Modernizing Spring Boot Applications with GitHub Copilot App Modernization
Upgrading Spring Boot applications from 2.x to the latest 3.x releases introduces significant changes across the framework, dependencies, and Jakarta namespace. These updates improve long-term support, performance, and compatibility with modern Java platforms, but the migration can surface breaking API changes and dependency mismatches. GitHub Copilot app modernization helps streamline this transition by analyzing your project, generating an upgrade plan, and applying targeted updates. Supported Upgrade Path GitHub Copilot app modernization supports upgrading Spring Boot applications to Spring Boot 3.5, including: Updating Spring Framework libraries to 6.x Migrating from javax to jakarta Aligning dependency versions with Boot 3.x Updating plugins and starter configurations Adjusting build files for the required JDK level Validating dependency updates and surfacing CVE issues These capabilities complement the Microsoft Learn quickstart for upgrading Java projects using GitHub Copilot app modernization. How GitHub Copilot app modernization helps When you open a Spring Boot 2.x project in Visual Studio Code or IntelliJ IDEA and initiate an upgrade, GitHub Copilot app modernization performs: Project Analysis Detects your current Spring Boot version Identifies incompatible starters, libraries, and plugins Flags javax.* imports requiring Jakarta migration Evaluates your build configuration and JDK requirements Upgrade Plan Generation The tool produces an actionable plan that outlines: New Spring Boot parent version Updated Spring Framework and related modules Required namespace changes from javax.* to jakarta.* Build plugin updates JDK configuration alignment for Boot 3 You can review and adjust the plan before applying changes. Automated Transformations GitHub Copilot app modernization applies targeted changes such as: Updating spring-boot-starter-parent to 3.5.x Migrating imports to jakarta.* Updating dependencies and BOM versions Rewriting removed or deprecated APIs Aligning test dependencies (e.g., JUnit 5) Build / Fix Iteration The agent automatically: Builds the project Captures failures Suggests fixes Applies updates Rebuilds until the project compiles successfully This loop continues until all actionable issues are addressed. Security & Behavior Checks As part of the upgrade, the tool can: Validate CVEs introduced by dependency version changes Surface potential behavior changes Recommend optional fixes Expected Output After running the upgrade for a Spring Boot 2.x project, you should expect: An updated Spring Boot parent in Maven or Gradle Spring Framework 6.x and Jakarta-aligned modules Updated starter dependencies and plugin versions Rewritten imports from javax.* to jakarta.* Updated testing stack A summary file detailing: Versions updated Code edits applied Dependencies changed CVE results Remaining manual review items Developer Responsibilities GitHub Copilot app modernization accelerates technical migration tasks, but final validation still requires developer review, including: Running the full test suite Reviewing custom filters, security configuration, and web components Re-validating integration points Confirming application behavior across runtime environments The tool handles mechanical upgrade work so you can focus on correctness, quality, and functional validation. Learn more For setup, prerequisites, and the broader Java upgrade workflow, refer to the official Microsoft Learn guide: Quickstart: Upgrade a Java Project with GitHub Copilot App Modernization Install GitHub Copilot app modernization for VS Code and IntelliJ IDEA
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. 👉 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. 👉 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 craft—they 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 StartedFind the Alerts You Didn't Know You Were Missing with Azure SRE Agent
I had 6 alert rules. CPU. Memory. Pod restarts. Container errors. OOMKilled. Job failures. I thought I was covered. Then my app went down. I kept refreshing the Azure portal, waiting for an alert. Nothing. That's when it hit me: my alerts were working perfectly. They just weren't designed for this failure mode. Sound familiar? The Problem Every Developer Knows If you're a developer or DevOps engineer, you've been here: a customer reports an issue, you scramble to check your monitoring, and then you realize you don't have the right alerts set up. By the time you find out, it's already too late. You set up what seems like reasonable alerting and assume you're covered. But real-world failures are sneaky. They slip through the cracks of your carefully planned thresholds. My Setup: AKS with Redis I love to vibe code apps using GitHub Copilot Agent mode with Claude Opus 4.5. It's fast, it understands context, and it lets me focus on building rather than boilerplate. For this project, I built a simple journal entry app: AKS cluster hosting the web API Azure Cache for Redis storing journal data Azure Monitor alerts for