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 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: â No synthetic URL availability test â No readiness/liveness probe failure alerts â No "pods not ready" alerts scoped to my namespace â No Redis connection error detection â No ingress 5xx/timeout spike alerts â 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 â List resources, alert rules, action groups Log Analytics workspace querying â Run KQL queries for diagnostics GitHub MCP â 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.Stop Running Runbooks at 3 am: Let Azure SRE Agent Do Your On-Call Grunt Work
Your pager goes off. It's 2:47am. Production is throwing 500 errors. You know the drill - SSH into this, query that, check these metrics, correlate those logs. Twenty minutes later, you're still piecing together what went wrong. Sound familiar? The On-Call Reality Nobody Talks About Every SRE, DevOps engineer, and developer who's carried a pager knows this pain. When incidents hit, you're not solving problems - you're executing runbooks. Copy-paste this query. Check that dashboard. Run these az commands. Connect the dots between five different tools. It's tedious. It's error-prone at 3am. And honestly? It's work that doesn't require human creativity but requires human time. What if an AI agent could do this for you? Enter Azure SRE Agent + Runbook Automation Here's what I built: I gave SRE Agent a simple markdown runbook containing the same diagnostic steps I'd run manually during an incident. The agent executes those steps, collects evidence, and sends me an email with everything I need to take action. No more bouncing between terminals. No more forgetting a step because it's 3am and your brain is foggy. What My Runbook Contains Just the basics any on-call would run: az monitor metrics â CPU, memory, request rates Log Analytics queries â Error patterns, exception details, dependency failures App Insights data â Failed requests, stack traces, correlation IDs az containerapp logs â Revision logs, app configuration That's it. Plain markdown with KQL queries and CLI commands. Nothing fancy. What the Agent Does Reads the runbook from its knowledge base Executes each diagnostic step Collects results and evidence Sends me an email with analysis and findings I wake up to an email that says: "CPU spiked to 92% at 2:45am, triggering connection pool exhaustion. Top exception: SqlException (1,832 occurrences). Errors correlate with traffic spike. Recommend scaling to 5 replicas." All the evidence. All the queries used. All the timestamps. Ready for me to act. How to Set This Up (6 Steps) Here's how you can build this yourself: Step 1: Create SRE Agent Create a new SRE Agent in the Azure portal. No Azure resource groups to configure. If your apps run on Azure, the agent pulls context from the incident itself. If your apps run elsewhere, you don't need Azure resource configuration at all. Step 2: Grant Reader Permission (Optional) If your runbooks execute against Azure resources, assign Reader role to the SRE Agent's managed identity on your subscription. This allows the agent to run az commands and query metrics. Skip this if your runbooks target non-Azure apps. Step 3: Add Your Runbook to SRE Agent's Knowledge base You already have runbooks, they're in your wiki, Confluence, or team docs. Just add them as .md files to the agent's knowledge base. To learn about other ways to link your runbooks to the agent, read this Step 4: Connect Outlook Connect the agent to your Outlook so it can send you the analysis email with findings. Step 5: Create a Subagent Create a subagent with simple instructions like: "You are an expert in triaging and diagnosing incidents. When triggered, search the knowledge base for the relevant runbook, execute the diagnostic steps, collect evidence, and send an email summary with your findings." Assign the tools the agent needs: RunAzCliReadCommands â for az monitor, az containerapp commands QueryLogAnalyticsByWorkspaceId â for KQL queries against Log Analytics QueryAppInsightsByResourceId â for App Insights data SearchMemory â to find the right runbook SendOutlookEmail â to deliver the analysis Step 6: Set Up Incident Trigger Connect your incident management tool - PagerDuty, ServiceNow, or Azure Monitor alerts and setup the incident trigger to the subagent. When an incident fires, the agent kicks off automatically. That's it. Your agentic workflow now looks like this: This Works for Any App, Not Just Azure Here's the thing: SRE Agent is platform agnostic. It's executing your runbooks, whatever they contain. On-prem databases? Add your diagnostic SQL. Custom monitoring stack? Add those API calls. The agent doesn't care where your app runs. It cares about following your runbook and getting you answers. Why This Matters Lower MTTR. By the time you're awake and coherent, the analysis is done. Consistent execution. No missed steps. No "I forgot to check the dependencies" at 4am. Evidence for postmortems. Every query, every result, timestamped and documented. Focus on what matters. Your brain should be deciding what to do not gathering data. The Bottom Line On-call runbook execution is the most common, most tedious, and most automatable part of incident response. It's grunt work that pulls engineers away from the creative problem-solving they were hired for. SRE Agent offloads that work from your plate. You write the runbook once, and the agent executes it every time, faster and more consistently than any human at 3am. Stop running runbooks. Start reviewing results. Try it yourself: Create a markdown runbook with your diagnostic queries and commands, add it to your SRE Agent's knowledge base, and let the agent handle your next incident. Your 3am self will thank you.