Identity Bindings: A Cleaner Model for Multi‑Cluster Identity in AKS
AKS has supported assigning Azure Managed Identities to pods for some time, first through Pod Identity and then later through Workload Identity. Using these tools it is possible to give a pod an Azure Identity that it can use to interact with other Azure services - pull secrets from Key Vault, read a file from Blob Storage or write to a database. Workload Identity is the latest incarnation of this feature and significantly simplified this feature, removing the need to run additional management pods in the cluster and to have the identity injected in every node however it does have some issues of it's own. These issues are particularly evident when operating at scale and wanting to share the same Managed Identity across multiple workloads in the same cluster, or across multiple clusters. Workload Identity relies on creating a Federated Identity Credential (FIC) in Azure that defines the trust relationship between the AKS OIDC issuer and Entra ID. Each combination of Service Account and Namespace that uses the same Managed Identity requires a separate FIC, as do services running in different clusters. As your scale grows, this can start to become a problem. Each managed identity can only support up to 20 FICs. Once you hit that limit, your only option is to create another Managed Identity. This leads to the proliferation of Managed Identities that have the same permissions and do the same job, but only exist to work around this problem. In addition to the 20 FIC limit, there are some other issues with Workload Identity: Creation of the FIC is an Azure Resource creation that often needs to occur alongside Kubernetes resource creation and makes automation of app deployment harder There can be a cyclic dependency issue where the service account in Kubernetes needs to know the Identity details before the pod is created, but the FIC needs the service account and namespace details to create the OIDC binding Additional outbound rules are required to allow the AKS cluster to access the Entra ID (login.microsoftonline.com) endpoints Introducing Identity Bindings Identity Bindings are currently in preview. Previews are provided "as is" and "as available," and they're excluded from the service-level agreements and limited warranty. AKS previews are partially covered by customer support on a best-effort basis. As such, these features aren't meant for production use. Identity Bindings introduce a cleaner, RBAC-driven approach to identity management in AKS. Instead of juggling multiple federated credentials and namespace scoping, you define bindings that link Kubernetes RBAC roles to Azure identities. Pods then request tokens via an AKS-hosted proxy, no external egress required. Key benefits: Centralised Access Control: Authorisation flows through Kubernetes RBAC. Cross-Cluster Identity Reuse: Federated credentials can be shared across namespaces and clusters. Reduced Networking Requirements: No outbound calls for token acquisition; everything stays within the cluster. Simplified IaC: Eliminates the “chicken-and-egg” problem when deploying identities alongside workloads. Identity Bindings act as the link between applications running in AKS and the Azure managed identities they need to use. Instead of every cluster or namespace requiring its own federated identity configuration, each application is authorised through an Identity Binding defined inside the cluster. The binding expresses which workloads (via service accounts and RBAC) are allowed to request tokens for a given identity. When a pod needs a token, AKS validates the request against the binding, and if it matches, the request is routed through the cluster’s identity proxy to the single Federated Identity Credential (FIC) associated with the managed identity. The FIC then exchanges the pod’s OIDC token for an Azure access token. This pattern allows multiple clusters or namespaces to share one managed identity cleanly, while keeping all workload‑level authorisation decisions inside Kubernetes. With Workload Identity, every workload using a managed identity required its own Federated Identity Credential (FIC) tied to a specific namespace and service account, and you had to repeat that for every cluster. Hitting the 20‑FIC limit often forced teams to create duplicate managed identities, and deployments had to be carefully ordered to avoid cyclic dependencies. You also needed outbound access to Entra ID for token requests. Identity Bindings significantly simplifies this. You create a single binding per cluster–identity pair, authorise workloads through RBAC, and let AKS handle token exchange internally with no external egress. There is no FIC sprawl, no need for identity duplication and less complexity in your automation. Using Identity Bindings To get started with using Identity Bindings, you need an AKS cluster and a Managed Identity created. Your Managed Identity should be granted permissions to access the Azure resources you require. The first thing you need to do is ensure the feature is registered. az feature register --namespace Microsoft.ContainerService --name IdentityBindingPreview Next, we need to do is create the identity binding. This is a one-to-one mapping between AKS cluster and Managed Identity, so only needs to be created once for each clusters/identity mapping. You provide the name of the cluster you want the binding mapped to, the full resource ID of the Managed Identity resources, and the name you want to give this binding, and this maps the two resources together. This only needs to be created once per cluster and all further administration is done via Kubernetes. az aks identity-binding create -g "<resource group name>" --cluster-name "<cluster name>" -n "<binding name>" --managed-identity-resource-id "<managed identity Azure Resource ID>" Once this has been created, we need to configure access to it inside Kubernetes. To do this we create a ClusterRole which references the