Proactive Cloud Ops with SRE Agent: Scheduled Checks for Cloud Optimization
The Cloud Optimization Challenge Your cloud environment is always changing: New features ship weekly Traffic patterns shift seasonally Costs creep up quietly Security best practices evolve Teams spin up resources and forget them It's Monday morning. You open the Azure portal. Everything looks... fine. But "fine" isn't great. That VM has been at 8% CPU for weeks. A Key Vault secret expires in 12 days. Nothing's broken. But security is drifting, costs are creeping, and capacity gaps are growing silently. The question isn't "is something broken?" it's "could this be better?" Four Pillars of Cloud Optimization Pillar What Teams Want The Challenge Security Stay compliant, reduce risk Config drift, legacy settings, expiring creds Cost Spend efficiently, justify budget Hard to spot waste across 100s of resources Performance Meet SLOs, handle growth Know when to scale before demand hits Availability Maximize uptime, build resilience Hidden dependencies, single points of failure Most teams check these sometimes. SRE Agent checks them continuously. Enter SRE Agent + Scheduled tasks SRE Agent can pull data from Azure Monitor, resource configurations, metrics, logs, traces, errors, cost data and analyze it on a schedule. If you use tools outside Azure (Datadog, PagerDuty, Splunk), you can connect those via MCP servers so the agent sees your full observability stack. My setup uses Azure-native sources. Here's how I wired it up. How I Set It Up: Step by Step Step 1: Create SRE Agent with Subscription Access I created an SRE Agent without attaching it to any specific resource group. Instead, I gave it Reader access at the subscription level. This lets the agent scan across all my resource groups for optimization opportunities. No resource group configuration needed. The agent builds a knowledge graph of everything VMs, storage accounts, Key Vaults, NSGs, web apps across the subscription. Step 2: Create and Upload My Organization Practices I created an org-practices.md file that defines what "good" looks like for my team: I uploaded this to SRE Agent's knowledge base. Now the agent knows our bar, not just Azure defaults. đ See my full org-practices.md Source repos for this demo: security-demoapp - App with intentional security misconfigurations costoptimizationapp - App with cost optimization opportunities Step 3: Connect to Teams Channel I connected SRE Agent to my team's Teams channel so findings land where we already work. Critical findings get immediate notifications. Warnings go into a daily digest. No more logging into separate dashboards. The insights come to us. Step 4: Connect Resource Groups to GitHub Repos Add the two resource groups to the SRE Agent and link the apps to their corresponding GitHub repos: Resource Group GitHub Repository rg-security-opt-demo security-demoapp rg-cost-opt-sreademo costoptimizationapp This enables the agent to create GitHub issues for findings linking violations directly to the repo responsible for that infrastructure. Step 5: Test with Prompts Before setting up automation, I tested the agent with manual prompts to make sure it was finding the right issues. The agent ran the checks, compared against my org-practices.md, and identified the issues. Security Check: Scan resource group "rg-security-opt-demo" for any violations of our security practices defined in org-practices.md in your knowledge base. list violations with severity and remediation steps. Make sure to check against all critical requirements and send message in teams channel with your findings and create an issue in the github repo https://github.com/dm-chelupati/security-demoapp.git Cost Check: Scan resource group "rg-cost-opt-sreademo" for any violations of our costpractices defined in org-practices.md in your knowledge base. list violations with severity and remediation steps. Make sure to check against all critical requirements and send message in teams channel with your findings and create an issue in the github repo https://github.com/dm-chelupati/costoptimizationapp.git Step 6: Check Output via GitHub Issues After running prompts, I checked GitHub. The agent had created issues. Each issue has the root cause, impact, and fix ready for the team to action or for Coding Agent to pick up and create a PR. đ See the actual issues created: Security findings issue Cost findings issue Step 7: Set Up Scheduled Triggers This is where it gets powerful. I configured recurring schedules: Weekly Security Check (Wednesdays 8 AM): Create a scheduled trigger that performs security practices checks against the org practices in knowledge base org-practices.md, creates github issue and send teams message on a weekly basis Wednesdays at 8 am UTC Weekly Cost Review (Mondays 8 AM): Create a scheduled trigger that performs cost practices checks against the org practices in knowledge base org-practices.md, creates github issue and send teams message on a weekly basis on Mondays at 8 am UTC Now optimization runs automatically. Every week, fresh findings land in GitHub Issues and Teams. Why Context Makes the SRE Agent Powerful Think about hiring a new SRE. They're excellent at their craftâthey know Kubernetes, networking, Azure inside out. But on day one, they can't solve problems in your environment yet. Why? They don't have context: What are your SLOs? What's "acceptable" latency for your app? When do you rotate secrets? Monthly? Quarterly? Before each release? Which