CPU, memory, pod restarts, container errors, OOMKilled, and job failures Seemed solid. What could go wrong? The Scenario: Redis Password Rotation Here's something that happens constantly in enterprise environments: the security team rotates passwords. It's best practice. It's in the compliance checklist. And it breaks things when apps don't pick up the new credentials. I simulated exactly this. The pods came back up. But they couldn't connect to Redis (as expected). The readiness probes started failing. The LoadBalancer had no healthy backends. The endpoint timed out. And not a single alert fired. Using SRE Agent to Find the Alert Gaps Instead of manually auditing every alert rule and trying to figure out what I missed, I turned to Azure SRE Agent. I asked it a simple question: "My endpoint is timing out. What alerts do I have, and why didn't any of them fire?" Within minutes, it had diagnosed the problem. Here's what it found: My Existing Alerts Why They Didn't Fire High CPU/Memory No resource pressure,just auth failures Pod Restarts Pods weren't restarting, just unhealthy Container Errors App logs weren't being written OOMKilled No memory issues Job Failures No K8s jobs involved The gaps SRE Agent identified: ❌ No synthetic URL availability test ❌ No readiness/liveness probe failure alerts ❌ No "pods not ready" alerts scoped to my namespace ❌ No Redis connection error detection ❌ No ingress 5xx/timeout spike alerts ❌ No per-pod resource alerts (only node-level) SRE Agent didn't just tell me what was wrong, it created a GitHub issue with : KQL queries to detect each failure type Bicep code snippets for new alert rules Remediation suggestions for the app code Exact file paths in my repo to update Check it out: GitHub 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 analyzes alerts across my subscription (not just one resource group), I didn't configure any specific resource groups. Instead, I gave the agent's managed identity Reader permissions on my entire subscription. This lets it discover resources, list alert rules, and query Log Analytics across all my resource groups. Step 2: Connect GitHub to SRE Agent via MCP I added a GitHub MCP server to give the agent access to my source code repository.MCP (Model Context Protocol) lets you bring any API into the agent. If your tool has an API, you can connect it. I use GitHub for both source code and tracking dev tickets, but you can connect to wherever your code lives (GitLab, Azure DevOps) or your ticketing system (Jira, ServiceNow, PagerDuty). Step 3: Create a Subagent inside SRE Agent for managing Azure Monitor Alerts I created a focused subagent with a specific job and only the tools it needs: Azure Monitor Alerts Expert Prompt: " You are expert in managing operations related to azure monitor alerts on azure resources including discovering alert rules configured on azure resources, creating new alert rules (with user approval and authorization only), processing the alerts fired on azure resources and identifying gaps in the alert rules. You can get the resource details from azure monitor alert if triggered via alert. If not, you need to ask user for the specific resource to perform analysis on. You can use az cli tool to diagnose logs, check the app health metrics. You must use the app code and infra code (bicep files) files you have access to in the github repo <insert your repo> to further understand the possible diagnoses and suggest remediations. Once analysis is done, you must create a github issue with details of analysis and suggested remediation to the source code files in the same repo." Tools enabled: az cli – List resources, alert rules, action groups Log Analytics workspace querying – Run KQL queries for diagnostics GitHub MCP – Search repositories, read file contents, create issues Step 4: Ask the Subagent About Alert Gaps I gave the agent context and asked a simple question: "@AzureAlertExpert: My API endpoint http://132.196.167.102/api/journals/john is timing out. What alerts do I have configured in rg-aks-journal, and why didn't any of them fire? The agent did the analysis autonomously and summarized findings with suggestions to add new alert rules in a GitHub issue. Here's the agentic workflow to perform azure monitor alert operations Why This Matters Faster response times. Issues get diagnosed in minutes, not hours of manual investigation. Consistent analysis. No more "I thought we had an alert for that" moments. The agent systematically checks what's covered and what's not. Proactive coverage. You don't have to wait for an incident to find gaps. Ask the agent to review your alerts before something breaks. The Bottom Line Your alerts have gaps. You just don't know it until something slips through. I had 6 alert rules and still missed a basic failure. My pods weren't restarting, they were just unhealthy. My CPU wasn't spiking, the app was just returning errors. None of my alerts were designed for this. You don't need to audit every alert rule manually. Give SRE Agent your environment, describe the failure, and let it tell you what's missing. Stop discovering alert gaps from customer complaints. Start finding them before they matter. A Few Tips Give the agent Reader access at subscription level so it can discover all resources Use a focused subagent prompt, don't try to do everything in one agent Test your MCP connections before running workflows What Alert Gaps Have Burned You? What's the alert you wish you had set up before an incident? Credential rotation? Certificate expiry? DNS failures? Let us know in the comments.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" } 🎉 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 pricing