Managed Identity ID. Note that this must be a ClusterRole, it cannot just be a Role. apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: identity-binding-user rules: - verbs: ["use-managed-identity"] apiGroups: ["cid.wi.aks.azure.com"] resources: ["<MI_CLIENT_ID>"] Once this ClusterRole is created, we can assign it to any Namespace and Service Account combination we require, using a ClusterRoleBinding. Indentity Bindings are accessible to all Pods that use that Service Account, in that Namespace. apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: <clusterrole name> roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: identity-binding-user subjects: - kind: ServiceAccount name: <service account name> namespace: <namespace of service account> Now all that is left to do is configure the Pod to use the Identity Binding, there are a two steps to this. First we need to apply the required labels and annotations to the pod to enable Identity Binding support: metadata: labels: azure.workload.identity/use: "true" annotations: azure.workload.identity/use-identity-binding: "true" Then, we need to ensure that the Pod is running using the Service Account we granted permission to use the Identity Binding. spec: serviceAccountName: <service account name> Below is an example deployment that uses Identity Bindings. apiVersion: apps/v1 kind: Deployment metadata: name: keyvault-demo namespace: identity-binding-demo spec: replicas: 1 selector: matchLabels: app: keyvault-demo template: metadata: labels: app: keyvault-demo azure.workload.identity/use: "true" annotations: azure.workload.identity/use-identity-binding: "true" spec: serviceAccountName: keyvault-demo-sa containers: - name: keyvault-demo ... Once this Pod has been created, the Identity Binding should be attached and you should then be able to use it within your application using your SDK and language of choice. You can see an example of consuming an Identity Binding in GO here . Demo App If you want to deploy a demo workload to test out your bindings, you can use the Pod definition below. This requires you to deploy a Key Vault, and grant your managed identity the "Key Vault Secret User" role on that Key Vault. You will also need to update the service principle and namespace to match your environment. apiVersion: v1 kind: Pod metadata: name: demo namespace: demo labels: azure.workload.identity/use: "true" annotations: azure.workload.identity/use-identity-binding: "true" spec: serviceAccount: demo containers: - name: azure-sdk # source code: https://github.com/Azure/azure-workload-identity/blob/feature/custom-token-endpoint/examples/identitybinding-msal-go/main.go image: ghcr.io/bahe-msft/azure-workload-identity/identitybinding-msal-go:latest-linux-amd64 env: - name: KEYVAULT_URL value: ${KEYVAULT_URL} - name: SECRET_NAME value: ${KEYVAULT_SECRET_NAME} restartPolicy: Never Once deployed, if you look at the logs, you should see that it is able to read the secret from Key Vault. kubectl logs demo -n demo I1107 20:03:42.865180 1 main.go:77] "successfully got secret" secret="Hello!" Conclusion Identity Bindings offer a much cleaner model for managing workload identities in AKS, especially once you start operating at scale. By moving authorisation decisions into Kubernetes and relying on a single Federated Identity Credential per managed identity, they avoid the FIC sprawl, cyclic dependencies, and networking requirements that made Workload Identity harder to operate in larger environments. The end result is a simpler, more predictable way to let pods acquire Azure tokens. If you’re already using Workload Identity today, Identity Bindings are a natural evolution that reduces operational friction while keeping the security properties you expect. Further Reading Identity Bindings Overview Setup Identity Bindings
Find 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.Expanding the Public Preview of the Azure SRE Agent
We are excited to share that the Azure SRE Agent is now available in public preview for everyone instantly – no sign up required. A big thank you to all our preview customers who provided feedback and helped shape this release! Watching teams put the SRE Agent to work taught us a ton, and we’ve baked those lessons into a smarter, more resilient, and enterprise-ready experience. You can now find Azure SRE Agent directly in the Azure Portal and get started, or use the link below. 📖 Learn more about SRE Agent. 👉 Create your first SRE Agent (Azure login required) What’s New in Azure SRE Agent - October Update The Azure SRE Agent now delivers secure-by-default governance, deeper diagnostics, and extensible automation—built for scale. It can even resolve incidents autonomously by following your team’s runbooks. With native integrations across Azure Monitor, GitHub, ServiceNow, and PagerDuty, it supports root cause analysis using both source code and historical patterns. And since September 1, billing and reporting are available via Azure Agent Units (AAUs). Please visit product documentation for the latest updates. Here are a few highlights for this month: Prioritizing enterprise governance and security: By default, the Azure SRE Agent operates with least-privilege access and never executes write actions on Azure resources without explicit human approval. Additionally, it uses role-based access control (RBAC) so organizations can assign read-only or approver roles, providing clear oversight and traceability from day one. This allows teams to choose their desired level of autonomy from read-only insights to approval-gated actions to full automation without compromising control. Covering the breadth and depth of Azure: The Azure SRE Agent helps teams manage and understand their entire Azure footprint. With built-in support for AZ CLI and kubectl, it works across all Azure services. But it doesn’t stop there—diagnostics are enhanced for platforms like PostgreSQL, API Management, Azure Functions, AKS, Azure Container Apps, and Azure App Service. Whether