model Context Protocol) lets you bring any API into the agent. If your tool has an API, you can connect it. Step 3: Create Subagents I created two focused subagents, each with a specific job and only the tools it needs: GitHub Issue Triager "You are expert in triaging GitHub issues, classifying them into categories such as user needs to supply additional information, bug, documentation question, or feature request. Use the knowledge base to search for the right document that helps you with performing this triaging. Perform all actions autonomously without waiting for user input. Hand off to Incident Creator for the issues you classified as bugs." Tools: GitHub MCP (issues, labels, comments) Incident Creator Here "You are expert in managing incidents in PagerDuty, listing services, incidents, creating incidents with all details. Once done, hand off back to GitHub Issue Triager." Tools: PagerDuty MCP (services, incidents) The handoff between them creates a workflow. They collaborate without human involvement. Step 4: Add Your Knowledge I uploaded my triage logic as a .md file to the agent's knowledge base. This is my rubric - my mental model for how to triage issues: How do I classify bugs vs. doc questions vs. feature requests? What info is required for each category? What labels do I use? When should an incident be created? Which team handles which type of issue? I wrote it down the way I'd explain it to a new teammate. The agent searches and follows it. Step 5: Add a Scheduled Task I didn't want to trigger this workflow manually every time. SRE Agent supports scheduled tasks, workflows that run automatically on a cadence. I set up a trigger to run twice a day: morning and evening. Now the workflow is fully automated. Here is the end to end automated agentic workflow to triage customer tickets. Why MCP Matters Every team uses different tools. Maybe your customer issues live in Zendesk, incidents go to ServiceNow and you use Jira or Azure DevOps. SRE Agent doesn't lock you in. With MCP, you connect to whatever tools you already use. The agent orchestrates across them. That's the extensibility model: your tools, your workflow, orchestrated by the agent. The Result Before: 2 hours every morning sorting tickets. After: By the time anyone logs in, issues are labeled, missing-info requests are posted, urgent bugs have incidents, and feature requests are acknowledged. Your team can finally focus on the complex stuff not sorting tickets. Why This Matters Faster response times. Issues get acknowledged in minutes, not days. Consistent classification. No "this should have been a P1" moments. No tickets bouncing between teams. Happier customers. They get a response immediately even if it's just "we're looking into it." Focus on what matters. Your team should be solving problems, not sorting them. The Bottom Line Triage isn't the job, it's the tax on the job. It quietly eats the hours your team could spend building, debugging, and shipping. You don't need to build a custom triage bot. You don't need to wire up webhooks and write glue code. You give the SRE agent your tools, your logic, and a schedule and it handles the sorting. Use GitHub? Connect GitHub. Use Zendesk? Connect Zendesk. PagerDuty, ServiceNow, Jira - whatever your team runs on, the agent meets you there. Stop sorting tickets. Start shipping. A Few Tips Test MCP endpoints before configuring them in the SRE agent Give each subagent only the tools it needs, don't enable everything Start read-only until you trust the classification, then enable comments Do You Still Want to Triage Issues Manually? What tools does your team use to track customer-reported issues and incidents? Let us know in the comments, we'd love to hear how you'd use this workflow with your stack. Is triage your most toilsome workflow or is there something even worse eating your team's time? Let us know in the comments.Build Long-Running AI Agents on Azure App Service with Microsoft Agent Framework
UPDATE 10/22/2025: An alternative implementation of this sample app has been added to this blog post. The alternate version uses a WebJob for background processing instead of an in-process hosted service. WebJobs are a great alternative for background processing in App Service, providing better separation of concerns, independent restarts, and dedicated logging. To learn more about WebJobs on App Service, see the Azure App Service WebJobs documentation. The AI landscape is evolving rapidly, and with the introduction of Microsoft Agent Framework, developers now have a powerful platform for building sophisticated AI agents that go far beyond simple chat completions. These agents can execute complex, multi-step workflows with persistent state, conversation threads, and structured executionâcapabilities that are essential for production AI applications. Today, we're excited to share how Azure App Service provides an excellent platform for running Agent Framework workloads, especially those involving long-running operations. Let's explore why App Service is a great choice and walk through a practical example. đ Quick link to sample app GitHub repo: https://github.com/Azure-Samples/app-service-agent-framework-travel-agent-dotnet đ Quick link to WebJob sample app GitHub repo: https://github.com/Azure-Samples/app-service-agent-framework-travel-agent-dotnet-webjob The Challenge: Long-Running Agent Framework Flows Agent Framework enables AI agents to perform complex tasks that can take significant time to complete: Multi-turn reasoning: Iterative calls to large language models (LLMs) where each response informs the next prompt Tool integration: Function calling and external API interactions for real-time data Complex processing: Budget calculations, content optimization, multi-phase generation Persistent context: Maintaining conversation state across multiple interactions These workflows often take 30 seconds to several minutes to completeâfar too long for synchronous HTTP request handling. Traditional web applications run into several constraints: âąď¸ Timeout Limitations: HTTP requests have timeout constraints (typically 30-230 seconds) â ď¸ Connection Issues: Clients may disconnect due to network interruptions or browser navigation đ Scalability Concerns: Long-running requests block worker threads and don't survive app restarts đŻ Poor User Experience: Users see endless loading spinners with no progress feedback The Solution: Async Pattern with App Service Azure App Service provides a robust solution through the asynchronous request-reply pattern combined with background processing: API immediately returns (202 Accepted) with a task ID Background worker processes the Agent Framework workflow Client polls for status with real-time progress updates Durable state storage (Cosmos DB) maintains task status and results This pattern ensures: â No HTTP timeoutsâAPI responds in milliseconds â Resilient to restartsâstate survives deployments and scale events â Progress trackingâusers see real-time updates (10%, 45%, 100%) â Better scalabilityâbackground workers process independently NOTE! This pattern can be implemented with either an in-process BackgroundService or as a separate WebJob process. Deployment Patterns: BackgroundService vs WebJob The following compares the two deployment options you have for this implementation. BackgroundService Pattern: â Simpler deployment (single project) â Shared process and memory â Good for moderate workloads â ď¸ API and worker restart together WebJob Pattern (alternative): â Separate processes (API + WebJob) â Independent restart without API downtime â Dedicated WebJob monitoring in portal â Better for production operations â ď¸ Slightly more complex deployment (manual WebJob upload) Either of these options are a great way to help you get started with implementing long-running processes on App Service. To learn more about WebJobs on App Service, see the Azure App Service WebJobs documentation. Rapid Innovation Support The AI landscape is changing at an unprecedented pace. New models, frameworks, and capabilities are released constantly. Azure App Service's managed platform ensures your applications can adapt quickly without infrastructure rewrites: Framework Updates: Deploy new Agent Framework SDK versions like any application update Model Upgrades: Switch between GPT-4, GPT-4o, or future models with configuration changes Scaling Patterns: Start with combined API+worker, split into separate apps as needs grow New Capabilities: Integrate emerging AI services without changing hosting infrastructure App Service handles the platform complexity so you can focus on building great AI experiences. Sample Application: AI Travel Planner To demonstrate this pattern, we've built a Travel Planner application that uses Agent Framework to generate detailed, multi-day travel itineraries. The agent performs complex reasoning including: Researching destination attractions and activities Optimizing daily schedules based