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 whatâs new and what has changed since the last update. Whatâs New: Automation, Integration, and Extensibility Azure SRE Agent just got a major upgrade. From no-code automation to seamless integrations and expanded data connectivity, hereâs whatâs new in this release: No-code Sub-Agent Builder: Rapidly create custom automations without writing code. Flexible, event-driven triggers: Instantly respond to incidents and operational changes. Expanded data connectivity: Unify diagnostics and troubleshooting across more data sources. Custom actions: Integrate with your existing tools and orchestrate end-to-end workflows via MCP. Prebuilt operational scenarios: Accelerate deployment and improve reliability out of the box. Unlike generic agent platforms, Azure SRE Agent comes with deep integrations, prebuilt tools, and frameworks specifically for IT, DevOps, and SRE workflows. This means you can automate complex operational tasks faster and more reliably, tailored to your organizationâs needs. Sub-Agent Builder: Custom Automation, No Code Required Empower teams to automate repetitive operational tasks without coding expertise, dramatically reducing manual workload and development cycles. This feature helps address the need for targeted automation, letting teams solve specific operational pain points without relying on one-size-fits-all solutions. Modular Sub-Agents: Easily create custom sub-agents tailored to your teamâs needs. Each sub-agent can have its own instructions, triggers, and toolsets, letting you automate everything from outage response to customer email triage. Prebuilt System Tools: Eliminate the inefficiency of creating basic automation from scratch, and choose from a rich library of hundreds of built-in tools for Azure operations, code analysis, deployment management, diagnostics, and more. Custom Logic: Align automation to your unique business processes by defining your automation logic and prompts, teaching the agent to act exactly as your workflow requires. Flexible Triggers: Automate on Your Terms Invoke the agent to respond automatically to mission-critical events, not wait for manual commands. This feature helps speed up incident response and eliminate missed opportunities for efficiency. Multi-Source Triggers: Go beyond chat-based interactions, and trigger the agent to automatically respond to Incident Management and Ticketing systems like PagerDuty and ServiceNow, Observability Alerting systems like Azure Monitor Alerts, or even on a cron-based schedule for proactive monitoring and best-practices checks. Additional trigger sources such as GitHub issues, Azure DevOps pipelines, email, etc. will be added over time. This means automation can start exactly when and where you need it. Event-Driven Operations: Integrate with your CI/CD, monitoring, or support systems to launch automations in response to real-world events - like deployments, incidents, or customer requests. Vital for reducing downtime, it ensures that business-critical actions happen automatically and promptly. Expanded Data Connectivity: Unified Observability and Troubleshooting Integrate data, enabling comprehensive diagnostics and troubleshooting and faster, more informed decision-making by eliminating silos and speeding up issue resolution. Multiple Data Sources: The agent can now read data from Azure Monitor, Log Analytics, and Application Insights based on its Azure role-based access control (RBAC). Additional observability data sources such as Dynatrace, New Relic, Datadog, and more can be added via the Remote Model Context Protocol (MCP) servers for these tools. This gives you a unified view for diagnostics and automation. Knowledge Integration: Rather than manually detailing every instruction in your prompt, you can upload your Troubleshooting Guide (TSG) or Runbook directly, allowing the agent to automatically create an execution plan from the file. You may also connect the agent to resources like SharePoint, Jira, or documentation repositories through Remote MCP servers, enabling it to retrieve needed files on its own. This approach utilizes your organizationâs existing knowledge base, streamlining onboarding and enhancing consistency in managing incidents. Azure SRE Agent is also building multi-agent collaboration by integrating with PagerDuty and Neubird, enabling advanced, cross-platform incident management and reliability across diverse environments. Custom Actions: Automate Anything, Anywhere Extend automation beyond Azure and integrate with any tool or workflow, solving the problem of limited automation scope and enabling end-to-end process orchestration. Out-of-the-Box Actions: Instantly automate common tasks like running azcli, kubectl, creating GitHub issues, or updating Azure resources, reducing setup time and operational overhead. Communication Notifications: The SRE Agent now features built-in connectors for Outlook, enabling automated email notifications, and for Microsoft Teams, allowing it to post messages directly to Teams channels for streamlined communication. Bring Your Own Actions: Drop in your own Remote MCP servers to extend the agentâs capabilities to any custom tool or workflow. Future-proof your agentic DevOps by automating proprietary or emerging processes with confidence. Prebuilt Operations Scenarios Address common operational challenges out of the box, saving teams time and effort while improving reliability and customer satisfaction. Incident Response: Minimize business impact and reduce operational risk by automating detection, diagnosis, and mitigation of your workload stack. The agent has built-in runbooks for common issues related to many Azure resource types including Azure Kubernetes Service (AKS), Azure Container Apps (ACA), Azure App Service, Azure Logic