resources are production-critical vs. dev experiments? What's your tagging policy? Who owns what? How do you deploy? GitOps? Pipelines? Manual approvals? A great engineer becomes your great engineer once they learn how your team operates. SRE Agent works the same way. Out of the box, it knows Azure resource types, networking, best practices. But it doesn't know your bar. Is 20% CPU utilization acceptable or wasteful? Should secrets expire in 30 days or 90? Are public endpoints ever okay, or never? The more context you give the agent, your SLOs, your runbooks, your policies, the more it reasons like a team member who understands your environment, not just Azure in general. That's why Step 2 matters so much. When I uploaded our standards, the agent stopped checking generic Azure best practices and started checking our best practices. Bring your existing knowledge: You don't have to start from scratch. If your team's documentation already lives in Atlassian Confluence, SharePoint, or other tools, you can connect those via MCP servers. The agent pulls context from where your team already works, no need to duplicate content. Why This Matters Before this setup, optimization was a quarterly thing. Now it happens automatically: Before After Check security when audit requests it Daily automated posture check Find waste when finance complains Weekly savings report in Teams Discover capacity issues during incidents Scheduled headroom analysis Expire credentials and debug at 2 AM 30-day warning with exact secret names Optimization isn't a project anymore. It's a practice. Try It Yourself Create an SRE Agent with access to your subscription Upload your team's standards (security policies, cost thresholds, tagging rules) Set up a scheduled trigger, start with a daily security check Watch the first report land in Teams See what you've been missing while everything looked "fine." Learn More Azure SRE Agent documentation Azure SRE Agent blogs Azure SRE Agent community Azure SRE Agent home page Azure SRE Agent pricing Azure SRE Agent is currently in preview. Get StartedAnnouncing the Public Preview of the New App Service Quota Self-Service Experience
Update 10/30/2025: The App Service Quota Self-Service experience is back online after a short period where we were incorporating your feedback and making needed updates. As this is public preview, availability and features are subject to change as we receive and incorporate feedback. Whatâs New? The updated experience introduces a dedicated App Service Quota blade in the Azure portal, offering a streamlined and intuitive interface to: View current usage and limits across the various SKUs Set custom quotas tailored to your App Service plan needs This new experience empowers developers and IT admins to proactively manage resources, avoid service disruptions, and optimize performance. Quick Reference - Start here! If your deployment requires quota for ten or more subscriptions, then file a support ticket with problem type Quota following the instructions at the bottom of this post. If any subscription included in your request requires zone redundancy (note that most Isolated v2 deployments require ZR), then file a support ticket with problem type Quota following the instructions at the bottom of this post. Otherwise, leverage the new self-service experience to increase your quota automatically. Self-service Quota Requests For non-zone-redundant needs, quota alone is sufficient to enable App Service deployment or scale-out. Follow the provided steps to place your request. 1. Navigate to the Quotas resource provider in the Azure portal 2. Select App Service (Pubic Preview) Navigating the primary interface: Each App Service VM size is represented as a separate SKU. If the intention is to be able to scale up or down within a specific offering (e.g., Premium v3), then equivalent number of VMs need to be requested for each applicable size of that offering (e.g., request 5 instances for both P1v3 and P3v3). As with other quotas, you can filter by region, subscription, provider, or usage. Note that your portal will now show "App Service (Public Preview)" for the Provider name. You can also group the results by usage, quota (App Service VM type), or location (region). Current usage is represented as App Service VMs. This allows you to quickly identify which SKUs are nearing their quota limits. Adjustments can be made inline: no need to visit another page. This is covered in detail in the next section. Total Regional VMs: There is a SKU in each region called Total Regional VMs. This SKU summarizes your usage and available quota across all individual SKUs in that region. There are three key points about using Total Regional VMs. You should never request Total Regional VMs quota directly - it will automatically increase in response to your request for individual SKU quota. If you are unable to deploy a given SKU, then you must request more quota for that SKU to unblock deployment. For your deployment to succeed, you must have sufficient quota in the individual SKU as well as Total Regional VMs. If either usage is at its respective limit, then you will be unable to deploy and must request more of that individual SKU's quota to proceed. In some regions, Total Regional VMs appears as "0 of 0" usage and limit and no individual SKU quotas are shown. This is an indication that you should not interact with the portal to resolve any quota-related issues in this region. Instead, you should try the