on location proximity Calculating detailed budget breakdowns Generating personalized travel tips and recommendations The entire application runs on a single P0v4 App Service with both the API and background worker combinedâshowcasing App Service's flexibility for hosting diverse workload patterns in one deployment. Key Architecture Components Azure App Service (P0v4 Premium) Hosts both REST API and background worker in a single app "Always On" feature keeps background worker running continuously Managed identity for secure, credential-less authentication Azure Service Bus Decouples API from long-running Agent Framework processing Reliable message delivery with automatic retries Dead letter queue for error handling Azure Cosmos DB Stores task status with real-time progress updates Automatic 24-hour TTL for cleanup Rich query capabilities for complex itinerary data Azure AI Foundry Hosts persistent agents with conversation threads Structured execution with Agent Framework runtime GPT-4o model for intelligent travel planning One of the powerful features of using Azure AI Foundry with Agent Framework is the ability to inspect agents and conversation threads directly in the Azure portal. This provides valuable visibility into what's happening during execution. Viewing Agents and Threads in Azure AI Foundry When you submit a travel plan request, the application creates an agent in Azure AI Foundry. You can navigate to your AI Foundry project in the Azure portal to see: Agents The application creates an agent for each request Important: Agents are **automatically deleted** after the itinerary is generated to keep your project clean Tip: You'll need to be quick! Navigate to Azure AI Foundry right after submitting a request to see the agent in action Once processing completes, the agent is removed as part of the cleanup process Conversation Threads Unlike agents, threads persist even after the agent completes You can view the complete conversation history at any time See the exact prompts sent to the model and the responses generated Useful for debugging, understanding agent behavior, and improving prompts The ephemeral nature of agents (created per request, deleted after completion) keeps your Azure AI Foundry project clean while the persistent threads give you full traceability of every interaction. Alternative Architecture: WebJob Pattern The alternate version of this app uses a WebJob for background processing instead of an in-process hosted service. However, just a single App Service is still required. WebJobs are a great alternative for background processing in App Service, providing better separation of concerns, independent restarts, and dedicated logging. To learn more about WebJobs on App Service, see the Azure App Service WebJobs documentation. Get Started Today The complete Travel Planner application is available as a reference implementation so you can quickly get started building your own apps with Agent Framework on App Service. Try one or both of these today! đ GitHub Repository for background process version: https://github.com/Azure-Samples/app-service-agent-framework-travel-agent-dotnet đ GitHub Repository for WebJob version: https://github.com/Azure-Samples/app-service-agent-framework-travel-agent-dotnet-webjob The repo includes: Complete .NET 9 source code with Agent Framework integration Infrastructure as Code (Bicep) for automated deployment Web UI with real-time progress tracking Comprehensive README with deployment instructions Deploy in minutes: git clone https://github.com/Azure-Samples/app-service-agent-framework-travel-agent-dotnet.git cd app-service-agent-framework-travel-agent-dotnet azd auth login azd up IMPORTANT! For the WebJob version, you will also need to manually deploy the WebJob. See the instructions in the README to learn how to do this. Key Takeaways â Agent Framework enables sophisticated AI agents beyond simple chat completions â Long-running workflows (30s-minutes) require async patterns to avoid timeouts â App Service provides a simple, cost-effective platform for these workloads â Async request-reply pattern with Service Bus + Cosmos DB ensures reliability â Rapid innovation in AI is supported by App Service's adaptable platform Whether you're building travel planners, document processors, research assistants, or other AI-powered applications, Azure App Service gives you the flexibility and reliability you needâwithout the complexity of container orchestration or function programming models. What's Next? Build on This Foundation This Travel Planner is just the starting pointâa foundation to help you understand the patterns and architecture. Agent Framework is designed to grow with your needs, making it easy to add sophisticated capabilities with minimal effort: đ ď¸ Add Tool Calling Connect your agent to real-time APIs for weather, flight prices, hotel availability, and actual booking systems. Agent Framework's built-in tool calling makes this straightforward. đ¤ Implement Multi-Agent Systems Create specialized agents (flight expert, hotel specialist, activity planner) that collaborate to build comprehensive travel plans. Agent Framework handles the orchestration. đ§ Enhance with RAG Add retrieval-augmented generation to give your agent deep knowledge of destinations, local customs, and insider tips from your own content library. đ Expand Functionality Real-time pricing and availability Interactive refinement based on user feedback Personalized recommendations from past trips Multi-language support for global users The beauty of Agent Framework is that these advanced features integrate seamlessly into the pattern we've built. Start with this sample, explore the Agent Framework documentation, and unlock powerful AI capabilities for your applications! Learn More Microsoft Agent Framework Documentation Azure App Service Documentation Async Request-Reply Pattern Azure App Service WebJobs documentation Have you built AI agents on App Service? We'd love to hear about your experience! Share your thoughts in the comments below. Questions about Agent Framework on App Service? Drop a comment and our team will help you get started.
Unlocking Application Modernisation with GitHub Copilot
AI-driven modernisation is unlocking new opportunities you may not have even considered yet. It's also allowing organisations to re-evaluate previously discarded modernisation attempts that were considered too hard, complex or simply didn't have the skills or time to do. During Microsoft Build 2025, we were introduced to the concept of Agentic AI modernisation and this post from Ikenna Okeke does a great job of summarising the topic - Reimagining App Modernisation for the Era of AI | Microsoft Community Hub. This blog post however, explores the modernisation opportunities that you may not even have thought of yet, the business benefits, how to start preparing your organisation, empowering your teams, and identifying where GitHub Copilot can help. Iâve spent the last 8 months working with customers exploring usage of GitHub Copilot, and want to share what my team members and I have discovered in terms of new opportunities to modernise, transform your applications, bringing some fun back into those migrations! Letâs delve into how GitHub Copilot is helping teams update old systems, move processes to the cloud, and achieve results faster than ever before. Background: The Modernisation Challenge (Then vs Now) Modernising legacy software has always been hard. In the past, teams faced steep challenges: brittle codebases full of technical debt, outdated languages (think decades-old COBOL or VB6), sparse documentation, and original developers long gone. Integrating old systems with modern cloud services often requiring specialised skills that were in short supply â for example, check out this fantastic post from Arvi LiVigni (@arilivigni ) which talks about migrating from COBOL âthe number of developers who can read and write COBOL isnât what it used to be,â making those systems much harder to update". Common pain points included compatibility issues, data migrations, high costs, security vulnerabilities, and the constant risk that any change could break critical business functions. Itâs no wonder many modernisation projects stalled or were âput offâ due to their complexity and risk. So, whatâs different now (circa 2025) compared to two years ago? In a word: Intelligent AI assistance. Tools like GitHub Copilot have emerged as AI pair programmers that dramatically lower the barriers to modernisation. Arviâs post talks about how only a couple of years ago, developers had to comb through documentation and Stack Overflow for clues when deciphering old code or upgrading frameworks. Today, GitHub Copilot can act like an expert co-developer inside your IDE, ready to explain mysterious code, suggest updates, and even rewrite legacy code in modern languages. This means less time fighting old code and more time implementing improvements. As Arvi says ânine times out of 10 it gives me the right answer⌠That speed â and not having to break out of my flow â is really whatâs so impactful.