Apps, Azure Database for PostgreSQL, Azure CosmosDB, Azure VMs, etc. Support for additional resource types is being added continually, please see product documentation for the latest information. Root Cause Analysis & IaC Drift Detection: Instantly pinpoint incident causes with AI-driven root cause analysis including automated source code scanning via GitHub and Azure DevOps integration. Proactively detect and resolve infrastructure drift by comparing live cloud environments against source-controlled IaC, ensuring configuration consistency and compliance. Handle Complex Investigations: Enable the deep investigation mode that uses a hypothesis-driven method to analyze possible root causes. It collects logs and metrics, tests hypotheses with iterative checks, and documents findings. The process delivers a clear summary and actionable steps to help teams accurately resolve critical issues. Incident Analysis: The integrated dashboard offers a comprehensive overview of all incidents managed by the SRE Agent. It presents essential metrics, including the number of incidents reviewed, assisted, and mitigated by the agent, as well as those awaiting human intervention. Users can leverage aggregated visualizations and AI-generated root cause analyses to gain insights into incident processing, identify trends, enhance response strategies, and detect areas for improvement in incident management. Inbuilt Agent Memory: The new SRE Agent Memory System transforms incident response by institutionalizing the expertise of top SREs - capturing, indexing, and reusing critical knowledge from past incidents, investigations, and user guidance. Benefit from faster, more accurate troubleshooting, as the agent learns from both successes and mistakes, surfacing relevant insights, runbooks, and mitigation strategies exactly when needed. This system leverages advanced retrieval techniques and a domain-aware schema to ensure every on-call engagement is smarter than the last, reducing mean time to resolution (MTTR) and minimizing repeated toil. Automatically gain a continuously improving agent that remembers what works, avoids past pitfalls, and delivers actionable guidance tailored to the environment. GitHub Copilot and Azure DevOps Integration: Automatically triage, respond to, and resolve issues raised in GitHub or Azure DevOps. Integration with modern development platforms such as GitHub Copilot coding agent increases efficiency and ensures that issues are resolved faster, reducing bottlenecks in the development lifecycle. Ready to get started? Azure SRE Agent home page Product overview Pricing Page Pricing Calculator Pricing Blog Demo recordings Deployment samples Whatâs Next? Give us feedback: Your feedback is critical - You can Thumbs Up / Thumbs Down each interaction or thread, or go to the âGive Feedbackâ button in the agent to give us in-product feedback - or you can create issues or just share your thoughts in our GitHub repo at https://github.com/microsoft/sre-agent. Weâre just getting started. In the coming months, expect even more prebuilt integrations, expanded data sources, and new automation scenarios. We anticipate continuous growth and improvement throughout our agentic AI platforms and services to effectively address customer needs and preferences. Let us know what Ops toil you want to automate next!Building AI apps and agents for the new frontier
Every new wave of applications brings with it the promise of reshaping how we work, build and create. From digitization to web, from cloud to mobile, these shifts have made us all more connected, more engaged and more powerful. The incoming wave of agentic applications, estimated to number 1.3 billion over the next 2 years[1] is no different. But the expectations of these new services are unprecedented, in part for how they will uniquely operate with both intelligence and agency, how they will act on our behalf, integrated as a member of our teams and as a part of our everyday lives. The businesses already achieving the greatest impact from agents are what we call Frontier Organizations. This week at Microsoft Ignite weâre showcasing what the best frontier organizations are delivering, for their employees, for their customers and for their markets. And weâre introducing an incredible slate of innovative services and tools that will help every organization achieve this same frontier transformation. What excites me most is how frontier organizations are applying AI to achieve their greatest level of creativity and problem solving. Beyond incremental increases in efficiency or cost savings, frontier firms use AI to accelerate the pace of innovation, shortening the gap from prototype to production, and continuously refining services to drive market fit. Frontier organizations arenât just moving faster, they are using AI and agents to operate in novel ways, redefining traditional business processes, evolving traditional roles and using agent fleets to augment and expand their workforce. To do this they build with intent, build for impact and ground services in deep, continuously evolving, context of you, your organization and your market that makes every service, every interaction, hyper personalized, relevant and engaging. Today weâre announcing new capabilities that help you build what was previously impossible. To launch and scale fleets of agents in an open system across models, tools, and knowledge. And to run and operate agents with the confidence that every service is secure, governed and trusted. The question is, how do you get there? How do you build the AI apps and agents fueling the future? Read further for just a few highlights of how Microsoft can help you become frontier: Build with agentic DevOps Perhaps the greatest area of agentic innovation today is in service of