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. I’ve 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! Let’s 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 – for example, check out this fantastic post from Arvi LiVigni (@arilivigni ) which talks about migrating from COBOL “the number of developers who can read and write COBOL isn’t what it used to be,” 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. It’s no wonder many modernisation projects stalled or were “put off” due to their complexity and risk. So, what’s 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. Arvi’s 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 “nine times out of 10 it gives me the right answer… That speed – and not having to break out of my flow – is really what’s so impactful.” In short, AI coding assistants have evolved from novel experiments to indispensable tools, reimagining how we approach software updates and cloud adoption. I’d also add from my own experience – 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 – with such speed to create those proof of concepts, experimentation and failing faster, this has also unlocked the ability to test out multiple hypothesis’ 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 – 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 isn’t just about updating code – 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 – 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 – 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 Copilot’s 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 – 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 application’s 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 you’re not left with broken builds. What used to require reading countless Azure migration guides is now handled in minutes. Automated Validation & Deployment: Modernisation doesn’t 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 you’re 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 – 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 Copilot’s AI is adept at interpreting such descriptions and suggesting step definition code or test implementations to match. For instance, given a scenario “When a user with no items checks out, then an error message is shown,” 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 aren’t 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 Copilot’s 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. It’s 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, what’s 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. There’s also an opportunity cost benefit: developers freed up by Copilot can work on other value-adding projects in parallel. Improved Quality & Reliability: GitHub Copilot’s 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 doesn’t mean “trading one set of bugs for another” – instead, you can increase quality as you modernise (GitHub’s 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 – 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 “boat anchors” into modern, cloud-enabled assets rapidly, while improving quality and developer morale. This aligns IT goals with business goals – 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 you’re excited about the possibilities, here are next steps and what to watch for: Start Experimenting: If you haven’t 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 that’s ripe for modernisation – 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, you’ll find tasks once deemed “too hard” 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, we’ll dive deeper into: Setting Up Your Organisation for Copilot Adoption: Practical tips on preparing your enterprise environment – from licensing and security considerations to training programs. We’ll 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. We’ll 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. We’ll look at different domains – code, cloud, tests, data – 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 “GitHub Copilot helped reduce our code review times” or “we migrated a component to Azure in 1 sprint”) 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 Guidance—a 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 let’s modernise what’s possible together!Search Less, Build More: Inner Sourcing with GitHub Copilot and ADO MCP Server