â In short, AI coding assistants have evolved from novel experiments to indispensable tools, reimagining how we approach software updates and cloud adoption. Iâd also add from my own experience â the models we were using 12 months ago have already been superseded by far superior models with ability to ingest larger context and tackle even further complexity. It's easier to experiment, and fail, bringing more robust outcomes â with such speed to create those proof of concepts, experimentation and failing faster, this has also unlocked the ability to test out multiple hypothesisâ and get you to the most confident outcome in a much shorter space of time. Modernisation is easier now because AI reduces the heavy lifting. Instead of reading the 10,000-line legacy program alone, a developer can ask Copilot to explain what the code does or even propose a refactored version. Rather than manually researching how to replace an outdated library, they can get instant recommendations for modern equivalents. These advancements mean that tasks which once took weeks or months can now be done in days or hours â with more confidence and less drudgery - more fun! The following sections will dive into specific opportunities unlocked by GitHub Copilot across the modernisation journey which you may not even have thought of. Modernisation Opportunities Unlocked by Copilot Modernising an application isnât just about updating code â it involves bringing everyone and everything up to speed with cloud-era practices. Below are several scenarios and how GitHub Copilot adds value, with the specific benefits highlighted: 1. AI-Assisted Legacy Code Refactoring and Upgrades Instant Code Comprehension: GitHub Copilot can explain complex legacy code in plain English, helping developers quickly understand decades-old logic without scouring scarce documentation. For example, you can highlight a cryptic COBOL or C++ function and ask Copilot to describe what it does â an invaluable first step before making any changes. This saves hours and reduces errors when starting a modernisation effort. Automated Refactoring Suggestions: The AI suggests modern replacements for outdated patterns and APIs, and can even translate code between languages. For instance, Copilot can help convert a COBOL program into JavaScript or C# by recognising equivalent constructs. It also uses transformation tools (like OpenRewrite for Java/.NET) to systematically apply code updates â e.g. replacing all legacy HTTP calls with a modern library in one sweep. Developers remain in control, but GitHub Copilot handles the tedious bulk edits. Bulk Code Upgrades with AI: GitHub Copilotâs App Modernisation capabilities can analyse an entire codebase and generate a detailed upgrade plan, then execute many of the code changes automatically. It can upgrade framework versions (say from .NET Framework 4.x to .NET 6, or Java 8 to Java 17) by applying known fix patterns and even fixing compilation errors after the upgrade. Teams can finally tackle those hundreds of thousand-line enterprise applications â a task that could take multiple years with GitHub Copilot handling the repetitive changes. Technical Debt Reduction: By cleaning up old code and enforcing modern best practices, GitHub Copilot helps chip away at years of technical debt. The modernised codebase is more maintainable and stable, which lowers the long-term risk hanging over critical business systems. Notably, the tool can even scan for known security vulnerabilities during refactoring as it updates your code. In short, each legacy component refreshed with GitHub Copilot comes out safer and easier to work on, instead of remaining a brittle black box. 2. Accelerating Cloud Migration and Azure Modernisation Guided Azure Migration Planning: GitHub Copilot can assess a legacy applicationâs cloud readiness and recommend target Azure services for each component. For instance, it might suggest migrating an on-premises database to Azure SQL, moving file storage to Azure Blob Storage, and converting background jobs to Azure Functions. This provides a clear blueprint to confidently move an app from servers to Azure PaaS. One-Click Cloud Transformations: GitHub Copilot comes with predefined migration tasksthat automate the code changes required for cloud adoption. With one click, you can have the AI apply dozens of modifications across your codebase. For example: File storage: Replace local file read/writes with Azure Blob Storage SDK calls. Email/Comms: Swap out SMTP email code for Azure Communication Services or SendGrid. Identity: Migrate authentication from Windows AD to Azure AD (Entra ID) libraries. Configuration: Remove hard-coded configurations and use Azure App Configuration or Key Vault for secrets. GitHub Copilot performs these transformations consistently, following best practices (like using connection strings from Azure settings). After applying the changes, it even fixes any compile errors automatically, so youâre not left with broken builds. What used to require reading countless Azure migration guides is now handled in minutes. Automated Validation & Deployment: Modernisation doesnât stop at code changes. GitHub Copilot can also generate unit tests to validate that the application still behaves correctly after the migration. It helps ensure that your modernised, cloud-ready app passes all its checks before going live. When youâre ready to deploy, GitHub Copilot can produce the necessary Infrastructure-as-Code templates (e.g. Azure Resource Manager Bicep files or Terraform configs) and even set up CI/CD pipeline scripts for you. In other words, the AI can configure the Azure environment and deployment process end-to-end. This dramatically reduces manual effort and error, getting your app to the cloud faster and with greater confidence. Integrations: GitHub Copilot also helps tackle larger migration scenarios that were previously considered too complex. For example, many enterprises want to retire expensive proprietary integration platforms like MuleSoft or Apigee and use Azure-native services instead, but rewriting hundreds of integration workflows was daunting. Now, GitHub Copilot can assist in translating those workflows: for instance, converting an Apigee API proxy into an Azure API Management policy, or a MuleSoft integration into an Azure Logic App. Multi-Cloud Migrations: if you plan to consolidate from other clouds into Azure, GitHub Copilot can suggest equivalent Azure services and SDK calls to replace AWS or GCP-specific code. These AI-assisted conversions significantly cut down the time needed to reimplement functionality on Azure. The business impact can be substantial. By lowering the effort of such migrations, GitHub Copilot makes it feasible to pursue opportunities that deliver big cost savings and simplification. 3. Boosting Developer Productivity and Quality Instant Unit Tests (TDD Made Easy): Writing tests for old code can be tedious, but GitHub Copilot can generate unit test cases on the fly. Developers can highlight an existing function and ask Copilot to create tests; it will produce meaningful test methods covering typical and edge scenarios. This makes it practical to apply test-driven development practices even to legacy systems â you can quickly build a safety net of tests before refactoring. By catching bugs early through these AI-generated tests, teams gain confidence to modernise code without breaking things. It essentially injects quality into the process from the start, which is crucial for successful modernisation. DevOps Automation: GitHub Copilot helps modernise your build and deployment process as well. It can draft CI/CD pipeline configurations, Dockerfiles, Kubernetes manifests, and other DevOps scripts by leveraging its knowledge of common patterns. For example, when setting up a GitHub Actions workflow to deploy your app, GitHub Copilot will autocomplete significant parts (like build steps, test runs, deployment jobs) based on the project structure. This not only saves time but also ensures best practices (proper caching, dependency installation, etc.) are followed by default. Microsoft even provides an extension where you can describe your Azure infrastructure needs in plain language and have GitHub Copilot generate the corresponding templates and pipeline YAML. By automating these pieces, teams can move to cloud-based, automated deployments much faster. Behaviour-Driven Development Support: Teams practicing BDD write human-readable scenarios (e.g. using Gherkin syntax) describing application behaviour. GitHub Copilotâs AI is adept at interpreting such descriptions and suggesting step definition code or test implementations to match. For instance, given a scenario âWhen a user with no items checks out, then an error message is shown,â GitHub Copilot can draft the code for