developers. Microsoftâs strategy for agentic DevOps is redefining the developer experience to be AI-native, extending the power of AI to every stage of the software lifecycle and integrating AI services into the tools embraced by millions of developers. At Ignite, weâre helping every developer build faster, build with greater quality and security and deliver increasingly innovative apps that will shape their businesses. Across our developer services, AI agents now operate like an active member of your development and operations teams â collaborating, automating, and accelerating every phase of the software development lifecycle. From planning and coding to deployment and production, agents are reshaping how we build. And developers can now orchestrate fleets of agents, assigning tasks to agents to execute code reviews, testing, defect resolution, and even modernization of legacy Java and .NET applications. We continue to take this strategy forward with a new generation of AI-powered tools, with GitHub Agent HQ making coding agents like Codex, Claude Code, and Jules available soon directly in GitHub and Visual Studio Code, to Custom Agents to encode domain expertise, and âbring your own modelsâ to empower teams to adapt and innovate. Itâs these advancements that make GitHub Copilot, the worldâs the most popular AI pair programmer, serving over 26 million users and helping organizations like Pantone, Ahold Delhaize USA, and Commerzbank streamline processes and save time. Within Microsoftâs own developer teams, weâre seeing transformative results with agentic DevOps. GitHub Copilot coding agent is now a top contributorânot only to GitHubâs core application but also to our major open-source projects like the Microsoft Agent Framework and Aspire. Copilot is reducing task completion time from hours to minutes and eliminating up to two weeks of manual development effort for complex work. Across Microsoft, 90% of pull requests are now covered by GitHub Copilot code review, increasing the pace of PR completion. Our AI-powered assistant for Microsoftâs engineering ecosystem is deeply integrated into VS Code, Teams, and other tools, giving engineers and product managers real-time, context-aware answers where they workâsaving 2.2k developer days in September alone. For app modernization, GitHub Copilot has reduced modernization project timelines by as much as 88%. In production environments, Azure SRE agent has handled over 7K incidents and collected diagnostics on over 18K incidents, saving over 10,000 hours for on-call engineers. These results underscore how agentic workflows are redefining speed, scale, and reliability across the software lifecycle at Microsoft. Launch at speed and scale with a full-stack AI app and agent platform Weâre making it easier to build, run, and scale AI agents that deliver real business outcomes. To accelerate the path to production for advanced AI applications and agents is delivering a complete, and flexible foundation that helps every organization move with speed and intelligence without compromising security, governance or operations. Microsoft Foundry helps organizations move from experimentation to execution at scale, providing the organization-wide observability and control that production AI requires. More than 80,000 customers, including 80% of the Fortune 500, use Microsoft Foundry to build, optimize, and govern AI apps and agents today. Foundry supports open frameworks like the Microsoft Agent Framework for orchestration, standard protocols like Model Context Protocol (MCP) for tool calling, and expansive integrations that enable context-aware, action-oriented agents. Companies like Nasdaq, Softbank, Sierra AI, and Blue Yonder are shipping innovative solutions with speed and precision. New at Ignite this year: Foundry Models With more than 11,000 models like OpenAIâs GPT-5, Anthropicâs Claude, and Microsoftâs Phi at their fingertips, developers, Foundry delivers the broadest model selection on any cloud. Developers have the power to benchmark, compare, and dynamically route models to optimize performance for every task. Model router is now generally available in Microsoft Foundry and in public preview in Foundry Agent Service. Foundry IQ, Delivering the deep context needed to make every agent grounded, productive, and reliable. Foundry IQ, now available in public preview, reimagines retrieval-augmented generation (RAG) as a dynamic reasoning process rather than a one-time lookup. Powered by Azure AI Search, it centralizes RAG workflows into a single grounding API, simplifying orchestration and improving response quality while respecting user permissions and data classifications. Foundry Agent Service now offers Hosted Agents, multi-agent workflows, built-in memory, and the ability to deploy agents directly to Microsoft 365 and Agent 365 in public preview. Foundry Tools, empowers developers to create agents with secure, real-time access to business systems, business logic, and multimodal capabilities. Developers can quickly enrich agents with real-time business context, multimodal capabilities, and custom business logic through secure, governed integration with 1,400+ systems and APIs. Foundry Control Plane, now in public preview, centralizes identity, policy, observability, and security signals and capabilities for AI developers in one portal. Build on an AI-Ready foundation for all applications Managed Instance on Azure App Service lets organizations migrate existing .NET web applications to the cloud without the cost or effort of rewriting code, allowing them to migrate directly into a fully managed platform-as-a-service (PaaS) environment. With Managed Instance, organizations can keep operating applications with critical dependencies on local Windows services, third-party vendor