you're running microservices or managing monoliths, the agent delivers consistent automation and deep insights across your cloud environment. Automating Incident Management: The Azure SRE Agent now plugs directly into Azure Monitor, PagerDuty, and ServiceNow to streamline incident detection and resolution. These integrations let the Agent ingest alerts and trigger workflows that match your team’s existing tools—so you can respond faster, with less manual effort. Engineered for extensibility: The Azure SRE Agent incident management approach lets teams reuse existing runbooks and customize response plans to fit their unique workflows. Whether you want to keep a human in the loop or empower the Agent to autonomously mitigate and resolve issues, the choice is yours. This flexibility gives teams the freedom to evolve—from guided actions to trusted autonomy—without ever giving up control. Root cause, meet source code: The Azure SRE Agent now supports code-aware root cause analysis (RCA) by linking diagnostics directly to source context in GitHub and Azure DevOps. This tight integration helps teams trace incidents back to the exact code changes that triggered them—accelerating resolution and boosting confidence in automated responses. By bridging operational signals with engineering workflows, the agent makes RCA faster, clearer, and more actionable. Close the loop with DevOps: The Azure SRE Agent now generates incident summary reports directly in GitHub and Azure DevOps—complete with diagnostic context. These reports can be assigned to a GitHub Copilot coding agent, which automatically creates pull requests and merges validated fixes. Every incident becomes an actionable code change, driving permanent resolution instead of temporary mitigation. Getting Started Start here: Create a new SRE Agent in the Azure portal (Azure login required) Blog: Announcing a flexible, predictable billing model for Azure SRE Agent Blog: Enterprise-ready and extensible – Update on the Azure SRE Agent preview Product documentation Product home page Community & Support We’d love to hear from you! Please use our GitHub repo to file issues, request features, or share feedback with the teamAnnouncing a flexible, predictable billing model for Azure SRE Agent
Billing for Azure SRE Agent will start on September 1, 2025. Announced at Microsoft Build 2025, Azure SRE Agent is a pre-built AI agent for root cause analysis, uptime improvement, and operational cost reduction. Learn more about the billing model and example scenarios.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/issuesAnnouncing Advanced Kubernetes Troubleshooting Agent Capabilities (preview) in Azure Copilot
What’s new? Today, we're announcing Kubernetes troubleshooting agent capabilities in Azure Copilot, offering an intuitive, guided agentic experience that helps users detect, triage, and resolve common Kubernetes issues in their AKS clusters. The agent can provide root cause analysis for Kubernetes clusters and resources and is triggered by Kubernetes-specific keywords. It can detect problems like resource failures and scaling bottlenecks and intelligently correlates signals across metrics and events using `kubectl` commands when reasoning and provides actionable solutions. By simplifying complex diagnostics and offering clear next steps, the agent empowers users to troubleshoot independently. How it works With Kubernetes troubleshooting agent, Azure Copilot automatically investigates issues in your cluster by running targeted `kubectl` commands and analyzing your cluster’s configuration and current state. For instance, it identifies failing or pending pods, cluster events, resource utilization metrics, and configuration details to build a complete picture of what’s causing the issue. Azure Copilot then determines the most effective mitigation steps for your specific environment. It provides clear, step-by-step guidance, and in many cases, offers a one-click fix to resolve the issue automatically. If Azure Copilot can’t fully resolve the problem, it can generate a pre-populated support request with all the diagnostic details Microsoft Support needs. You’ll be able to review and confirm everything before the request is submitted. This agent is available via Azure Copilot in the Azure Portal. Learn more about how Azure Copilot works. How to Get Started To start using agents, your global administrator must request access to the agents preview at the tenant level in the Azure Copilot admin center. This confirms your interest in the preview and allows us to enable access. Once approved, users will see the Agent mode toggle in Azure Copilot chat and can then start using Copilot agents. Capacity is limited, so sign up early for the best chance to participate. Additionally, if you are interested in helping shape the future of agentic cloud ops and the role Copilot will play in it, please join our customer feedback program by filling up this form. Agents (preview) in Azure Copilot | Microsoft Learn Troubleshooting sample prompts From an AKS cluster resource, click Kubernetes troubleshooting with Copilot to automatically open Azure Copilot in context of the resource you want to troubleshoot: Try These Prompts to Get Started: Here are a few examples of the kinds of prompts you can use. If you're not already working in the context of a resource, you may need to provide the specific resource that you want to troubleshoot. "My pod keeps restarting can you help me figure out why" "Pods are stuck pending what is blocking them from being scheduled" "I am getting ImagePullBackOff how do I fix this" "One of my nodes is NotReady what is causing it" "My service cannot reach the backend pod what should I check" Note: When using these kinds of prompts, be sure agent mode is enabled by selecting the icon in the chat window: Learn More Troubleshooting agent capabilities in Agents (preview) in Azure Copilot | Microsoft Learn Announcing the CLI Agent for AKS: Agentic AI-powered operations and