Agentic Applications on Azure Container Apps with Microsoft Foundry
Agents have exploded in popularity over the last year, reshaping not only the kinds of applications developers build but also the underlying architectures required to run them. As agentic applications grow more complex by invoking tools, collaborating with other services, and orchestrating multi-step workflows, architectures are naturally shifting toward microservice patterns. Azure Container Apps is purpose-built for this world: a fully managed, serverless platform designed to run independent, composable services with autoscaling, pay-per-second pricing, and seamless app-to-app communication. By combining Azure Container Apps with the Microsoft Agent Framework (MAF) and Microsoft Foundry, developers can run containerized agents on ACA while using Foundry to visualize and monitor how those agents behave. Azure Container Apps handles scalable, high-performance execution of agent logic, and Microsoft Foundry lights up rich observability for reasoning, planning, tool calls, and errors through its integrated monitoring experience. Together, they form a powerful foundation for building and operating modern, production-grade agentic applications. In this blog, we’ll walk through how to build an agent running on Azure Container Apps using Microsoft Agent Framework and OpenTelemetry, and then connect its telemetry to Microsoft Foundry so you can see your ACA-hosted agent directly in the Foundry monitoring experience. Prerequisites An Azure account with an active subscription. If you don't have one, you can create one for free. Ensure you have a Microsoft Foundry project setup. If you don’t already have one, you can create a project from the Azure AI Foundry portal. Azure Developer CLI (azd) installed Git installed The Sample Agent The complete sample code is available in this repo and can be deployed end-to-end with a single command. It's a basic currency agent. This sample deploys: An Azure Container Apps environment An agent built with Microsoft Agent Framework (MAF) OpenTelemetry instrumentation using the Azure Monitor exporter Application Insights to collect agent telemetry A Microsoft Foundry resource Environment wiring to integrate the agent with Microsoft Foundry Deployment Steps Clone the repository: git clone https://github.com/cachai2/foundry-3p-agents-samples.git cd foundry-3p-agents-samples/azure Authenticate with Azure: azd auth login Set the following azd environment variable azd env set AZURE_AI_MODEL_DEPLOYMENT_NAME gpt-4.1-mini Deploy to Azure azd up This provisions your Azure Container App, Application Insights, logs pipeline and required environment variables. While deployment runs, let’s break down how the code becomes compatible with Microsoft Foundry. 1. Setting up the agent in Azure Container Apps To integrate with Microsoft Foundry, the agent needs two essential capabilities: Microsoft Agent Framework (MAF): Handles agent logic, tools, schema-driven execution, and emits standardized gen_ai.* spans. OpenTelemetry: Sends the required agent/model/tool spans to Application Insights, which Microsoft Foundry consumes for visualization and monitoring. Although this sample uses MAF, the same pattern works with any agent framework. MAF and LangChain currently provide the richest telemetry support out-of-the-box. 1.1 Configure Microsoft Agent Framework (MAF) The agent includes: A tool (get_exchange_rate) An agent created by ChatAgent A runtime manager (AgentRuntime) A FastAPI app exposing /invoke Telemetry is enabled using two components already present in the repo: configure_azure_monitor: Configures OpenTelemetry + Azure Monitor exporter + auto-instrumentation. setup_observability(): Enables MAF’s additional spans (gen_ai.*, tool spans, agent lifecycle spans). From the repo (_configure_observability()): from azure.monitor.opentelemetry import configure_azure_monitor from agent_framework.observability import setup_observability from opentelemetry.sdk.resources import Resource def _configure_observability() -> None: configure_azure_monitor( resource=Resource.create({"service.name": SERVICE_NAME}), connection_string=APPLICATION_INSIGHTS_CONNECTION_STRING, ) setup_observability(enable_sensitive_data=False) This gives you: gen_ai.model.* spans (model usage + token counts) tool call spans agent lifecycle & execution spans HTTP + FastAPI instrumentation Standardized telemetry required by Microsoft Foundry No manual TracerProvider wiring or OTLP exporter setup is needed. 