Developers burn cycles context‑switching: 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 re‑implementing, 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 – list and navigate across projects in your organization. Repositories – browse repos, branches, and files. Work items – surface user stories, bugs, or acceptance criteria. Wiki's – pull policies, standards, and documentation. This means Copilot can ground its answers in live ADO content, instead of hallucinating or relying only on what’s 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 “playbook” for how it should behave. There are different types of instructions you can provide: Organisational instructions – apply across your entire workspace, regardless of which repo you’re in. Repo-specific instructions – scoped to a particular repository, useful when one project has unique standards or patterns. Personal instructions – smaller overrides layered on top of global rules when a local exception applies. (Stored in .github/copilot-instructions.md) In this solution, I’m 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: ``` ✅ Use: wit_my_work_items, wit_create_work_item, wit_update_work_item ✅ Use: wit_list_backlogs, wit_get_work_items_for_iteration ✅ 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 → 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 – Start generic (“How do we secure APIs?”). 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: ``` ✅ Use: wit_my_work_items, wit_create_work_item, wit_update_work_item ✅ Use: wit_list_backlogs, wit_get_work_items_for_iteration ✅ Use: work_list_team_iterations, core_list_projects ``` ### Code and Repository Operations When users ask about code, branches, or pull requests: ``` ✅ Use: repo_list_repos_by_project, repo_list_pull_requests_by_repo ✅ Use: repo_list_branches_by_repo, repo_search_commits ✅ Use: search_code for finding patterns across repositories ``` ### Documentation and Knowledge Sharing When users need documentation or want to create/update docs: ``` ✅ Use: wiki_list_wikis, wiki_get_page_content, wiki_create_or_update_page ✅ Use: search_wiki for finding existing documentation ``` ### Build and Deployment When users ask about builds, deployments, or CI/CD: ``` ✅ Use: pipelines_get_builds, pipelines_get_build_definitions ✅ 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..." → 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: - ✅ Search organizational context before suggesting solutions - ✅ Reference existing patterns and standards - ✅ Offer to create/update Azure DevOps artifacts - ✅ Maintain consistency with organizational practices - ✅ Provide actionable next steps ### Never Do: - ❌ Suggest solutions without checking organizational context - ❌ Ignore existing patterns and implementations - ❌ Provide generic advice when specific organizational context is available - ❌ 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.**Reimagining 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 what’s new and what has changed since the last update. What’s New: Automation, Integration, and Extensibility Azure SRE Agent just got a major upgrade. From no-code automation to seamless integrations and expanded data connectivity, here’s what’s 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 organization’s 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 team’s 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 organization’s 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 agent’s 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 What’s Next? Give us feedback: Your feedback is critical - You can Thumbs Up / Thumbs Down each interaction or thread, or go to the “Give Feedback” 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. We’re 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!Proactive Monitoring Made Simple with Azure SRE Agent
SRE teams strive for proactive operations, catching issues before they impact customers and reducing the chaos of incident response. While perfection may be elusive, the real goal is minimizing outages and gaining immediate line of sight into production environments. Today, that’s harder than ever. It requires correlating countless signals and alerts, understanding how they relate—or don’t relate—to each other, and assigning the right sense of urgency and impact. Anything that shortens this cycle, accelerates detection, and enables automated remediation is what modern SRE teams crave. What if you could skip the scripting and pipelines? What if you could simply describe what you want in plain language and let it run automatically on a schedule? Scheduled Tasks for Azure SRE Agent With Scheduled Tasks for Azure SRE Agent, that what-if scenario is now a reality. Scheduled tasks combine natural language prompts with Azure SRE Agent’s automation capabilities, so you can express intent, set a schedule, and let the agent do the rest—without writing a single line of code. This means: ⚡ Faster incident response through early detection ✅ Better compliance with automated checks 🎯 More time for high-value engineering work and innovation 💡 The shift from reactive to proactive: Instead of waiting for alerts to fire or customers to report issues, you’re continuously monitoring, validating, and catching problems before they escalate. How Scheduled Tasks Work Under the Hood When you create a Scheduled Task, the process is more than just running a prompt on a timer. Here’s what happens: 1. Prompt Interpretation and Plan Creation The SRE Agent takes your natural language prompt—such as “Scan all resources for security best practices”—and converts it into a structured execution plan. This plan defines the steps, tools, and data sources required to fulfill your request. 