Introducing the Azure Resource Manager MCP Server!
We're super excited to announce the public preview of the Azure Resource Manager MCP Server! This is a remote MCP server that provides tools to give AI agents first-class access to Azure infrastructure operations through Azure Resource Manager (ARM). AI agents can now be equipped with tools to generate, validate, execute Azure Resource Graph (ARG) queries and tools to deploy and manage ARM template deployments. This server is able to generate and execuite queries that return data across all your Azure resource types! At its core, this server is built to help AI agents interact with Azure resources seamlessly. What this means for you Ask natural language questions about your Azure estate to your agents and get real time, accurate answers backed with an ARG query Deploy and manage infrastructure easily by having AI deploy ARM templates for you Monitor deployment status and catch issues before they escalate Ability to build more advanced AI agents that understand your Azure environment What You Can Do Today Generate, Validate, and Execute Azure Resource Graph Queries from Natural Language No need to struggle with writing KQL from stratch! Describe what you need, and the MCP server tool generates Azure Resource Graph queries that match your intent. You ask an AI Agent: "Find all virtual machines in my subscription that don't have managed disks". It uses the tool and returns: A ready-to-execute ARG query without manual KQL writing. These queries spans across all your azure resource types so can learn and navigate across any type! Deploy, monitor and cancel ARM Templates Pass an ARM template, and the MCP server kicks off the deployment targeted to an existing resource group scope. Monitor the deployment by getting status about it and even cancel it if you decide its not doing what you need it to. Here is the complete list of the tool available in this preview: generate_query validate_query execute_query create_template_deployment get_arm_template_deployment_status cancel_arm_template_deployment Real-World Scenarios Infrastructure Compliance Audit "Show me all resources created in the last 30 days that don't have required tags." - The MCP server generates and executes the query, returning resources that need remediation. Your team can then fix them programmatically or through Copilot. Rapid Infrastructure Provisioning "Using this ARM template <path to template>, deploy a secure storage account with HTTPS-only access, private endpoints, and Standard_LRS replication to my production resource group." This will take an existing ARM template and deploy it to a resource group scope. Policy Compliance Check "Check if all resources in my subscription comply with the latest policy applied to it." - The MCP server generates and executes the query, returning resources that are non-compliant. Your team can then take corrective actions programmatically or through Copilot. Building Agents with Azure Resource Manager MCP Server The MCP server's tools can be integrated into custom agents you build with GitHub Copilot. What this means is you can create custom agents that automatically check compliance, track changes in a scope, or ensure all resources have a particular tag applied to them! Getting Started Prerequisites VS Code installed Valid Azure account with appropriate permissions GitHub Copilot subscription Installation Install the MCP Server Open https://aka.ms/JoinARMMCP VS Code launches automatically Click Install under Azure Resource Manager MCP Server Sign in with your Azure credentials If you hit any authentication issues see Troubleshooting Guide in our repo Check tools are enabled in Chat Open Chat in VS Code (View > Chat) Click Configure Tools Ensure the six Azure Resource Manager MCP Server tools are enabled Start Using It Ask Copilot a question about your Azure resources or infrastructure needs The MCP server handles the rest Governance & Security The Azure Resource Manager MCP Server respects your Azure permissions and governance policies. All operations run in the context of your signed-in user. Additionally you can apply Azure Policies to prevent deployments via the MCP Server. Find more details in the README of our documentation repo. What's Next? We are actively expanding the capabilities of the Azure Resource Manager MCP Server! The Server will expand to include: Additional ARM API capabilities with ARM Enhanced query generation and optimization Support for additional MCP clients beyond VS Code, next up: Claude Get Feedback We want to hear from you. Try the public preview and share your feedback. Found a bug? Or have a feature request? Open an issue on GitHub at https://aka.ms/ARMMCPIssue Resources - đ Full Documentation â Complete setup and usage guide - đ Install Now â Get started with the public preview - đ Report Issues â Share feedback and bugs - â FAQ â Common questions answered - đ ď¸ Troubleshooting â Resolve common issues Try It Today The Azure Resource Manager MCP Server public preview is available now. Visit https://aka.ms/JoinARMMCP to install and start automating your Azure infrastructure with AI. What agents will you build with these tools? We can't wait to see how you'll use this. Steven Bucher PM on Azure Resource Manager and Azure Governance