1.2 OpenTelemetry Setup (Azure Monitor Exporter) In this sample, OpenTelemetry is fully configured by Azure Monitor’s helper: import os from azure.monitor.opentelemetry import configure_azure_monitor from opentelemetry.sdk.resources import Resource from agent_framework.observability import setup_observability SERVICE_NAME = os.getenv("ACA_SERVICE_NAME", "aca-currency-exchange-agent") configure_azure_monitor( resource=Resource.create({"service.name": SERVICE_NAME}), connection_string=os.getenv("APPLICATION_INSIGHTS_CONNECTION_STRING"), ) # Enable Microsoft Agent Framework gen_ai/tool spans on top of OTEL setup_observability(enable_sensitive_data=False) This automatically: Installs and configures the OTEL tracer provider Enables batching + exporting of spans Adds HTTP/FastAPI/Requests auto-instrumentation Sends telemetry to Application Insights Adds MAF’s agent + tool spans All required environment variables (such as APPLICATION_INSIGHTS_CONNECTION_STRING) are injected automatically by azd up. 2. Deploy the Model and Test Your Agent Once azd up completes, you're ready to deploy a model to the Microsoft Foundry instance and test it. Find the resource name of your deployed Azure AI Services from azd up and navigate to it. From there, open it in Microsoft Foundry, navigate to the Model Catalog and add the gpt-4.1-mini model. Find the resource name of your deployed Azure Container App and navigate to it. Copy the application URL Set your container app URL environment variable in your terminal. (The below commands are for WSL.) export APP_URL="Your container app URL" Now, go back to your terminal and run the following curl command to invoke the agent curl -X POST "$APP_URL/invoke" \ -H "Content-Type: application/json" \ -d '{ "prompt": "How do I convert 100 USD to EUR?" }' 3. Verifying Telemetry to Application Insights Once your Container App starts, you can validate telemetry: Open the Application Insights resource created by azd up Go to Logs Run these queries (make sure you're in KQL mode not simple mode) Check MAF-genAI spans: dependencies | where timestamp > ago(3h) | extend genOp = tostring(customDimensions["gen_ai.operation.name"]), genSys = tostring(customDimensions["gen_ai.system"]), reqModel = tostring(customDimensions["gen_ai.request.model"]), resModel = tostring(customDimensions["gen_ai.response.model"]) | summarize count() by genOp, genSys, reqModel, resModel | order by count_ desc Check agent + tools: dependencies | where timestamp > ago(1h) | extend genOp = tostring(customDimensions["gen_ai.operation.name"]), agent = tostring(customDimensions["gen_ai.agent.name"]), tool = tostring(customDimensions["gen_ai.tool.name"]) | where genOp in ("agent.run", "invoke_agent", "execute_tool") | project timestamp, genOp, agent, tool, name, target, customDimensions | order by timestamp desc If telemetry is flowing, you’re ready to plug your agent into Microsoft Foundry. 4. Connect Application Insights to Microsoft Foundry Microsoft Foundry uses your Application Insights resource to power: Agent monitoring Tool call traces Reasoning graphs Multi-agent orchestration views Error analysis To connect: Navigate to Monitoring in the left navigation pane of the Microsoft Foundry portal. Select the Application analytics tab. Select your application insights resource created from azd up Connect the resource to your AI Foundry project. Note: If you are unable to add your application insights connection this way, you may need to follow the following: Navigate to the Overview of your Foundry project -> Open in management center -> Connected resources -> New Connection -> Application Insights Foundry will automatically start ingesting: gen_ai.* spans tool spans agent lifecycle spans workflow traces No additional configuration is required. 5. Viewing Dashboards & Traces in Microsoft Foundry Once your Application Insights connection is added, you can view your agent’s telemetry directly in Microsoft Foundry’s Monitoring experience. 5.1 Monitoring The Monitoring tab shows high-level operational metrics for your application, including: Total inference calls Average call duration Overall success/error rate Token usage (when available) Traffic trends over time This view is useful for spotting latency spikes, increased load, or changes in usage patterns, and these visualizations are powered by the telemetry emitting from your agents in Azure Container Apps. 5.2 Traces Timeline The Tracing tab shows the full distributed trace of each agent request, including all spans emitted by Microsoft Foundry and your Azure Container App with Microsoft Agent Framework. You can see: Top-level operations such as invoke_agent, chat, and process_thread_run Tool calls like execute_tool_get_exchange_rate Internal MAF steps (create_thread, create_message, run tool) Azure credential calls (GET /msi/token) Input/output tokens and duration for each span This view gives you an end-to-end breakdown of how your agent executed, which tools it invoked, and how long each step took — essential for debugging and performance tuning. Conclusion By combining Azure Container Apps, the Microsoft Agent Framework, and OpenTelemetry, you can build agents that are not only scalable and production-ready, but also fully observable and orchestratable inside Microsoft Foundry. Container Apps provides the execution engine and autoscaling foundation, MAF supplies structured agent logic and telemetry, and Microsoft Foundry ties everything together with powerful planning, monitoring, and workflow visualization. This architecture gives you the best of both worlds: the flexibility of running your own containerized agents with the dependencies you choose, and the intelligence of Microsoft Foundry to coordinate multi-step reasoning, tool call, and cross-agent workflows. As the agent ecosystem continues to evolve, Azure Container Apps and Microsoft Foundry provide a strong, extensible foundation for building the next generation of intelligent, microservice-driven applications.Announcing 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.