2. Built-In Tools and MCP Integration The agent uses its built-in capabilities (Azure CLI, Log Analytics workspace, Appinsights) and can also leverage 3 rd party data sources or tools via MCP server integration for extended functionality. 3. Results Analysis and Smart Summarization After execution, the agent analyzes results, identifies anomalies or issues, and provides actionable summaries not just raw data dumps. 4. Notification and Escalation Based on findings, the agent can: Post updates to Teams channels Create or update incidents Send email notifications Trigger follow-up actions Real-World Use Cases for Proactive Ops Here’s where scheduled tasks shine for SRE teams: Use Case Prompt Example Schedule Security Posture Check “Scan all subscriptions for resources with public endpoints and flag any that shouldn’t be exposed” Daily Cost Anomaly Detection “Compare this week’s spend against last week and alert if any service exceeds 20% growth” Weekly Compliance Drift Detection “Check all storage accounts for encryption settings and report any non-compliant resources” Daily Resource Health Summary “Summarize the health status of all production VMs and highlight any degraded instances” Every 4 hours Incident Trend Analysis “Analyze ICM incidents from the past week, identify patterns, and summarize top contributing services” Weekly Getting Started in 3 Steps Step 1: Define Your Intent Write a natural language prompt describing what you want to monitor or check. Be specific about: - What resources or scope - What conditions to look for - What action to take if issues are found Example: > “Every morning at 8 AM, check all production Kubernetes clusters for pods in CrashLoopBackOff state. If any are found, post a summary to the #sre-alerts Teams channel with cluster name, namespace, and pod details.” Step 2: Set Your Schedule Choose how often the task should run: - Cron expressions for precise control - Simple intervals (hourly, daily, weekly) Step 3: Define Where to Receive Updates Include in your prompt where you want results delivered when the task finishes execution. The agent can use its built-in tools and connectors to: - Post summaries to a Teams channel - Send email notifications - Create or update ICM incidents Example prompt with notification: > "Check all production databases for long-running queries over 30 seconds. If any are found, post a summary to the #database-alerts Teams channel." Why This Matters for Proactive Operations Traditional monitoring approaches have limitations: Traditional Approach With Scheduled Tasks Write scripts, maintain pipelines Describe in plain language Static thresholds and rules Contextual, AI-powered analysis Alert fatigue from noisy signals Smart summarization of what matters Separate tools for check vs. action Unified detection and response Requires dedicated DevOps effort Any SRE can create and modify The result? Your team spends less time building and maintaining monitoring infrastructure and more time on the work that truly requires human expertise. Best Practices for Scheduled Tasks Start simple, iterate — Begin with one or two high-value checks and expand as you gain confidence Be specific in prompts — The more context you provide, the better the results Set appropriate frequencies — Not everything needs to run hourly; match the schedule to the risk Review and refine — Check task results periodically and adjust prompts for better accuracy What’s Next? Scheduled tasks are just the beginning. We’re continuing to invest in capabilities that help SRE teams shift left—catching issues earlier, automating routine checks, and freeing up time for strategic work. Ready to Start? Use this sample that shows how to create a scheduled health check sub-agent: https://github.com/microsoft/sre-agent/blob/main/samples/automation/samples/02-scheduled-health-check-sample.md This example demonstrates: - Building a HealthCheckAgent using built-in tools like Azure CLI and Log Analytics Workspace - Scheduling daily health checks for a container app at 7 AM - Sending email alerts when anomalies are detected 🔗 Explore more samples here: https://github.com/microsoft/sre-agent/tree/main/samples More to Learn Ignite 2025 announcements: https://aka.ms/ignite25/blog/sreagent Documentation: https://aka.ms/sreagent/docs Support & Feature Requests: https://github.com/microsoft/sre-agent/issuesAgentic Applications on Azure Container Apps with Microsoft Foundry