libraries, and custom runtimes without requiring any code changes. The result is faster modernizations with lower overhead, and access to cloud-native scalability, built-in security and Azureâs AI capabilities. MCP Governance with Azure API Management now delivers a unified control plane for APIs and MCP servers, enabling enterprises to extend their existing API investments directly into the agentic ecosystem with trusted governance, secure access, and full observability. Agent Loop and native AI integrations in Azure Logic Apps enable customers to move beyond rigid workflows to intelligent, adaptive automation that saves time and reduces complexity. These capabilities make it easier to build AI-powered, context-aware applications using low-code tools, accelerating innovation without heavy development effort. Azure Functions now supports hosting production-ready, reliable AI agents with stateful sessions, durable tool calls, and deterministic multi-agent orchestrations through the durable extension for Microsoft Agent Framework. Developers gain automatic session management, built-in HTTP endpoints, and elastic scaling from zero to thousands of instances â all with pay-per-use pricing and automated infrastructure. Azure Container Apps agents and security supercharges agentic workloads with automated deployment of multi-container agents, on-demand dynamic execution environments, and built-in security for runtime protection, and data confidentiality. Run and operate agents with confidence New at Ignite, weâre also expanding the use of agents to keep every application secure, managed and operating without compromise. Expanded agentic capabilities protect applications from code to cloud and continuously monitor and remediate production issues, while minimizing the efforts on developers, operators and security teams. Microsoft Defender for Cloud and GitHub Advanced Security: With the rise of multi-agent systems, the security threat surface continues to expand. Increased alert volumes, unprioritized threat signals, unresolved threats and a growing backlog of vulnerabilities is increasing risk for businesses while security teams and developers often operate in disconnected tools, making collaboration and remediation even more challenging. The new Defender for Cloud and GitHub Advanced Security integration closes this gap, connecting runtime context to code for faster alert prioritization and AI-powered remediation. Runtime context prioritizes security risks with insights that allow teams to focus on what matters most and fix issues faster with AI-powered remediation. When Defender for Cloud finds a threat exposed in production, it can now link to the exact code in GitHub. Developers receive AI suggested fixes directly inside GitHub, while security teams track progress in Defender for Cloud in real time. This gives both sides a faster, more connected way to identify issues, drive remediation, and keep AI systems secure throughout the app lifecycle. Azure SRE Agent is an always-on, AI-powered partner for cloud reliability, enabling production environments to become self-healing, proactively resolve issues, and optimize performance. Seamlessly integrated with Azure Monitor, GitHub Copilot, and incident management tools, Azure SRE Agent reduces operational toil. The latest update introduces no-code automation, empowering teams to tailor processes to their unique environments with minimal engineering overhead. Event-driven triggers enable proactive checks and faster incident response, helping minimize downtime. Expanded observability across Azure and third-party sources is designed to help teams troubleshoot production issues more efficiently, while orchestration capabilities support integration with MCP-compatible tools for comprehensive process automation. Finally, its adaptive memory system is designed to learn from interactions, helping improve incident handling and reduce operational toil, so organizations can achieve greater reliability and cost efficiency. The future is yours to build We are living in an extraordinary time, and across Microsoft weâre focused on helping every organization shape their future with AI. Todayâs announcements are a big step forward on this journey. Whether youâre a startup fostering the next great concept or a global enterprise shaping your future, we can help you deliver on this vision. The frontier is open. Letâs build beyond expectations and build the future! Check out all the learning at Microsoft Ignite on-demand and read more about the announcements making it happen at: Recommended sessions BRK113: Connected, managed, and complete BRK103: Modernize your apps in days, not months, with GitHub Copilot BRK110: Build AI Apps fast with GitHub and Microsoft Foundry in action BRK100: Best practices to modernize your apps and databases at scale BRK114: AI Agent architectures, pitfalls and real-world business impact BRK115: Inside Microsoft's AI transformation across the software lifecycle Announcements aka.ms/AgentFactory aka.ms/AppModernizationBlog aka.ms/SecureCodetoCloudBlog aka.ms/AppPlatformBlog [1] IDC Info Snapshot, sponsored by Microsoft, 1.3 Billion AI Agents by 2028, #US53361825 and May 2025.Compose for Agents on Azure Container Apps and Serverless GPU (public preview)