that condition or the test steps required. This helps bridge the gap between non-technical specifications and actual code. It makes BDD more efficient and accessible, because even if team members arenât strong coders, the AI can translate their intent into working code that developers can refine. Quality and Consistency: By using AI to handle boilerplate and repetitive tasks, developers can focus more on high-value improvements. GitHub Copilotâs suggestions are based on a vast corpus of code, which often means it surfaces well-structured, idiomatic patterns. Starting from these suggestions, developers are less likely to introduce errors or reinvent the wheel, which leads to more consistent code quality across the project. The AI also often reminds you of edge cases (for example, suggesting input validation or error handling code that might be missed), contributing to a more robust application. In practice, many teams find that adopting GitHub Copilot results in fewer bugs and quicker code reviews, as the code is cleaner on the first pass. Itâs like having an extra set of eyes on every pull request, ensuring standards are met. Business Benefits of AI-Powered Modernisation Bringing together the technical advantages above, whatâs the payoff for the business and stakeholders? Modernising with GitHub Copilot can yield multiple tangible and intangible benefits: Accelerated Time-to-Market: Modernisation projects that might have taken a year can potentially be completed in a few months, or an upgrade that took weeks can be done in days. This speed means you can deliver new features to customers sooner and respond faster to market changes. It also reduces downtime or disruption since migrations happen more swiftly. Cost Savings: By automating repetitive work and reducing the effort required from highly paid senior engineers, GitHub Copilot can trim development costs. Faster project completion also means lower overall project cost. Additionally, running modernised apps on cloud infrastructure (with updated code) often lowers operational costs due to more efficient resource usage and easier maintenance. Thereâs also an opportunity cost benefit: developers freed up by Copilot can work on other value-adding projects in parallel. Improved Quality & Reliability: GitHub Copilotâs contributions to testing, bug-fixing, and even security (like patching known vulnerabilities during upgrades) result in more robust applications. Modernised systems have fewer outages and security incidents than shaky legacy ones. Stakeholders will appreciate that with GitHub Copilot, modernisation doesnât mean âtrading one set of bugs for anotherâ â instead, you can increase quality as you modernise (GitHubâs research noted higher code quality when using Copilot, as developers are less likely to introduce errors or skip tests). Business Agility: A modernised application (especially one refactored for cloud) is typically more scalable and adaptable. New integrations or features can be added much faster once the platform is up-to-date. GitHub Copilot helps clear the modernisation hurdle, after which the business can innovate on a solid, flexible foundation (for example, once a monolith is broken into microservices or moved to Azure PaaS, you can iterate on it much faster in the future). AI-assisted modernisation thus unlocks future opportunities (like easier expansion, integrations, AI features, etc.) that were impractical on the legacy stack. Employee Satisfaction and Innovation: Developer happiness is a subtle but important benefit. When tedious work is handled by AI, developers can spend more time on creative tasks â designing new features, improving user experience, exploring new technologies. This can foster a culture of innovation. Moreover, being seen as a company that leverages modern tools (like AI Copilot) helps attract and retain top tech talent. Teams that successfully modernise critical systems with Copilot will gain confidence to tackle other ambitious projects, creating a positive feedback loop of improvement. To sum up, GitHub Copilot acts as a force-multiplier for application modernisation. It enables organisations to do more with less: convert legacy âboat anchorsâ into modern, cloud-enabled assets rapidly, while improving quality and developer morale. This aligns IT goals with business goals â faster delivery, greater efficiency, and readiness for the future. Call to Action: Embrace the Future of Modernisation GitHub Copilot has proven to be a catalyst for transforming how we approach legacy systems and cloud adoption. If youâre excited about the possibilities, here are next steps and what to watch for: Start Experimenting: If you havenât already, try GitHub Copilot on a sample of your code. Use Copilot or Copilot Chat to explain a piece of old code or generate a unit test. Seeing it in action on your own project can build confidence and spark ideas for where to apply it. Identify a Pilot Project: Look at your application portfolio for a candidate thatâs ripe for modernisation â maybe a small legacy service that could be moved to Azure, or a module that needs a refactor. Use GitHub Copilot to assess and estimate the effort. Often, youâll find tasks once deemed âtoo hardâ might now be feasible. Early successes will help win support for larger initiatives. Stay Tuned for Our Upcoming Blog Series: This post is just the beginning. In forthcoming posts, weâll dive deeper into: Setting Up Your Organisation for Copilot Adoption: Practical tips on preparing your enterprise environment â from licensing and security considerations to training programs. Weâll discuss best practices (like running internal awareness campaigns, defining success metrics, and creating Copilot champions in your teams) to ensure a smooth rollout. Empowering Your Colleagues: How to foster a culture that embraces AI assistance. This includes enabling continuous learning, sharing prompt techniques and knowledge bases, and addressing any scepticism. Weâll cover strategies to support developers in using Copilot effectively, so that everyone from new hires to veteran engineers can amplify their productivity. Identifying High-Impact Modernisation Areas: Guidance on spotting where GitHub Copilot can add the most value. Weâll look at different domains â code, cloud, tests, data â and how to evaluate opportunities (for example, using telemetry or feedback to find repetitive tasks suited for AI, or legacy components with high ROI if modernised). Engage and Share: As you start leveraging Copilot for modernisation, share your experiences and results. Success stories (even small wins like âGitHub Copilot helped reduce our code review timesâ or âwe migrated a component to Azure in 1 sprintâ) can build momentum within your organisation and the broader community. We invite you to discuss and ask questions in the comments or in our tech community forums. Take a look at the new App Modernisation Guidanceâa comprehensive, step-by-step playbook designed to help organisations: Understand what to modernise and why Migrate and rebuild apps with AI-first design Continuously optimise with built-in governance and observability Modernisation is a journey, and AI is the new compass and Copilot to guide the way. By embracing tools like GitHub Copilot, you position your organisation to break through modernisation barriers that once seemed insurmountable. The result is not just updated software, but a more agile, cloud-ready business and a happier, more productive development team. Now is the time to take that step. Empower your team with Copilot, and unlock the full potential of your applications and your developers. Stay tuned for more insights in our next posts, and letâs modernise whatâs possible together!Search Less, Build More: Inner Sourcing with GitHub Copilot and ADO MCP Server