deployment and observe any error messages that arise. If any error messages indicate more quota is needed, then this must be requested by filing a support ticket with problem type Quota following the instructions at the bottom of this post so that App Service can identify and fix any potential quota issues. In most cases, this will not be necessary, and your deployment will work without requesting quota wherever "0 of 0" is shown for Total Regional VMs and no individual SKU quotas are visible. See the example below: 3. Request quota adjustments Clicking the pen icon opens a flyout window to capture the quota request: The quota type (App Service SKU) is already populated, along with current usage. Note that your request is not incremental: you must specify the new limit that you wish to see reflected in the portal. For example, to request two additional instances of P1v2 VMs, you would file the request like this: Click submit to send the request for automatic processing. How quota approvals work: Immediately upon submitting a quota request, you will see a processing dialog like the one shown: If the quota request can be automatically fulfilled, then no support request is needed. You should receive this confirmation within a few minutes of submission: If the request cannot be automatically fulfilled, then you will be given the option to file a support request with the same information. In the example below, the requested new limit exceeds what can be automatically granted for the region: 4. If applicable, create support ticket When creating a support ticket, you will need to repopulate the Region and App Service plan details; the new limit has already been populated for you. If you forget the region or SKU that was requested, you can reference them in your notifications pane: If you choose to create a support ticket, then you will interact with the capacity management team for that region. This is a 24x7 service, so requests may be created at any time. Once you have filed the support request, you can track its status via the Help + support dashboard. Known issues The self-service quota request experience for App Service is in public preview. Here are some caveats worth mentioning while the team finalizes the release for general availability: Closing the quota request flyout window will stop meaningful notifications for that request. You can still view the outcome of your quota requests by checking actual quota, but if you want to rely on notifications for alerts, then we recommend leaving the quota request window open for the few minutes that it is processing. Some SKUs are not yet represented in the quota dashboard. These will be added later in the public preview. The Activity Log does not currently provide a meaningful summary of previous quota requests and their outcomes. This will also be addressed during the public preview. As noted in the walkthrough, the new experience does not enable zone-redundant deployments. Quota is an inherently regional construct, and zone-redundant enablement requires a separate step that can only be taken in response to a support ticket being filed. Quota API documentation is being drafted to enable bulk non-zone redundant quota requests without requiring you to file a support ticket. Filing a Support Ticket If your deployment requires zone redundancy or contains many subscriptions, then we recommend filing a support ticket with issue type "Technical" and problem type "Quota": We want your feedback! If you notice any aspect of the experience that does not work as expected, or you have feedback on how to make it better, please use the comments below to share your thoughts!AI Transcription & Text Analytics for Health
Industry Challenge Healthcare organizations depend on qualitative research, patient interviews, and clinical documentation to improve care delivery. Traditional transcription services often create bottlenecks: Manual Processes: Require manual uploads and lack automation. Delayed Turnaround: Transcripts can take days, slowing research and decision-making. Limited Integration: Minimal interoperability with EMR systems or analytics platforms. Cost Inefficiencies: Pricing models that scale poorly for large volumes. The need for real-time, HIPAA-compliant transcription and integrated analytics has never been greater. Azure AI Solution Overview Azure provides a comprehensive, cloud-native transcription and analytics pipeline that addresses these challenges head-on. By leveraging Azure AI Services, organizations can: Transcribe audio/video recordings in real time. Process PDFs and text documents for structured data extraction. Apply Text Analytics for Health to identify medical entities and structure data into FHIR format. Generate summaries and insights using cutting edge LLMs including Azure OpenAI. This approach accelerates workflows, improves compliance, and reduces costs compared to traditional transcription vendors. Azure Speech Service Options Azure Speech Service offers multiple transcription modes to fit different scenarios: Real-Time Transcription: Converts live audio streams into text instantly for telehealth sessions and interviews. Batch Transcription: Processes large volumes of pre-recorded audio asynchronously for research studies. Fast Transcription: Optimized for quick turnaround on short recordings for rapid documentation needs. Azure Text Analytics for Health One of the most powerful components of this solution is Azure AI Language â Text Analytics for