Empowering intelligent applications The next wave of AI is agentic â systems that can reason, plan, and act on our behalf. Whether youâre building a virtual assistant that books travel or a multiâmodel workflow that triages support tickets, these applications rely on multiple models, tools, and services working together. Unfortunately, building them has not been easy: Tooling sprawl.âŻDevelopers must wire together LLMs, vector databases, MCP (Model Context Protocol) tools and orchestration logic, often across disparate SDKs and running processes. Keeping those dependencies in sync for local development and production is tedious and errorâprone. Specialized hardware.âŻLarge language models and agent orchestration frameworks often require GPUs to run effectively. Procuring and managing GPU instances can be costly, particularly for prototypes and small teams. Operational complexity.âŻAgentic applications are typically composed of many services. Scaling them, managing health and secure connectivity, and reproducing the same environment from a developer laptop into production quickly becomes a fullâtime job. Why Azure Container Apps is the right home With AzureâŻContainerâŻApps (ACA), you can now tackle these challenges without sacrificing the familiar Docker Compose workflow that so many developers love. Weâre excited to announce that Compose for Agents is in public preview on Azure Container Apps. This integration brings the power of Dockerâs new agentic tooling to a platform that was built for serverless containers. Hereâs why ACA is the natural home for agentic workloads: Serverless GPUs with perâsecond billing.âŻContainerâŻApps offers serverless GPU compute. Your agentic workloads can run on GPUs only when they need to, and you only pay for the seconds your container is actually running. This makes it economical to prototype and scale complex models without upfront infrastructure commitments. Media reports on the preview note that Dockerâs Offload service uses remote GPUs via cloud providers such as Microsoft to overcome local hardware limits, and ACA brings that capability directly into the Azure native experience. Sandboxed dynamic sessions for tools.âŻMany agentic frameworks execute userâprovided code as part of their workflows. ACAâs dynamic sessions provide secure, shortâlived sandboxes for running these tasks. This means untrusted or transient code (for example, evaluation scripts or thirdâparty plugins) runs in an isolated environment, keeping your production services safe. Fully managed scaling and operations.âŻContainerâŻApps automatically scales each service based on traffic and queue length, and it can scale down to zero when idle. You get builtâin service discovery, ingress, rolling updates and revision management without having to operate your own orchestrator. Developers can focus on building agents rather than patching servers. Firstâclass Docker Compose support.âŻCompose remains a favourite tool for developersâ inner loop and for orchestrating multiâcontainer systems. Compose for Agents extends the format to declare openâsource models, agents and tools alongside your microservices. By pointing docker compose up at ACA, the same YAML file you use locally now deploys automatically to a fully managed container environment. ModelâŻRunner and MCP Gateway built in.âŻDockerâs ModelâŻRunner lets you pull openâweight language models from DockerâŻHub and exposes them via OpenAIâcompatible endpoints, and the MCP (Model Context Protocol)âŻGateway connects your agents to curated tools. ACA integrates these components into your Compose stack, giving you everything you need for retrievalâaugmented generation, vector search or domainâspecific tool invocation. What this means for developers The Compose for Agents public preview on ContainerâŻApps brings together the simplicity of Docker Compose and the operational power of Azureâs serverless compute platform. Developers can now: Define agent stacks declaratively.âŻInstead of cobbling together scripts, you describe your entire agentic application in a single compose.yaml file. Compose already supports popular frameworks like LangGraph, Embabel, VercelâŻAIâŻSDK, SpringâŻAI, CrewâŻAI, GoogleâŻADK and Agno. You can mix and match these frameworks with your own microservices, databases and queues. Run anywhere with the same configuration.âŻDocker emphasizes that you can âdefine your open models, agents and MCPâcompatible tools, then spin up your full agentic stack with a simple dockerâŻcompose upâ. By bringing this workflow to ACA, Microsoft ensures that the same compose file runs unchanged on your laptop and in the cloud. Scale seamlessly.âŻLarge language models and multiâagent orchestration can be computeâintensive. News coverage notes that Dockerâs Offload service provides remote GPUs for these workloads ACA extends that capability with serverless GPUs and automated scaling, letting you test locally and then burst to the cloud with no changes to your YAML. Collaboration with Docker This preview is the result of close collaboration between Microsoft and Docker. A Docker has always been focused on simplifying complex developer workflows. âWith Compose for Agents, weâre extending that same experience that developers know and love from containers to agents, bringing the power of Compose to the emerging world of AI-native, agentic applications. It delivers the same simplicity and predictability to prototyping, testing, and deploying across local and cloud environmentsâ said Elyi Aleyner, VP of Strategy and Head of Tech Alliances at Docker. âWeâre excited to partner with Microsoft to bring this innovation to Azure Container Apps, enabling developers to go from âcompose upâ on their laptops to secure, GPU-backed workloads in the cloud with zero friction.â Empowering choice Every team has its own favourite frameworks and tools. Weâve ensured that Compose for Agents on ACA is frameworkâagnostic: you can use LangGraph for complex workflows, CrewAI for multiâagent coordination, or SpringâŻAI to integrate with your existing Java stack. Want to run a vector store from the MCPâŻcatalog alongside your own service? Simply add it to your Compose file. Dockerâs curated catalog provides over a hundred readyâtoâuse tools and services for retrieval, document summarization, database access and more. ACAâs flexibility means youâre free to choose the stack that best fits your problem. Get started today The public preview of Compose for Agents support in AzureâŻContainerâŻApps is available now. You can: Install the latest Azure Container Apps Extension Define your application in a compose.yaml file, including models, tools and agent code and deploy to ACA via az containerapp compose up. ACA will provision GPU resources, dynamic sessions and autoâscaling infrastructure automatically. Iterate locally using standard docker compose up commands, then push the same configuration to the cloud when youâre ready. For more detailed instructions please go to https://aka.ms/aca/compose-for-agents