Developers burn cycles contextâswitching: opening five repos to find a logging example, searching a wiki for a data masking rule, scrolling chat history for the latest pipeline pattern. Organisations that I speak to are often on the path of transformational platform engineering projects but always have the fear or doubt of "what if my engineers don't use these resources". While projects like Backstage still play a pivotal role in inner sourcing and discoverability I also empathise with developers who would argue "How would I even know in the first place, which modules have or haven't been created for reuse". In this blog we explore how we can ensure organisational standards and developer satisfaction without any heavy lifting on either side, no custom model training, no rewriting or relocating of repositories and no stagnant local data. Using GitHub Copilot + Azure DevOps MCP server (with the free `code_search` extension) we turn the IDE into an organizational knowledge interface. Instead of guessing or reâimplementing, engineers can start scaffolding projects or solving issues as they would normally (hopefully using Copilot) and without extra prompting. GitHub Copilot can lean into organisational standards and ensure recommendations are made with code snippets directly generated from existing examples. What Is the Azure DevOps MCP Server + code_search Extension? MCP (Model Context Protocol) is an open standard that lets agents (like GitHub Copilot) pull in structured, on-demand context from external systems. MCP servers contain natural language explanations of the tools that the agent can utilise allowing dynamic decision making of when to implement certain toolsets over others. The Azure DevOps MCP Server is the ADO Product Team's implementation of that standard. It exposes your ADO environment in a way Copilot can consume. Out of the box it gives you access to: Projects â list and navigate across projects in your organization. Repositories â browse repos, branches, and files. Work items â surface user stories, bugs, or acceptance criteria. Wiki's â pull policies, standards, and documentation. This means Copilot can ground its answers in live ADO content, instead of hallucinating or relying only on whatâs in the current editor window. The ADO server runs locally from your own machine to ensure that all sensitive project information remains within your secure network boundary. This also means that existing permissions on ADO objects such as Projects or Repositories are respected. Wiki search tooling available out of the box with ADO MCP server is very useful however if I am honest I have seen these wiki's go unused with documentation being stored elsewhere either inside the repository or in a project management tool. This means any tool that needs to implement code requires the ability to accurately search the code stored in the repositories themself. That is where the code_search extension enablement in ADO is so important. Most organisations have this enabled already however it is worth noting that this pre-requisite is the real unlock of cross-repo search. This allows for Copilot to: Query for symbols, snippets, or keywords across all repos. Retrieve usage examples from code, not just docs. Locate standards (like logging wrappers or retry policies) wherever they live. Back every recommendation with specific source lines. In short: MCP connects Copilot to Azure DevOps. code_search makes that connection powerful by turning it into a discovery engine. What is the relevance of Copilot Instructions? One of the less obvious but most powerful features of GitHub Copilot is its ability to follow instructions files. Copilot automatically looks for these files and uses them as a âplaybookâ for how it should behave. There are different types of instructions you can provide: Organisational instructions â apply across your entire workspace, regardless of which repo youâre in. Repo-specific instructions â scoped to a particular repository, useful when one project has unique standards or patterns. Personal instructions â smaller overrides layered on top of global rules when a local exception applies. (Stored in .github/copilot-instructions.md) In this solution, Iâm using a single personal instructions file. It tells Copilot: When to search (e.g., always query repos and wikis before answering a standards question). Where to look (Azure DevOps repos, wikis, and with code_search, the code itself). How to answer (responses must cite the repo/file/line or wiki page; if no source is found, say so). How to resolve conflicts (prefer dated wiki entries over older README fragments). As a small example, a section of a Copilot instruction file could look like this: # GitHub Copilot Instructions for Azure DevOps MCP Integration This project uses Azure DevOps with MCP server integration to provide organizational context awareness. Always check to see if the Azure DevOps MCP server has a tool relevant to the user's request. ## Core Principles ### 1. Azure DevOps Integration - **Always prioritize Azure DevOps MCP tools** when users ask about: - Work items, stories, bugs, tasks - Pull requests and code reviews - Build pipelines and deployments - Repository operations and branch management - Wiki pages and documentation - Test plans and test cases - Project and team information ### 2. Organizational Context Awareness - Before suggesting solutions, **check existing organizational patterns** by: - Searching code across repositories for similar implementations - Referencing established coding standards and frameworks - Looking for existing shared libraries and utilities - Checking architectural decision records (ADRs) in wikis ### 3. Cross-Repository Intelligence - When providing code suggestions: - **Search for existing patterns** in other repositories first - **Reference shared libraries** and common utilities - **Maintain consistency** with organizational standards - **Suggest reusable components** when appropriate ## Tool Usage Guidelines ### Work Items and Project Management When users mention bugs, features, tasks, or project planning: ``` â Use: wit_my_work_items, wit_create_work_item, wit_update_work_item â Use: wit_list_backlogs, wit_get_work_items_for_iteration â Use: work_list_team_iterations, core_list_projects The result... To test this I created 3 ADO Projects each with between 1-2 repositories. The repositories were light with only ReadMe's inside containing descriptions of the "repo" and some code snippets examples for usage. I have then created a brand-new workspace with no context apart from a Copilot instructions document (which could be part of a repo scaffold or organisation wide) which tells Copilot to search code and the wikis across all ADO projects in my demo environment. It returns guidance and standards from all available repo's and starts to use it to formulate its response. In the screenshot I have highlighted some key parts with red boxes. The first being a section of the readme that Copilot has identified in its response, that part also highlighted within CoPilot chat response. I have highlighted the rather generic prompt I used to get this response at the bottom of that window too. Above I have highlighted Copilot using the MCP server tooling searching through projects, repo's and code. Finally the largest box highlights the instructions given to Copilot on how to search and how easily these could be optimised or changed depending on the requirements and organisational coding standards. How did I implement this? Implementation is actually incredibly simple. As mentioned I created multiple projects and repositories within my ADO Organisation in order to test cross-project & cross-repo discovery. I then did the following: Enable code_search - in your Azure DevOps organization (Marketplace â install extension). Login to Azure - Use the AZ CLI to authenticate to Azure with "az login". Create vscode/mcp.json file - Snippet is provided below, the organisation name should be changed to your organisations name. Start and enable your MCP server - In the mcp.json file you should see a "Start" button. Using the snippet below you will be prompted to add your organisation name. Ensure your Copilot agent has access to the server under "tools" too. View this setup guide for full setup instructions (azure-devops-mcp/docs/GETTINGSTARTED.md at main ¡ microsoft/azure-devops-mcp) Create a Copilot Instructions file - with a search-first directive. I have inserted the full version used in this demo at the bottom of the article. Experiment with Prompts â Start generic (âHow do we secure APIs?â). Review the output and tools used and then tailor your instructions. Considerations While this is a great approach I do still have some considerations when going to production. Latency - Using MCP tooling on every request will add some latency to developer requests. We can look at optimizing usage through copilot instructions to better identify when Copilot should or shouldn't use the ADO MCP server. Complex Projects and Repositories - While I have demonstrated cross project and cross repository retrieval my demo environment does not truly simulate an enterprise ADO environment. Performance should be tested and closely monitored as organisational complexity increases. Public Preview - The ADO MCP server is moving quickly but is currently still public preview. We have demonstrated in this article how quickly we can make our Azure DevOps content discoverable. While their are considerations moving forward this showcases a direction towards agentic inner sourcing. Feel free to comment below how you think this approach could be extended or augmented for other use cases! Resources MCP Server Config (/.vscode/mcp.json) { "inputs": [ { "id": "ado_org", "type": "promptString", "description": "Azure DevOps organization name (e.g. 