Agents have exploded in popularity over the last year, reshaping not only the kinds of applications developers build but also the underlying architectures required to run them. As agentic applications grow more complex by invoking tools, collaborating with other services, and orchestrating multi-step workflows, architectures are naturally shifting toward microservice patterns. Azure Container Apps is purpose-built for this world: a fully managed, serverless platform designed to run independent, composable services with autoscaling, pay-per-second pricing, and seamless app-to-app communication. By combining Azure Container Apps with the Microsoft Agent Framework (MAF) and Microsoft Foundry, developers can run containerized agents on ACA while using Foundry to visualize and monitor how those agents behave. Azure Container Apps handles scalable, high-performance execution of agent logic, and Microsoft Foundry lights up rich observability for reasoning, planning, tool calls, and errors through its integrated monitoring experience. Together, they form a powerful foundation for building and operating modern, production-grade agentic applications. In this blog, we’ll walk through how to build an agent running on Azure Container Apps using Microsoft Agent Framework and OpenTelemetry, and then connect its telemetry to Microsoft Foundry so you can see your ACA-hosted agent directly in the Foundry monitoring experience. Prerequisites An Azure account with an active subscription. If you don't have one, you can create one for free. Ensure you have a Microsoft Foundry project setup. If you don’t already have one, you can create a project from the Azure AI Foundry portal. Azure Developer CLI (azd) installed Git installed The Sample Agent The complete sample code is available in this repo and can be deployed end-to-end with a single command. It's a basic currency agent. This sample deploys: An Azure Container Apps environment An agent built with Microsoft Agent Framework (MAF) OpenTelemetry instrumentation using the Azure Monitor exporter Application Insights to collect agent telemetry A Microsoft Foundry resource Environment wiring to integrate the agent with Microsoft Foundry Deployment Steps Clone the repository: git clone https://github.com/cachai2/foundry-3p-agents-samples.git cd foundry-3p-agents-samples/azure Authenticate with Azure: azd auth login Set the following azd environment variable azd env set AZURE_AI_MODEL_DEPLOYMENT_NAME gpt-4.1-mini Deploy to Azure azd up This provisions your Azure Container App, Application Insights, logs pipeline and required environment variables. While deployment runs, let’s break down how the code becomes compatible with Microsoft Foundry. 1. Setting up the agent in Azure Container Apps To integrate with Microsoft Foundry, the agent needs two essential capabilities: Microsoft Agent Framework (MAF): Handles agent logic, tools, schema-driven execution, and emits standardized gen_ai.* spans. OpenTelemetry: Sends the required agent/model/tool spans to Application Insights, which Microsoft Foundry consumes for visualization and monitoring. Although this sample uses MAF, the same pattern works with any agent framework. MAF and LangChain currently provide the richest telemetry support out-of-the-box. 1.1 Configure Microsoft Agent Framework (MAF) The agent includes: A tool (get_exchange_rate) An agent created by ChatAgent A runtime manager (AgentRuntime) A FastAPI app exposing /invoke Telemetry is enabled using two components already present in the repo: configure_azure_monitor: Configures OpenTelemetry + Azure Monitor exporter + auto-instrumentation. setup_observability(): Enables MAF’s additional spans (gen_ai.*, tool spans, agent lifecycle spans). From the repo (_configure_observability()): from azure.monitor.opentelemetry import configure_azure_monitor from agent_framework.observability import setup_observability from opentelemetry.sdk.resources import Resource def _configure_observability() -> None: configure_azure_monitor( resource=Resource.create({"service.name": SERVICE_NAME}), connection_string=APPLICATION_INSIGHTS_CONNECTION_STRING, ) setup_observability(enable_sensitive_data=False) This gives you: gen_ai.model.* spans (model usage + token counts) tool call spans agent lifecycle & execution spans HTTP + FastAPI instrumentation Standardized telemetry required by Microsoft Foundry No manual TracerProvider wiring or OTLP exporter setup is needed. 1.2 OpenTelemetry Setup (Azure Monitor Exporter) In this sample, OpenTelemetry is fully configured by Azure Monitor’s helper: import os from azure.monitor.opentelemetry import configure_azure_monitor from opentelemetry.sdk.resources import Resource from agent_framework.observability import setup_observability SERVICE_NAME = os.getenv("ACA_SERVICE_NAME", "aca-currency-exchange-agent") configure_azure_monitor( resource=Resource.create({"service.name": SERVICE_NAME}), connection_string=os.getenv("APPLICATION_INSIGHTS_CONNECTION_STRING"), ) # Enable Microsoft Agent Framework gen_ai/tool spans on top of OTEL setup_observability(enable_sensitive_data=False) This automatically: Installs and configures the OTEL tracer provider Enables batching + exporting of spans Adds HTTP/FastAPI/Requests auto-instrumentation Sends telemetry to Application Insights Adds MAF’s agent + tool spans