Health, which transforms raw text into structured clinical insights. Key capabilities include: Named Entity Recognition (NER): Automatically identifies clinical entities such as symptoms, diagnoses, medications, procedures, and anatomy from transcripts and documents. Relation Extraction: Detects relationships between entities (e.g., linking a medication to its dosage or a condition to its treatment), enabling richer context for clinical decision-making. Entity Linking to UMLS Codes: Maps recognized entities to Unified Medical Language System (UMLS) concepts, ensuring interoperability and standardization across healthcare systems. Assertion Detection: Determines the status of an entity (e.g., present, absent, conditional, or hypothetical), which is critical for accurate interpretation of patient data. These features allow healthcare organizations to move beyond simple transcription and unlock structured, actionable insights that can feed downstream analytics and reporting. Other Azure Resources Azure AI Document Intelligence â Extracts structured data from PDFs and scanned documents. Azure OpenAI Service â Summarizes transcripts and generates clinical insights. Azure Storage & Functions â Securely stores raw and processed data; orchestrates workflows for transcription and analytics. Integration with Microsoft Fabric OneLake Once FHIR JSON output is generated from Text Analytics for Health, it can be stored in Microsoft Fabric OneLake. This unlocks powerful downstream capabilities: Unified Data Lake: Centralized storage for structured healthcare data. Analytics & Reporting: Use Fabricâs Lakehouse and Power BI to build dashboards for clinical research trends, patient outcomes, and operational metrics. AI-Driven Insights: Combine transcription data with other datasets for predictive modeling and advanced analytics. This integration ensures that transcription and clinical insights are not siloedâthey become part of a broader data ecosystem for research and decision-making. Why Azure Stands Out Compared to other transcription solutions in the market, Azure offers: Real-Time Processing: Immediate access to transcripts versus multi-day turnaround. Integrated Analytics: Built-in medical entity recognition and AI summarization. Compliance & Security: HIPAA-ready architecture with enterprise-grade governance. Cost Efficiency: Pay-as-you-go pricing with elastic scaling for large datasets. End-to-End Data Flow: From transcription to Fabric OneLake for analytics. Step-by-Step Deployment Guide As part of the Azure Field team working in the Healthcare and Life Sciences industry, this challenge has emerged as a common theme among organizations seeking to modernize transcription and analytics workflows. To assist organizations exploring Azure AI solutions to address these challenges, the following demo application was developed by Solution Engineer Samuel Tauil and Cloud & AI Platform Specialist Hannah Abbott. This application is intended to allow organizations to quickly stand up and test these Azure services for their needs and is not intended as a production-ready solution. This Azure-powered web application demonstrates how organizations can modernize transcription and clinical insights using cloud-native AI services. Users can upload audio files in multiple formats, which are stored in Azure Storage and trigger an Azure Function to perform speech-to-text transcription with speaker diarization. The transcript is then enriched through Azure Text Analytics for Health, applying advanced capabilities like named entity recognition, relation extraction, UMLS-based entity linking, and assertion detection to deliver structured clinical insights. Finally, Azure OpenAI generates a concise summary and a downloadable clinical report, while FHIR-compliant JSON output seamlessly integrates with Microsoft Fabric OneLake for downstream analytics and reportingâunlocking a complete, scalable, and secure solution for healthcare data workflows. The following video clip uses AI-generated dialog for a fictitious scenario to demonstrate the capabilities of the sample application. Sample application developed by Samuel Tauil Microsoft Solution Engineer (25) Samuel Tauil | LinkedIn Prerequisites Azure Subscription GitHub account Azure CLI installed locally (optional, for manual deployment) 1. Fork the Repository GitHub - samueltauil/transcription-services-demo: Azure Healthcare Transcription Services Demo - Speech-to-text with Text Analytics for Health for HIPAA-compliant medical transcription 2. Create Azure Service Principal for GitHub Actions Copy the JSON output. 3. Add GitHub Secrets (Settings â Secrets and variables â Actions): AZURE_CREDENTIALS: Paste the service principal JSON from step 2 4. Run the deployment workflow: Go to Actions tab â "0. Deploy All (Complete)" Click "Run workflow" Enter your resource group name and Azure region Click "Run workflow" 5. After infrastructure deploys, add these additional secrets: AZURE_FUNCTIONAPP_NAME: The function app name (shown in workflow output) AZURE_STATIC_WEB_APPS_API_TOKEN: Get from Azure Portal â Static Web App â Manage deployment token Benefits Accelerated Research: Reduce transcription time from days to minutes. Enhanced Accuracy: AI-driven entity recognition for clinical terms. Scalable & Secure: Built on Azureâs compliance-ready infrastructure. Analytics-Ready: Seamless integration with Fabric for reporting and insights. Reference Links: Transcription Service: Speech to text overview - Speech service - Foundry Tools | Microsoft Learn Batch transcription overview - Speech service - Foundry Tools | Microsoft Learn Speech to text quickstart - Foundry Tools | Microsoft Learn Real-time diarization quickstart - Speech service - Foundry Tools | Microsoft Learn Text Analytics: Watch this: Embedded Video | Microsoft Learn What is the Text Analytics for health in Azure Language in Foundry Tools? - Foundry Tools | Microso⌠Fast Healthcare Interoperability Resources (FHIR) structuring in Text Analytics for health - Foundr⌠azure-ai-docs/articles/ai-services/language-service/text-analytics-for-health/quickstart.md at main⌠AI Foundry: Model catalog - Azure AI FoundryApp Service Easy MCP: Add AI Agent Capabilities to Your Existing Apps with Zero Code Changes