What's New in Azure App Service at #MSIgnite 2025
Azure App Service introduces a new approach to accelerate application migration and modernization at Microsoft Ignite 2025. Known as Managed Instance on Azure App Service, it enables seamless modernization of classic web apps to the cloud with minimal code changes, especially for apps with custom Windows dependencies. Other major updates include enhanced Aspire support for .NET developers on Azure App Service for Linux, new AI integration features, expanded language/runtime support, and improvements in scaling, networking, and developer experience.What's new in Azure Container Apps at Ignite'25
Azure Container Apps (ACA) is a fully managed serverless container platform that enables developers to design and deploy microservices and modern apps without requiring container expertise or needing infrastructure management. ACA is rapidly emerging as the preferred platform for hosting AI workloads and intelligent agents in the cloud. With features like code interpreter, Serverless GPUs, simplified deployments, and per-second billing, ACA empowers developers to build, deploy, and scale AI-driven applications with exceptional agility. ACA makes it easy to integrate agent frameworks, leverage GPU acceleration, and manage complex, multi-container AI environments - all while benefiting from a serverless, fully managed infrastructure. External customers like Replit, NFL Combine, Coca-Cola, and European Space Agency as well as internal teams like Microsoft Copilot (as well as many others) have bet on ACA as their compute platform for AI workloads. ACA is quickly becoming the leading platform for updating existing applications and moving them to a cloud-native setup. It allows organizations to seamlessly migrate legacy workloads - such as Java and .NET apps - by using AI-powered tools like GitHub Copilot to automate code upgrades, analyze dependencies, and handle cloud transformations. ACAâs fully managed, serverless environment removes the complexity of container orchestration. This helps teams break down monolithic or on-premises applications into robust microservices, making use of features like version control, traffic management, and advanced networking for fast iteration and deployment. By following proven modernization strategies while ensuring strong security, scalability, and developer efficiency, ACA helps organizations continuously innovate and future-proof their applications in the cloud. Customers like EY, London Stock Exchange, Chevron, and Paychex have unlocked significant business value by modernizing their workloads onto ACA. This blog presents the latest features and capabilities of ACA, enhancing its value for customers by enabling the rapid migration of existing workloads and development of new cloud applications, all while following cloud-native best practices. Secure sandboxes for AI compute ACA now supports dynamic shell sessions, currently available in public preview. These shell sessions are platform-managed built-in containers designed to execute common shell commands within an isolated, sandboxed environment. With the addition of empty shell sessions and an integrated MCP server, ACA enables customers to provision secure, isolated sandboxes instantly - ideal for use cases such as code execution, tool testing, and workflow automation. This functionality facilitates seamless integration with agent frameworks, empowering agents to access disposable compute environments as needed. Customers can benefit from rapid provisioning, improved security, and decreased operational overhead when managing agentic workloads. To learn more about how to add secure sandbox shell sessions to Microsoft Foundry agents as a tool, visit the walkthrough at https://aka.ms/aca/dynamic-sessions-mcp-tutorial. Docker Compose for Agents support ACA has added Docker Compose for Agents support in public preview, making it easy for developers to define agentic applications stack-agnostic, with MCP and custom model support. Combined with native serverless GPU support, Docker Compose for Agents allows fast iteration and scaling for AI-driven agents and application using LangGraph, LangChain CrewAI, Spring AI, Vercel AI SDK and Agno. These enhancements provide a developer-focused platform that streamlines the process for modern AI workloads, bringing together both development and production cycles into one unified environment. Additional regional availability for Serverless GPUs Serverless GPU solutions offer capabilities such as automatic scaling with NVIDIA A100 or T4 GPUs, per-second billing, and strict data isolation within container boundaries. ACA Serverless GPUs are now generally available in 11 additional regions, further facilitating developersâ ability to deploy AI inference, model training, and GPU-accelerated workloads efficiently. For further details on supported regions, please visit https://aka.ms/aca/serverless-gpu-regions. New Flexible Workload Profile The Flexible workload profile is a new option that combines the simplicity of serverless Consumption with the performance and