'contoso')" } ], "servers": { "ado": { "type": "stdio", "command": "npx", "args": ["-y", "@azure-devops/mcp", "${input:ado_org}"] } } } Copilot Instructions (/.github/copilot-instructions.md) # GitHub Copilot Instructions for Azure DevOps MCP Integration This project uses Azure DevOps with MCP server integration to provide organizational context awareness. Always check to see if the Azure DevOps MCP server has a tool relevant to the user's request. ## Core Principles ### 1. Azure DevOps Integration - **Always prioritize Azure DevOps MCP tools** when users ask about: - Work items, stories, bugs, tasks - Pull requests and code reviews - Build pipelines and deployments - Repository operations and branch management - Wiki pages and documentation - Test plans and test cases - Project and team information ### 2. Organizational Context Awareness - Before suggesting solutions, **check existing organizational patterns** by: - Searching code across repositories for similar implementations - Referencing established coding standards and frameworks - Looking for existing shared libraries and utilities - Checking architectural decision records (ADRs) in wikis ### 3. Cross-Repository Intelligence - When providing code suggestions: - **Search for existing patterns** in other repositories first - **Reference shared libraries** and common utilities - **Maintain consistency** with organizational standards - **Suggest reusable components** when appropriate ## Tool Usage Guidelines ### Work Items and Project Management When users mention bugs, features, tasks, or project planning: ``` â Use: wit_my_work_items, wit_create_work_item, wit_update_work_item â Use: wit_list_backlogs, wit_get_work_items_for_iteration â Use: work_list_team_iterations, core_list_projects ``` ### Code and Repository Operations When users ask about code, branches, or pull requests: ``` â Use: repo_list_repos_by_project, repo_list_pull_requests_by_repo â Use: repo_list_branches_by_repo, repo_search_commits â Use: search_code for finding patterns across repositories ``` ### Documentation and Knowledge Sharing When users need documentation or want to create/update docs: ``` â Use: wiki_list_wikis, wiki_get_page_content, wiki_create_or_update_page â Use: search_wiki for finding existing documentation ``` ### Build and Deployment When users ask about builds, deployments, or CI/CD: ``` â Use: pipelines_get_builds, pipelines_get_build_definitions â Use: pipelines_run_pipeline, pipelines_get_build_status ``` ## Response Patterns ### 1. Discovery First Before providing solutions, always discover organizational context: ``` "Let me first check what patterns exist in your organization..." â Search code, check repositories, review existing work items ``` ### 2. Reference Organizational Standards When suggesting code or approaches: ``` "Based on patterns I found in your [RepositoryName] repository..." "Following your organization's standard approach seen in..." "This aligns with the pattern established in [TeamName]'s implementation..." ``` ### 3. Actionable Integration Always offer to create or update Azure DevOps artifacts: ``` "I can create a work item for this enhancement..." "Should I update the wiki page with this new pattern?" "Let me link this to the current iteration..." ``` ## Specific Scenarios ### New Feature Development 1. **Search existing repositories** for similar features 2. **Check architectural patterns** and shared libraries 3. **Review related work items** and planning documents 4. **Suggest implementation** based on organizational standards 5. **Offer to create work items** and documentation ### Bug Investigation 1. **Search for similar issues** across repositories and work items 2. **Check related builds** and recent changes 3. **Review test results** and failure patterns 4. **Provide solution** based on organizational practices 5. **Offer to create/update** bug work items and documentation ### Code Review and Standards 1. **Compare against organizational patterns** found in other repositories 2. **Reference coding standards** from wiki documentation 3. **Suggest improvements** based on established practices 4. **Check for reusable components** that could be leveraged ### Documentation Requests 1. **Search existing wikis** for related content 2. **Check for ADRs** and technical documentation 3. **Reference patterns** from similar projects 4. **Offer to create/update** wiki pages with findings ## Error Handling If Azure DevOps MCP tools are not available or fail: 1. **Inform the user** about the limitation 2. **Provide alternative approaches** using available information 3. **Suggest manual steps** for Azure DevOps integration 4. **Offer to help** with configuration if needed ## Best Practices ### Always Do: - â Search organizational context before suggesting solutions - â Reference existing patterns and standards - â Offer to create/update Azure DevOps artifacts - â Maintain consistency with organizational practices - â Provide actionable next steps ### Never Do: - â Suggest solutions without checking organizational context - â Ignore existing patterns and implementations - â Provide generic advice when specific organizational context is available - â Forget to offer Azure DevOps integration opportunities --- **Remember: The goal is to provide intelligent, context-aware assistance that leverages the full organizational knowledge base available through Azure DevOps while maintaining development efficiency and consistency.**Industry-Wide Certificate Changes Impacting Azure App Service Certificates
Executive Summary In early 2026, industry-wide changes mandated by browser applications and the CA/B Forum will affect both how TLS certificates are issued as well as their validity period. The CA/B Forum is a vendor body that establishes standards for securing websites and online communications through SSL/TLS certificates. Azure App Service is aligning with these standards for both App Service Managed Certificates (ASMC, free, DigiCert-issued) and App Service Certificates (ASC, paid, GoDaddy-issued). Most customers will experience no disruption. Action is required only if you pin certificates or use them for client authentication (mTLS). Who Should Read This? App Service administrators Security and compliance teams Anyone responsible for certificate management or application security Quick Reference: Whatâs Changing & What To Do Topic ASMC (Managed, free) ASC (GoDaddy, paid) Required Action New Cert Chain New chain (no action unless pinned) New chain (no action unless pinned) Remove certificate pinning Client Auth EKU Not supported (no action unless cert is used for mTLS) Not supported (no action unless cert is used for mTLS) Transition from mTLS Validity No change (already compliant) Two overlapping certs issued for the full year None (automated) If you do not pin certificates or use them for mTLS, no action is required. Timeline of Key Dates Date Change Action Required Mid-Jan 2026 and after ASMC migrates to new chain ASMC stops supporting client auth EKU Remove certificate pinning if used Transition to alternative authentication if the certificate is used for mTLS Mar 2026 and after ASC validity shortened ASC migrates to new chain ASC stops supporting client auth EKU Remove certificate pinning if used Transition to alternative authentication if the certificate is used for mTLS Actions Checklist For All Users Review your use of App Service certificates. If you do not pin these certificates and do not use them for mTLS, no action is required. If You Pin Certificates (ASMC or ASC) Remove all certificate or chain pinning before their respective key change dates to avoid service disruption. See Best Practices: Certificate Pinning. If You Use Certificates for Client Authentication (mTLS) Switch to an alternative authentication method before their respective key change dates to avoid service disruption, as client authentication EKU will no longer be supported for these certificates. See Sunsetting the client authentication EKU from DigiCert public TLS certificates. See Set Up TLS Mutual Authentication - Azure App Service Details & Rationale Why Are These Changes Happening? These updates are required by major browser programs (e.g., Chrome) and apply to all public CAs. They are designed to enhance security and compliance across the industry. Azure App Service is automating updates to minimize customer impact. Whatâs Changing? New Certificate Chain Certificates will be issued from a new chain to maintain browser trust. Impact: Remove any certificate pinning to avoid disruption. Removal of Client Authentication EKU Newly issued certificates will not support client authentication EKU. This change aligns with Google Chromeâs root program requirements to enhance security. Impact: If you use these certificates for mTLS, transition to an alternate authentication method. Shortening of Certificate Validity Certificate validity is now limited to a maximum of 200 days. Impact: ASMC is already compliant; ASC will automatically issue two overlapping certificates to cover one year. No billing impact. Frequently Asked Questions (FAQs) Will I lose coverage due to shorter validity? No. For App Service Certificate, App Service will issue two certificates to span the full year you purchased. Is this unique to DigiCert and GoDaddy? No. This is an industry-wide change. Do these changes impact certificates from other CAs? Yes. These changes are an industry-wide change. We recommend you reach out to your certificatesâ CA for more information. Do I need to act today? If you do not pin or use these certs for mTLS, no action is required. Glossary ASMC: App Service Managed Certificate (free, DigiCert-issued) ASC: App Service Certificate (paid, GoDaddy-issued) EKU: Extended Key Usage mTLS: Mutual TLS (client certificate authentication) CA/B Forum: Certification Authority/Browser Forum Additional Resources Changes to the Managed TLS Feature Set Up TLS Mutual Authentication Azure App Service Best Practices â Certificate pinning DigiCert Root and Intermediate CA Certificate Updates 2023 Sunsetting the client authentication EKU from DigiCert public TLS certificates Feedback & Support If you have questions or need help, please visit our official support channels or the Microsoft Q&A, where our team and the community can assist you.Security Where It Matters: Runtime Context and AI Fixes Now Integrated in Your Dev Workflow