The age of AI agents is here. Tools like GitHub Copilot, Claude, and other AI assistants are no longer just answering questionsâthey're taking actions, calling APIs, and automating complex workflows. But how do you make your existing applications and APIs accessible to these intelligent agents? At Microsoft Ignite, I teamed up to present session BRK116: Apps, agents, and MCP is the AI innovation recipe, where I demonstrated how you can add agentic capabilities to your existing applications with little to no code changes. Today, I'm excited to share a concrete example of that vision: Easy MCPâa way to expose any REST API to AI agents with absolutely zero code changes to your existing apps. The Challenge: Bridging REST APIs and AI Agents Most organizations have invested years building REST APIs that power their applications. These APIs represent critical business logic, data access patterns, and integrations. But AI agents speak a different languageâthey use protocols like Model Context Protocol (MCP) to discover and invoke tools. The traditional approach would require you to: Learn the MCP SDK Write new MCP server code Manually map each API endpoint to an MCP tool Deploy and maintain additional infrastructure What if you could skip all of that? Introducing Easy MCP (a proof of concept not associated with the App Service platform) Easy MCP is an OpenAPI-to-MCP translation layer that automatically generates MCP tools from your existing REST APIs. If your API has an OpenAPI (Swagger) specificationâwhich most modern APIs doâyou can make it accessible to AI agents in minutes. This means that if you have existing apps with OpenAPI specifications already running on App Service, or really any hosting platform, this tool makes enabling MCP seamless. How It Works Point the gateway at your API's base URL Detect your OpenAPI specification automatically Connect and the gateway generates MCP tools for every endpoint Use the MCP endpoint URL with any MCP-compatible AI client That's it. No code changes. No SDK integration. No manual tool definitions. See It in Action Let's say you have a Todo API running on Azure App Service at `https://my-todo-app.azurewebsites.net`. In just a few clicks: Open the Easy MCP web UI Enter your API URL Click "Detect" to find your OpenAPI spec Click "Connect" Now configure your AI client (like VS Code with GitHub Copilot) to use the gateway's MCP endpoint: { "servers": { "my-api": { "type": "http", "url": "https://my-gateway.azurewebsites.net/mcp" } } } Instantly, your AI assistant can: "What's on my todo list?" "Add 'Review PR #123' to my todos with high priority" "Mark all tasks as complete" All powered by your existing REST API, with zero modifications. The Bigger Picture: Modernization Without Rewrites This approach aligns perfectly with a broader modernization strategy we're enabling on Azure App Service. App Service Managed Instance: Move and Modernize Legacy Apps For organizations with legacy applicationsâwhether they're running on older Windows frameworks, custom configurations, or traditional hosting environmentsâAzure App Service Managed Instance provides a path to the cloud with minimal friction. You can migrate these applications to a fully managed platform without rewriting code. Easy MCP: Add AI Capabilities Post-Migration Once your legacy applications are running on App Service, Easy MCP becomes the next step in your modernization journey. That 10-year-old internal API? It can now be accessed by AI agents. That legacy inventory system? AI assistants can query and update it. No code changes needed. The modernization path: Migrate legacy apps to App Service with Managed Instance (no code changes) Expose APIs to AI agents with Easy MCP Gateway (no code changes) Empower your organization with AI-assisted workflows Deploy It Yourself Easy MCP is open source and ready to deploy. If you already have an existing API to use with this tool, go for it. If you need an app to test with, check out this sample. Make sure you complete the "Add OpenAPI functionality to your web app" step. You don't need to go beyond that. GitHub Repository: seligj95/app-service-easy-mcp Deploy to Azure in minutes with Azure Developer CLI: azd auth login azd init azd up Or run it locally for testing: npm install npm run dev # Open http://localhost:3000 What's Next: Native App Service Integration Here's where it gets really exciting. We're exploring ways to build this capability directly into the Azure App Service platform so you won't have to deploy a second app or additional resources to get this capability. Azure API Management recently released a feature with functionality to expose a REST API, including an API on App Service, as an MCP server, which I highly recommend that you check out if you're familiar with Azure API Management. But in this case, imagine a future where adding AI agent capabilities to your App Service apps is as simple as flipping a switch in the Azure Portalâno gateway or API Management deployment required, no additional infrastructure or services to manage, and built-in security, monitoring, scaling, etc.âall of the features you're already using and are familiar with on App Service. Stay tuned for updates as we continue to make Azure App Service the best platform for AI-powered applications. And please share your feedback on Easy MCPâwe want to hear how you're using it and what features you'd like to see next as we consider this feature for native integration.Secure Unique Default Hostnames Now GA for Functions and Logic Apps