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. ACA’s 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 developers’ 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. What’s 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 ACA’s 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 you’re curious about what we’re working on next, check out our roadmap. We look forward to hearing from 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 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!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/issuesMicrosoft Azure at KubeCon North America 2025 | Atlanta, GA - Nov 10-13
KubeCon + CloudNativeCon North America is back - this time in Atlanta, Georgia, and the excitement is real. Whether you’re a developer, operator, architect, or just Kubernetes-curious, Microsoft Azure is showing up with a packed agenda, hands-on demos, and plenty of ways to connect and learn with our team of experts. Read on for all the ways you can connect with our team! Kick off with Azure Day with Kubernetes (Nov 10) Before the main conference even starts, join us for Azure Day with Kubernetes on November 10. It’s a full day of learning, best practices, deep-dive discussions, and hands-on labs, all designed to help you build cloud-native and AI apps with Kubernetes on Azure. You’ll get to meet Microsoft experts, dive into technical sessions, roll up your sleeves in the afternoon labs or have focused deep-dive discussions in our whiteboarding sessions. If you’re looking to sharpen your skills or just want to chat with folks who live and breathe Kubernetes on Azure, this is the place to be. Spots are limited, so register today at: https://aka.ms/AzureKubernetesDay Catch up with our experts at Booth #500 The Microsoft booth is more than just a spot to grab swag (though, yes, there will be swag and stickers!). It’s a central hub for connecting with product teams, setting up meetings, and seeing live demos. Whether you want to learn how to troubleshoot Kubernetes with agentic AI tools, explore open-source projects, or just talk shop, you’ll find plenty of friendly faces ready to help. We will be running a variety of theatre sessions and demos out of the booth week on topics including AKS Automatic, agentic troubleshooting, Azure Verified Modules, networking, app modernization, hybrid deployments, storage, and more. 🔥Hot tip: join us for our live Kubernetes Trivia Show at the Microsoft Azure booth during the KubeCrawl on Tuesday to win exclusive swag! Microsoft sessions at KubeCon NA 2025 Here’s a quick look at all the sessions with Microsoft speakers that you won’t want to miss. Click the titles for full details and add them to your schedule! Keynotes Date: Thu November 13, 2025 Start Time: 9:49 AM Room: Exhibit Hall B2 Title: Scaling Smarter: Simplifying Multicluster AI with KAITO and KubeFleet Speaker: Jorge Palma Abstract: As demand for AI workloads on Kubernetes grows, multicluster inferencing has emerged as a powerful yet complex architectural pattern. While multicluster support offers benefits in terms of geographic redundancy, data sovereignty, and resource optimization, it also introduces significant challenges around orchestration, traffic routing, cost control, and operational overhead. To address these challenges, we’ll introduce two CNCF projects—KAITO and KubeFleet—that work together to simplify and optimize multicluster AI operations. KAITO provides a declarative framework for managing AI inference workflows with built-in support for model versioning, and performance telemetry. KubeFleet complements this by enabling seamless workload distribution across clusters, based on cost, latency, and availability. Together, these tools reduce operational complexity, improve cost efficiency, and ensure consistent performance at scale. Date: Thu November 13, 2025 Start Time: 9:56 AM Room: Exhibit Hall B2 Title: Cloud Native Back to the Future: The Road Ahead Speakers: Jeremy Rickard (Microsoft), Alex Chircop (Akamai) Abstract: The Cloud Native Computing Foundation (CNCF) turns 10 this year, now home to more than 200 projects across the cloud native landscape. As we look ahead, the community faces new demands around security, sustainability, complexity, and emerging workloads like AI inference and agents. As many areas of the ecosystem transition to mature foundational building