Security teams and developers face the same frustrating cycle: thousands of alerts, limited time, and no clear way to know which issues matter most. Applications suffer attacks as quickly as once every three minutes, 1 emphasizing the importance of proactive security that prioritizes critical, exploitable vulnerabilities. Microsoft is leading this shift with new integrations in the end-to-end solution that combines GitHub Advanced Securityâs developer-first application security tool with Microsoft Defender for Cloud's runtime protection, enhanced by agentic remediation. Now available in public preview. This integration empowers organizations to secure code to cloud and accelerates tackling of security issues in their software portfolio using agentic remediation and runtime context-based vulnerability prioritization. The result: fewer distractions, faster fixes, better collaboration and more proactive security from code to cloud. The DevSecOps Dilemmaâ too many alerts, not enough action Over the past decade, the application security industry has made significant strides in improving detection accuracy and fostering collaboration between security teams and developers. These advances have enabled both groups to work together on real issues and drive meaningful progress. However, despite these improvements, remediation trends across the industry have remained stagnant. Quarter after quarter, year after year, vulnerability counts continue to rise with critical / high vulnerabilities constituting 17.4% of vulnerability backlogs and a mean-time-to-remediation (MTTR) of 116 days 2 Today, three big challenges slow teams down: Security teams are drowning in alert fatigue, struggling to distinguish real, exploitable risks from noise. At the same time, AI is rapidly introducing new threat vectors that defenders have little time to research or understandâleaving organizations vulnerable to missed threats and evolving attack techniques. Developers lack clear prioritization while remediation takes long, so they lose time fixing issues that may never be exploited. Remediation cycles are slow, leaving systems exposed to potential attacks while teams debate which issues matter most or search for the right person to fix them Both teams rely on separate, non-integrated tools, making collaboration slow and frustrating. Development and security teams frequently operate in silos, reducing efficiency and creating blind spots. This leads to wasted time, unresolved threats, and growing backlogs. Teams are stuck reacting to noise instead of solving real problems. DevSecOps reimagined in the era of AI Your app is live and serving thousands of customers. Defender for Cloud detects a vulnerability in an internet-facing API that handles sensitive data. In the past, this alert would age in a dashboard while developers worked on unrelated fixes because they didnât know this was the critical one. Now, with the new integration, a security campaign can be created in GitHub filtering for runtime risk (internet exposed, sensitive data etc.) notifying the developer to prioritize this issue. The developer views the issue in their workflow, understands why it matters, and uses Copilot Autofix to apply an AI-suggested fix in minutes. The developer can then select these risks at bulk and assign the GitHub Copilot coding agent to create a draft PR for a multi merge fix ready for human review. Virtual Registry: Code-to-Runtime Mapping Code to runtime mapping is possible with the Virtual Registry which makes GitHub a trusted source for artifact metadata. Integrated with Microsoft Defender for Cloud, the Virtual Registry enables smarter risk prioritization and faster incident response. Teams can quickly answer: Is this vulnerability running in production? Is it exposed to sensitive workloads? Do I need to act now? By combining runtime and repository context, the Virtual Registry streamlines alert triage and incident response. We shipped a new set of filters to both Code Scanning and Dependabot and Security Campaigns that are based on the artifact metadata that is stored in the Virtual Registry. Faster fixes with agentic remediation The integration includes Copilot Autofix, an AI-powered tool that suggests code changes to fix security problems. It checks that the fixes work and helps developers resolve issues quickly, without switching tools. To complete the agentic work flow we can be bulk assign these autofixes to GitHub Copilot Coding agent to create a draft Pull Request awaiting human review. Why this matters Fewer alerts to sort through: Focus only on whatâs exploitable in production. Faster fixes: AI-powered fix suggestions through GitHub Copilot Autofix have shown to fix 50% of alerts within the PR with a 70% reduction in mean time-to-remediation 3 Better teamwork: Developers and security teams collaborate seamlessly. With collaborative security now powered by connected context, weâve seen 68% of alert remediated using GitHub Advanced Securityâs security campaigns. 3 Try it now This feature is available in public preview and will be showcased at Microsoft Ignite. If your team builds cloud-native applications, this integration helps you protect code to cloud more effectivelyâwithout slowing down development. Customer FAQs How do I start using the integration? From Microsoft Defender for Cloud: Go to the environment section in the Defender for Cloud portal. Grant a new GitHub connector or update an existing one to provide consent to scan your source code. If you use GitHub, setup is one click. Youâll immediately see initial scan results and recommended fixes. From GitHub: You will be able to filter alerts by runtime context in addition to receiving AI-suggested fixes. How do I purchase this integration? For GitHub: GitHub Advanced Security (GHAS) is available as: Code Security SKU: $30 per committer/month (available April 2025) GHAS Bundle: $49 per committer/month (available now) GitHub Enterprise Cloud GitHub Copilot For Microsoft Defender for Cloud CSPM: Defender CSPM: $5 per billable resource/month Both can be enabled through the Azure Portal as Azure meters. [1]: Software Under Siege | AppSec Threat Report 2025 | Contrast Security [2]: Edgescan | Vulnerability Statistics Report 2025 [3]: GitHub Internal DataReimagining AI Ops with Azure SRE Agent: New Automation, Integration, and Extensibility features
Azure SRE Agent offers intelligent and context aware automation for IT operations. Enhanced by customer feedback from our preview, the SRE Agent has evolved into an extensible platform to automate and manage tasks across Azure and other environments. Built on an Agentic DevOps approach - drawing from proven practices in internal Azure operations - the Azure SRE Agent has already saved over 20,000 engineering hours across Microsoft product teams operations, delivering strong ROI for teams seeking sustainable AIOps. An Operations Agent that adapts to your playbooks Azure SRE Agent is an AI powered operations automation platform that empowers SREs, DevOps, IT operations, and support teams to automate tasks such as incident response, customer support, and developer operations from a single, extensible agent. Its value proposition and capabilities have evolved beyond diagnosis and mitigation of Azure issues, to automating operational workflows and seamless integration with the standards and processes used in your organization. SRE Agent is designed to automate operational work and reduce toil, enabling developers and operators to focus on high-value tasks. By streamlining repetitive and complex processes, SRE Agent accelerates innovation and improves reliability across cloud and hybrid environments. In this article, we will look at whatâs new and what has changed since the last update. Whatâs New: Automation, Integration, and Extensibility Azure SRE Agent