All required environment variables (such as APPLICATION_INSIGHTS_CONNECTION_STRING) are injected automatically by azd up. 2. Deploy the Model and Test Your Agent Once azd up completes, you're ready to deploy a model to the Microsoft Foundry instance and test it. Find the resource name of your deployed Azure AI Services from azd up and navigate to it. From there, open it in Microsoft Foundry, navigate to the Model Catalog and add the gpt-4.1-mini model. Find the resource name of your deployed Azure Container App and navigate to it. Copy the application URL Set your container app URL environment variable in your terminal. (The below commands are for WSL.) export APP_URL="Your container app URL" Now, go back to your terminal and run the following curl command to invoke the agent curl -X POST "$APP_URL/invoke" \ -H "Content-Type: application/json" \ -d '{ "prompt": "How do I convert 100 USD to EUR?" }' 3. Verifying Telemetry to Application Insights Once your Container App starts, you can validate telemetry: Open the Application Insights resource created by azd up Go to Logs Run these queries (make sure you're in KQL mode not simple mode) Check MAF-genAI spans: dependencies | where timestamp > ago(3h) | extend genOp = tostring(customDimensions["gen_ai.operation.name"]), genSys = tostring(customDimensions["gen_ai.system"]), reqModel = tostring(customDimensions["gen_ai.request.model"]), resModel = tostring(customDimensions["gen_ai.response.model"]) | summarize count() by genOp, genSys, reqModel, resModel | order by count_ desc Check agent + tools: dependencies | where timestamp > ago(1h) | extend genOp = tostring(customDimensions["gen_ai.operation.name"]), agent = tostring(customDimensions["gen_ai.agent.name"]), tool = tostring(customDimensions["gen_ai.tool.name"]) | where genOp in ("agent.run", "invoke_agent", "execute_tool") | project timestamp, genOp, agent, tool, name, target, customDimensions | order by timestamp desc If telemetry is flowing, you’re ready to plug your agent into Microsoft Foundry. 4. Connect Application Insights to Microsoft Foundry Microsoft Foundry uses your Application Insights resource to power: Agent monitoring Tool call traces Reasoning graphs Multi-agent orchestration views Error analysis To connect: Navigate to Monitoring in the left navigation pane of the Microsoft Foundry portal. Select the Application analytics tab. Select your application insights resource created from azd up Connect the resource to your AI Foundry project. Note: If you are unable to add your application insights connection this way, you may need to follow the following: Navigate to the Overview of your Foundry project -> Open in management center -> Connected resources -> New Connection -> Application Insights Foundry will automatically start ingesting: gen_ai.* spans tool spans agent lifecycle spans workflow traces No additional configuration is required. 5. Viewing Dashboards & Traces in Microsoft Foundry Once your Application Insights connection is added, you can view your agent’s telemetry directly in Microsoft Foundry’s Monitoring experience. 5.1 Monitoring The Monitoring tab shows high-level operational metrics for your application, including: Total inference calls Average call duration Overall success/error rate Token usage (when available) Traffic trends over time This view is useful for spotting latency spikes, increased load, or changes in usage patterns, and these visualizations are powered by the telemetry emitting from your agents in Azure Container Apps. 5.2 Traces Timeline The Tracing tab shows the full distributed trace of each agent request, including all spans emitted by Microsoft Foundry and your Azure Container App with Microsoft Agent Framework. You can see: Top-level operations such as invoke_agent, chat, and process_thread_run Tool calls like execute_tool_get_exchange_rate Internal MAF steps (create_thread, create_message, run tool) Azure credential calls (GET /msi/token) Input/output tokens and duration for each span This view gives you an end-to-end breakdown of how your agent executed, which tools it invoked, and how long each step took — essential for debugging and performance tuning. Conclusion By combining Azure Container Apps, the Microsoft Agent Framework, and OpenTelemetry, you can build agents that are not only scalable and production-ready, but also fully observable and orchestratable inside Microsoft Foundry. Container Apps provides the execution engine and autoscaling foundation, MAF supplies structured agent logic and telemetry, and Microsoft Foundry ties everything together with powerful planning, monitoring, and workflow visualization. This architecture gives you the best of both worlds: the flexibility of running your own containerized agents with the dependencies you choose, and the intelligence of Microsoft Foundry to coordinate multi-step reasoning, tool call, and cross-agent workflows. As the agent ecosystem continues to evolve, Azure Container Apps and Microsoft Foundry provide a strong, extensible foundation for building the next generation of intelligent, microservice-driven applications.