We are pleased to announce that Secure Unique Default Hostnames are now generally available (GA) for Azure Functions and Logic Apps (Standard). This expands the security model previously available for Web Apps to the entire App Service ecosystem and provides customers with stronger, more secure, and standardized hostname behavior across all workloads. Why This Feature Matters Historically, App Service resources have used default hostname format such as: <SiteName>.azurewebsites.net While straightforward, this pattern introduced potential security risks, particularly in scenarios where DNS records were left behind after deleting a resource. In those situations, a different user could create a new resource with the same name and unintentionally receive traffic or bindings associated with the old DNS configuration, creating opportunities for issues such as subdomain takeover. Secure Unique Default Hostnames address this by assigning a unique, randomized, regionâscoped hostname to each resource, for example: <SiteName>-<Hash>.<Region>.azurewebsites.net This change ensures that: No other customer can recreate the same default hostname. Apps inherently avoid risks associated with dangling DNS entries. Customers gain a more secure, reliable baseline behavior across App Service. Adopting this model now helps organizations prepare for the longâterm direction of the platform while improving security posture today. Whatâs New: GA Support for Functions and Logic Apps With this release, both Azure Functions and Logic Apps (Standard) fully support the Secure Unique Default Hostname capability. This brings these services in line with Web Apps and ensures customers across all App Service workloads benefit from the same secure and consistent default hostname model. Azure CLI Support The Azure CLI for Web Apps and Function Apps now includes support for the â--domain-name-scopeâ parameter. This allows customers to explicitly specify the scope used when generating a unique default hostname during resource creation. Examples: az webapp create --domain-name-scope {NoReuse, ResourceGroupReuse, SubscriptionReuse, TenantReuse} az functionapp create --domain-name-scope {NoReuse, ResourceGroupReuse, SubscriptionReuse, TenantReuse} Including this parameter ensures that deployments consistently use the intended hostname scope and helps teams prepare their automation and provisioning workflows for the secure unique default hostname model. Why Customers Should Adopt This Now While existing resources will continue to function normally, customers are strongly encouraged to adopt Secure Unique Default Hostnames for all new deployments. Early adoption provides several important benefits: A significantly stronger security posture. Protection against dangling DNS and subdomain takeover scenarios. Consistent default hostname behavior as the platform evolves. Alignment with recommended deployment practices and modern DNS hygiene. This feature represents the current best practice for hostname management on App Service and adopting it now helps ensure that new deployments follow the most secure and consistent model available. Recommended Next Steps Enable Secure Unique Default Hostnames for all new Web Apps, Function Apps, and Logic Apps. Update automation and CLI scripts to include the --domain-name-scope parameter when creating new resources. Begin updating internal documentation and operational processes to reflect the new hostname pattern. Additional Resources For readers who want to explore the technical background and earlier announcements in more detail, the following articles offer deeper coverage of unique default hostnames: Public Preview: Creating Web App with a Unique Default Hostname This article introduces the foundational concepts behind unique default hostnames. It explains why the feature was created, how the hostname format works, and provides examples and guidance for enabling the feature when creating new resources. Secure Unique Default Hostnames: GA on App Service Web Apps and Public Preview on Functions This article provides the initial GA announcement for Web Apps and early preview details for Functions. It covers the security benefits, recommended usage patterns, and guidance on how to handle existing resources that were created without unique default hostnames. Conclusion Secure Unique Default Hostnames now provide a more secure and consistent default hostname model across Web Apps, Function Apps, and Logic Apps. This enhancement reduces DNSârelated risks and strengthens application security, and organizations are encouraged to adopt this feature as the standard for new deployments.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!