just got a major upgrade. From no-code automation to seamless integrations and expanded data connectivity, hereâs whatâs new in this release: No-code Sub-Agent Builder: Rapidly create custom automations without writing code. Flexible, event-driven triggers: Instantly respond to incidents and operational changes. Expanded data connectivity: Unify diagnostics and troubleshooting across more data sources. Custom actions: Integrate with your existing tools and orchestrate end-to-end workflows via MCP. Prebuilt operational scenarios: Accelerate deployment and improve reliability out of the box. Unlike generic agent platforms, Azure SRE Agent comes with deep integrations, prebuilt tools, and frameworks specifically for IT, DevOps, and SRE workflows. This means you can automate complex operational tasks faster and more reliably, tailored to your organizationâs needs. Sub-Agent Builder: Custom Automation, No Code Required Empower teams to automate repetitive operational tasks without coding expertise, dramatically reducing manual workload and development cycles. This feature helps address the need for targeted automation, letting teams solve specific operational pain points without relying on one-size-fits-all solutions. Modular Sub-Agents: Easily create custom sub-agents tailored to your teamâs needs. Each sub-agent can have its own instructions, triggers, and toolsets, letting you automate everything from outage response to customer email triage. Prebuilt System Tools: Eliminate the inefficiency of creating basic automation from scratch, and choose from a rich library of hundreds of built-in tools for Azure operations, code analysis, deployment management, diagnostics, and more. Custom Logic: Align automation to your unique business processes by defining your automation logic and prompts, teaching the agent to act exactly as your workflow requires. Flexible Triggers: Automate on Your Terms Invoke the agent to respond automatically to mission-critical events, not wait for manual commands. This feature helps speed up incident response and eliminate missed opportunities for efficiency. Multi-Source Triggers: Go beyond chat-based interactions, and trigger the agent to automatically respond to Incident Management and Ticketing systems like PagerDuty and ServiceNow, Observability Alerting systems like Azure Monitor Alerts, or even on a cron-based schedule for proactive monitoring and best-practices checks. Additional trigger sources such as GitHub issues, Azure DevOps pipelines, email, etc. will be added over time. This means automation can start exactly when and where you need it. Event-Driven Operations: Integrate with your CI/CD, monitoring, or support systems to launch automations in response to real-world events - like deployments, incidents, or customer requests. Vital for reducing downtime, it ensures that business-critical actions happen automatically and promptly. Expanded Data Connectivity: Unified Observability and Troubleshooting Integrate data, enabling comprehensive diagnostics and troubleshooting and faster, more informed decision-making by eliminating silos and speeding up issue resolution. Multiple Data Sources: The agent can now read data from Azure Monitor, Log Analytics, and Application Insights based on its Azure role-based access control (RBAC). Additional observability data sources such as Dynatrace, New Relic, Datadog, and more can be added via the Remote Model Context Protocol (MCP) servers for these tools. This gives you a unified view for diagnostics and automation. Knowledge Integration: Rather than manually detailing every instruction in your prompt, you can upload your Troubleshooting Guide (TSG) or Runbook directly, allowing the agent to automatically create an execution plan from the file. You may also connect the agent to resources like SharePoint, Jira, or documentation repositories through Remote MCP servers, enabling it to retrieve needed files on its own. This approach utilizes your organizationâs existing knowledge base, streamlining onboarding and enhancing consistency in managing incidents. Azure SRE Agent is also building multi-agent collaboration by integrating with PagerDuty and Neubird, enabling advanced, cross-platform incident management and reliability across diverse environments. Custom Actions: Automate Anything, Anywhere Extend automation beyond Azure and integrate with any tool or workflow, solving the problem of limited automation scope and enabling end-to-end process orchestration. Out-of-the-Box Actions: Instantly automate common tasks like running azcli, kubectl, creating GitHub issues, or updating Azure resources, reducing setup time and operational overhead. Communication Notifications: The SRE Agent now features built-in connectors for Outlook, enabling automated email notifications, and for Microsoft Teams, allowing it to post messages directly to Teams channels for streamlined communication. Bring Your Own Actions: Drop in your own Remote MCP servers to extend the agentâs capabilities to any custom tool or workflow. Future-proof your agentic DevOps by automating proprietary or emerging processes with confidence. Prebuilt Operations Scenarios Address common operational challenges out of the box, saving teams time and effort while improving reliability and customer satisfaction. Incident Response: Minimize business impact and reduce operational risk by automating detection, diagnosis, and mitigation of your workload stack. The agent has built-in runbooks for common issues related to many Azure resource types including Azure Kubernetes Service (AKS), Azure Container Apps (ACA), Azure App Service, Azure Logic Apps, Azure Database for PostgreSQL, Azure CosmosDB, Azure VMs, etc. Support for additional resource types is being added continually, please see product documentation for the latest information. Root Cause Analysis & IaC Drift Detection: Instantly pinpoint incident causes with AI-driven root cause analysis including automated source code scanning via GitHub and Azure DevOps integration. Proactively detect and resolve infrastructure drift by comparing live cloud environments against source-controlled IaC, ensuring configuration consistency and compliance. Handle Complex Investigations: Enable the deep investigation mode that uses a hypothesis-driven method to analyze possible root causes. It collects logs and metrics, tests hypotheses with iterative checks, and documents findings. The process delivers a clear summary and actionable steps to help teams accurately resolve critical issues. Incident Analysis: The integrated dashboard offers a comprehensive overview of all incidents managed by the SRE Agent. It presents essential metrics, including the number of incidents reviewed, assisted, and mitigated by the agent, as well as those awaiting human intervention. Users can leverage aggregated visualizations and AI-generated root cause analyses to gain insights into incident processing, identify trends, enhance response strategies, and detect areas for improvement in incident management. Inbuilt Agent Memory: The new SRE Agent Memory System transforms incident response by institutionalizing the expertise of top SREs - capturing, indexing, and reusing critical knowledge from past incidents, investigations, and user guidance. Benefit from faster, more accurate troubleshooting, as the agent learns from both successes and mistakes, surfacing relevant insights, runbooks, and mitigation strategies exactly when needed. This system leverages advanced retrieval techniques and a domain-aware schema to ensure every on-call engagement is smarter than the last, reducing mean time to resolution (MTTR) and minimizing repeated toil. Automatically gain a continuously improving agent that remembers what works, avoids past pitfalls, and delivers actionable guidance tailored to the environment. GitHub Copilot and Azure DevOps Integration: Automatically triage, respond to, and resolve issues raised in GitHub or Azure DevOps. Integration with modern development platforms such as GitHub Copilot coding agent increases efficiency and ensures that issues are resolved faster, reducing bottlenecks in the development lifecycle. Ready to get started? Azure SRE Agent home page Product overview Pricing Page Pricing Calculator Pricing Blog Demo recordings Deployment samples Whatâs Next? Give us feedback: Your feedback is critical - You can Thumbs Up / Thumbs Down each interaction or thread, or go to the âGive Feedbackâ button in the agent to give us in-product feedback - or you can create issues or just share your thoughts in our GitHub repo at https://github.com/microsoft/sre-agent. Weâre just getting started. In the coming months, expect even more prebuilt integrations, expanded data sources, and new automation scenarios. We anticipate continuous growth and improvement throughout our agentic AI platforms and services to effectively address customer needs and preferences. Let us know what Ops toil you want to automate next!