blocks, we are excited to explore the next evolution of cloud native development. The TOC will highlight how these challenges open opportunities to shape the next generation of applications and ensure the ecosystem continues to thrive. How are new projects addressing these new emerging workloads? How will these new projects impact security hygiene in the ecosystem? How will existing projects adapt to meet new realities? How is the CNCF evolving to support this next generation of computing? Join us as we reflect on the first decade of cloud native—and look ahead to how this community will power the age of AI, intelligent systems, and beyond. Featured Demo Date: Wed November 12, 2025 Start Time: 2:15-2:35 PM Room: Expo Demo Area Title: HolmesGPT: Agentic K8s troubleshooting in your terminal Speakers: Pavneet Singh Ahluwalia (Microsoft), Arik Alon (Robusta) Abstract: Troubleshooting Kubernetes shouldn’t require hopping across dashboards, logs, and docs. With open-source tools like HolmesGPT and the Model Context Protocol (MCP) server, you can now bring an agentic experience directly into your CLI. In this demo, we’ll show how this OSS stack can run everywhere, from lightweight kind clusters on your laptop to production-grade clusters at scale. The experience supports any LLM provider: in-cluster, local, or cloud, ensuring data never leaves your environment and costs remain predictable. We will showcase how users can ask natural-language questions (e.g., “why is my pod Pending?”) and get grounded reasoning, targeted diagnostics, and safe, human-in-the-loop remediation steps -- all without leaving the terminal. Whether you’re experimenting locally or running mission-critical workloads, you’ll walk away knowing how to extend these OSS components to build your own agentic workflows in Kubernetes. All sessions Microsoft Speaker(s) Session Will Case No Kubectl, No Problem: The Future With Conversational Kubernetes Ana Maria Lopez Moreno Smarter Together: Orchestrating Multi-Agent AI Systems With A2A and MCP on Container Neha Aggarwal 10 Years of Cilium: Connecting, Securing, and Simplifying the Cloud Native Stack Yi Zha Strengthening Supply Chain for Kubernetes: Cross-Cloud SLSA Attestation Verification Joaquim Rocha & Oleksandr Dubenko Contribfest: Power up Your CNCF Tools With Headlamp Jeremy Rickard Shaping LTS Together: What We’ve Learned the Hard Way Feynman Zhou Shipping Secure, Reusable, and Composable Infrastructure as Code: GE HealthCare’s Journey With ORAS Jackie Maertens & Nilekh Chaudhari No Joke: Two Security Maintainers Walk Into a Cluster Paul Yu, Sachi Desai Rage Against the Machine: Fighting AI Complexity with Kubernetes simplicity Dipti Pai Flux - The GitLess GitOps Edition Trask Stalnaker OpenTelemetry: Unpacking 2025, Charting 2026 Mike Morris Gateway API: Table Stakes Anish Ramasekar, Mo Khan, Stanislav Láznička, Rita Zhang & Peter Engelbert Strengthening Kubernetes Trust: SIG Auth's Latest Security Enhancements Ernest Wong AI Models Are Huge, but Your GPUs Aren’t: Mastering multi-mode distributed inference on Kubernetes Rita Zhang Navigating the Rapid Evolution of Large Model Inference: Where does Kubernetes fit? Suraj Deshmukh LLMs on Kubernetes: Squeeze 5x GPU Efficiency with cache, route, repeat! Aman Singh Drasi: A New Take on Change-driven Architectures Ganeshkumar Ashokavardhanan & Qinghui Zhuang Agent-Driven MCP for AI Workloads on Kubernetes Steven Jin Contribfest: From Farm (Fork) To Table (Feature): Growing Your First (Free-range Organic) Istio PR Jack Francis SIG Autoscaling Projects Update Mark Rossetti Kubernetes SIG-Windows Updates Apurup Chevuru & Michael Zappa Portable MTLS for Kubernetes: A QUIC-Based Plugin Compatible With Any CNI Ciprian Hacman The Next Decoupling: From Monolithic Cluster, To Control-Plane With Nodes Keith Mattix Istio Project Updates: AI Inference, Ambient Multicluster & Default Deny Jonathan Smith How Comcast Leverages Radius in Their Internal Developer Platform Jon Huhn Lightning Talk: Getting (and Staying) up To Speed on DRA With the DRA Example Driver Rita Zhang, Jaydip Gabani Open Policy Agent (OPA) Intro & Deep Dive Bridget Kromhout SIG Cloud Provider Deep Dive: Expanding Our Mission Pavneet Ahluwalia Beyond ChatOps: Agentic AI in Kubernetes—What Works, What Breaks, and What’s Next Ryan Zhang Finally, a Cluster Inventory I Can USE! Michael Katchinskiy, Yossi Weizman You Deployed What?! Data-driven lesson on Unsafe Helm Chart Defaults Mauricio Vásquez Bernal & Jose Blanquicet Contribfest: Inspektor Gadget Contribfest: Enhancing the Observability and Security of Your K8s Clusters Through an easy to use Framework Wei Fu etcd V3.6 and Beyond + Etcd-operator Updates Jeremy Rickard GitHub Actions: Project Usage and Deep Dive Dor Serero & Michael Katchinskiy What Doesn't Kill You Makes You Stronger: The Vulnerabilities that Redefined Kubernetes Security We can't wait to see you in Atlanta! Microsoft’s presence is all about empowering developers and operators to build, secure, and scale modern applications. You’ll see us leading sessions, sharing open-source contributions, and hosting roundtables on how cloud native powers AI in production. We’re here to learn from you, too - so bring your questions, ideas, and feedback.Azure Container Registry Repository Permissions with Attribute-based Access Control (ABAC)