control in Dedicated profiles. It offers a familiar pay-per-use model along with enhanced features like scheduled maintenance, dedicated networking, and support for larger replicas to meet demanding application needs. Customers can enjoy the advantages of dedicated resources together with effortless infrastructure management and billing from the Consumption model. Operating on a dedicated compute pool, this profile ensures better predictability and isolation without introducing extra operational complexity. It is designed for users who want the ease of serverless scaling, but also need more control over performance and environmental stability. Confidential Computing Confidential computing support is now available in public preview for ACA, offering hardware-based Trusted Execution Environments (TEEs) to secure data in use. This adds to existing encryption of data at rest and in transit by encrypting memory and verifying the cloud environment before processing. It helps protect sensitive data from unauthorized access, including by cloud operators, and is useful for organizations with high security needs. Confidential computing can be enabled via workload profiles, with the preview limited to certain regions. Extending Network capabilities General Availability of Rule-based Routing Rule-based routing for ACA is now generally available, offering users improved flexibility and easier composition when designing microservice architectures, conducting A/B testing, or implementing blue-green deployments. With this feature, you can route incoming HTTP traffic to specific apps within your environment by specifying host names or paths - including support for custom domains. You no longer need to set up an extra reverse proxy (like NGINX); simply define routing rules for your environment, and traffic will be automatically directed to the appropriate target apps. General Availability of Premium Ingress ACA support for Premium Ingress is now Generally Available. This feature introduces environment-level ingress configuration options, with the primary highlight being customizable ingress scaling. This capability supports the scaling of the ingress proxy, enabling customers to better handle higher demand workloads, such as large performance tests. By configuring your ingress proxy to run on workload profiles, you can scale out more ingress instances to handle more load. Running the ingress proxy on a workload profile will incur associated costs. To further enhance the flexibility of your application, this release includes other ingress-related settings, such as termination grace period, idle request timeout, and header count. Additional Management capabilities Public Preview of Deployment labels ACA now offers deployment labels in public preview, letting you assign names like dev, staging, or prod to container revisions which can be automatically assigned. This makes environment management easier and supports advanced strategies such as A/B testing and blue-green deployments. Labels help route traffic, control revisions, and streamline rollouts or rollbacks with minimal hassle. With deployment labels, you can manage app lifecycles more efficiently and reduce complexity across environments. General Availability of Durable Task Scheduler support Durable Task Scheduler (DTS) support is now generally available on ACA, empowering users with a robust pro-code workflow solution. With DTS, you can define reliable, containerized workflows as code, benefiting from built-in state persistence and fault-tolerant execution. This enhancement streamlines the creation and administration of complex workflows by boosting scalability, reliability, and enabling efficient monitoring capabilities. Whatâs next ACA is redefining how developers build and deploy intelligent agents. Agents deployed to Azure Container Apps with Microsoft Agent Framework and Open Telemetry can also be plugged directly into Microsoft Foundry, giving teams a single pane of glass for their agents in Azure. With serverless scale, GPU-on-demand, and enterprise-grade isolation, ACA provides the ideal foundation for hosting AI agents securely and cost-effectively. Utilizing open-source frameworks such as n8n on ACA enables the deployment of no-code automation agents that integrate seamlessly with Azure OpenAI models, supporting intelligent routing, summarization, and adaptive decision-making processes. Similarly, running other agent frameworks like Goose AI Agent on ACA enables it to operate concurrently with model inference workloads (including Ollama and GPT-OSS) within a unified, secure environment. The inclusion of serverless GPU support allows for efficient hosting of large language models such as GPT-OSS, optimizing both cost and scalability for inference tasks. Furthermore, ACA facilitates the remote hosting of Model Context Protocol (MCP) servers, granting agents secure access to external tools and APIs via streamable HTTP transport. Collectively, these features enable organizations to develop, scale, and manage complex agentic workloads - from workflow automation to AI-driven assistants - while leveraging ACAâs enterprise-grade security, autoscaling capabilities, and developer-centric user experience. In addition to these, ACA also enables a wide range of cross-compatibility with various frameworks and services, making it an ideal platform for running Azure Functions on ACA, Distributed Application Runtime (Dapr) microservices, as well as polyglot apps across .NET / Java / JavaScript. As always, we invite you to visit our GitHub page for feedback, feature requests, or questions about Azure Container Apps, where you can open a new issue or up-vote existing ones. If youâre curious about what weâre working on next, check out our roadmap. We look forward to hearing from you!