diagnostics at your fingertips - AKS Engineering Blog Microsoft Copilot in Azure Series - Kubectl | Microsoft Community HubBuilding 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 we’re showcasing what the best frontier organizations are delivering, for their employees, for their customers and for their markets. And we’re 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 aren’t 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 we’re 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. Microsoft’s 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, we’re 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 – 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 “bring your own models” to empower teams to adapt and innovate. It’s these advancements that make GitHub Copilot, the world’s 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 Microsoft’s own developer teams, we’re seeing transformative results with agentic DevOps. GitHub Copilot coding agent is now a top contributor—not only to GitHub’s 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 Microsoft’s engineering ecosystem is deeply integrated into VS Code, Teams, and other tools, giving engineers and product managers real-time, context-aware answers where they work—saving 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 We’re 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 OpenAI’s GPT-5, Anthropic’s Claude, and Microsoft’s 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 Azure’s 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 — 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, we’re 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 we’re focused on helping every organization shape their future with AI. Today’s announcements are a big step forward on this journey. Whether you’re 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. Let’s 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.Agentic Power for AKS: Introducing the Agentic CLI in Public Preview
We are excited to announce the agentic CLI for AKS, available now in public preview directly through the Azure CLI. A huge thank you to all our private preview customers who took the time to try out our beta releases and provide feedback to our team. The agentic CLI is now available for everyone to try--continue reading to learn how you can get started. Why we built the agentic CLI for AKS The way we build software is changing with the democratization of coding agents. We believe the same should happen for how users manage their Kubernetes environments. With this feature, we want to simplify the management and troubleshooting of AKS clusters, while reducing the barrier to entry for startups and developers by bridging the knowledge gap. The agentic CLI for AKS is designed to simplify this experience by bringing agentic capabilities to your cluster operations and observability, translating natural language into actionable guidance and analysis. Whether you need to right-size your infrastructure, troubleshoot complex networking issues like DNS or outbound connectivity, or ensure smooth K8s upgrades, the agentic CLI helps you make informed decisions quickly and confidently. Our goal: streamline cluster operations and empower teams to ask questions like “Why is my pod restarting?” or “How can I optimize my cluster for cost?” and get instant, actionable answers. The agentic CLI for AKS is built on the open-source HolmesGPT project, which has recently been accepted as a CNCF Sandbox project. With a pluggable LLM endpoint structure and open-source backing, the agentic CLI is purpose-built for customizability and data privacy. From private to public preview: what's new? Earlier this year, we launched the agentic CLI in private beta for a small group of AKS customers. Their feedback has shaped what's new in our public preview release, which we are excited to share with the broader AKS community. Let’s dig in: Simplified setup: One-time initialization for LLM parameters with ‘az aks agent-init'. Configure your LLM parameters such as API key and model through a simple, guided user interface. AKS MCP integration: Enable the agent to install and run the AKS MCP server locally (directly in your CLI client) for advanced context-aware operations. The AKS MCP server includes tools for AKS clusters and associated Azure resources. Try it out: az aks agent “list all my unhealthy nodepools” --aks-mcp -n <cluster-name> -g <resource-group> Deeper investigations: New "Task List" feature which helps the agent plan and execute on complex investigations. Checklist-style tracker that allows you to stay updated on the agent's progress and planned tool calls. Provide in-line feedback: Share insights directly from the CLI about the agent's performance using /feedback. Provide a rating of the agent's analysis and optional written feedback directly to the agentic CLI team. Your feedback is highly appreciated and will help us improve the agentic CLI's capabilities. Performance and security improvements: Minor improvements for faster load times and reduced latency, as well as hardened initialization and token handling. Getting Started Install the extension az extension add --name aks-agent Set up you LLM endpoint az aks agent-init Start asking questions Some recommended scenarios to try out: Troubleshoot cluster health: az aks agent "Give me an overview of my cluster's health" Right-size your cluster: az aks agent "How can I optimize my node pool for cost?" Try out the AKS MCP integration: az aks agent "Show me CPU and memory usage trends" --aks-mcp -n <cluster-name> -g <resource-group> Get upgrade guidance: az aks agent "What should I check before upgrading my AKS cluster?" Update the agentic CLI extension az extension update --name aks-agent Join the Conversation We’d love your feedback! Use the built-in '/feedback' command or visit our GitHub repository to share ideas and issues. Learn more: https://aka.ms/aks/agentic-cli Share feedback: https://aka.ms/aks/agentic-cli/issues