General Availability announcement Today marks the general availability of Azure Container Registry (ACR) repository permissions with Microsoft Entra attribute-based access control (ABAC). ABAC augments the familiar Azure RBAC model with namespace and repository-level conditions so platform teams can express least-privilege access at the granularity of specific repositories or entire logical namespaces. This capability is designed for modern multi-tenant platform engineering patterns where a central registry serves many business domains. With ABAC, CI/CD systems and runtime consumers like Azure Kubernetes Service (AKS) clusters have least-privilege access to ACR registries. Why this matters Enterprises are converging on a central container registry pattern that hosts artifacts and container images for multiple business units and application domains. In this model: CI/CD pipelines from different parts of the business push container images and artifacts only to approved namespaces and repositories within a central registry. AKS clusters, Azure Container Apps (ACA), Azure Container Instances (ACI), and consumers pull only from authorized repositories within a central registry. With ABAC, these repository and namespace permission boundaries become explicit and enforceable using standard Microsoft Entra identities and role assignments. This aligns with cloud-native zero trust, supply chain hardening, and least-privilege permissions. What ABAC in ACR means ACR registries now support a registry permissions mode called “RBAC Registry + ABAC Repository Permissions.” Configuring a registry to this mode makes it ABAC-enabled. When a registry is configured to be ABAC-enabled, registry administrators can optionally add ABAC conditions during standard Azure RBAC role assignments. This optional ABAC conditions scope the role assignment’s effect to specific repositories or namespace prefixes. ABAC can be enabled on all new and existing ACR registries across all SKUs, either during registry creation or configured on existing registries. ABAC-enabled built-in roles Once a registry is ABAC-enabled (configured to “RBAC Registry + ABAC Repository Permissions), registry admins can use these ABAC-enabled built-in roles to grant repository-scoped permissions: Container Registry Repository Reader: grants image pull and metadata read permissions, including tag resolution and referrer discoverability. Container Registry Repository Writer: grants Repository Reader permissions, as well as image and tag push permissions. Container Registry Repository Contributor: grants Repository Reader and Writer permissions, as well as image and tag delete permissions. Note that these roles do not grant repository list permissions. The separate Container Registry Repository Catalog Lister must be assigned to grant repository list permissions. The Container Registry Repository Catalog Lister role does not support ABAC conditions; assigning it grants permissions to list all repositories in a registry. Important role behavior changes in ABAC mode When a registry is ABAC-enabled by configuring its permissions mode to “RBAC Registry + ABAC Repository Permissions”: Legacy data-plane roles such as AcrPull, AcrPush, AcrDelete are not honored in ABAC-enabled registries. For ABAC-enabled registries, use the ABAC-enabled built-in roles listed above. Broad roles like Owner, Contributor, and Reader previously granted full control plane and data plane permissions, which is typically an overprivileged role assignment. In ABAC-enabled registries, these broad roles will only grant control plane permissions to the registry. They will no longer grant data plane permissions, such as image push, pull, delete or repository list permissions. ACR Tasks, Quick Tasks, Quick Builds, and Quick Runs no longer inherit default data-plane access to source registries; assign the ABAC-enabled roles above to the calling identity as needed. Identities you can assign ACR ABAC uses standard Microsoft Entra role assignments. Assign RBAC roles with optional ABAC conditions to users, groups, service principals, and managed identities, including AKS kubelet and workload identities, ACA and ACI identities, and more. Next steps Start using ABAC repository permissions in ACR to enforce least-privilege artifact push, pull, and delete boundaries across your CI/CD systems and container image workloads. This model is now the recommended approach for multi-tenant platform engineering patterns and central registry deployments. To get started, follow the step-by-step guides in the official ACR ABAC documentation: https://aka.ms/acr/auth/abac