Announcing Public Preview for Essential Machine Management
Managing servers and VMs across Azure, on premises, and multi-cloud environments often means turning on core capabilitiesâmonitoring, updates, inventory, and configurationâthrough separate setup experiences. Weâve heard feedback that this makes it harder to get visibility into machine state and take actions. Weâre excited to announce the public preview of Essential Machine Management experience within Compute Infrastructure Hubâa new entry point in Azure that streamlines onboarding for machines at scale and enables basic management capabilities. Start once at subscription scope, get a clear view of whatâs turned on, and move from setup to operations faster across your Azure and cloud and hybrid estate. What is Essential Machine Management? Essential Machine Management is a centralized onboarding experience that helps customers enroll their machines into a set of selected cloud-native management services from Azure in a simple, scalable way, Instead of enabling monitoring, updates, inventory, and configuration independently per machine, Essential Machine Management allows you to enroll entire subscriptions at once, including both Azure Virtual Machines and Azure Arcâenabled servers. These services are pre-configured with best practices, enabling customers with out-of-the-box value right away. Once enrolled, current and future machines in the selected subscriptions are automatically onboarded to the enabled management services, helping ensure consistent visibility and operational coverage from day one. What management capabilities are enabled? Using Essential Machine Management, you can quickly onboard machines to multiple Azure management capabilities, including: Monitoring insights and recommended alerts for machine health and performance Azure Update Manager to help keep machines secure and compliant Change tracking and inventory for visibility and auditability Machine configuration for managing in-machine configuration, compliance and security Azure Security baseline policy is a set of tailored rules to assess your machine's security posture These services help keep your infrastructure secure and healthy. How much does it cost? Azure VMs: For Azure Virtual Machines only, capabilities enabled by Essential Machine Management are provided at no additional charge. Azure Arc-enabled servers: For Azure Arc-enabled servers with Windows Server Software Assurance, Windows Server PayGo, and Windows Server Extended Security Updates, capabilities enabled by Essential Machine Management are provided at no additional charge. For all other Arc-enabled servers, Essential Machine Management will be priced at $9 per server per month once billing is enabled. See more details here. Getting started If you manage Azure VMs or Arc-enabled servers and are looking to simplify how you onboard and manage machines at scale, Essential Machine Management feature is now available for you to try in public preview. Check out the preview in the Azure Portal under Compute infrastructure --> Monitoring + Operations --> Essential Machine Management (preview): Check out Essential Machine Management now and reach out to machineenrollmentsupport@microsoft.com for any feedback or support. Learn more about Essential Machine Management here.
Azure Update Manager to support CIS hardened images among other images
Whatâs coming in by first week of August: Azure Update Manager will add support for 35 CIS hardened images. This is the first time that Update Management product in Azure is supporting CIS hardened images. Apart from CIS hardened images, Azure Update Manager will also add support for 59 other images to unblock Automation Update Management migrations to Azure Update Manager. Whatâs coming in September: After this release, another batch of 30 images will be added support for. Please refer to the article below to check the details of which images will be supported. Below 35 CIS images will be supported by Azure Update Manager by first week of August. Please note Publisher for all these images is center-for-internet-security-inc. Offer Plan cis-windows-server cis-windows-server2016-l1-gen1 cis-windows-server2019-l1-gen1 cis-windows-server2019-l1-gen2 cis-windows-server2019-l2-gen1 cis-windows-server2022-l1-gen2 cis-windows-server2022-l2-gen2 cis-windows-server2022-l1-gen1 cis-windows-server-2022-l1 cis-windows-server-2022-l1 cis-windows-server-2022-l1-gen2 cis-windows-server-2022-l2 cis-windows-server-2022-l2 cis-windows-server-2022-l2-gen2 cis-windows-server-2019-v1-0-0-l1 cis-ws2019-l1 cis-windows-server-2019-v1-0-0-l2 cis-ws2019-l2 cis-windows-server-2016-v1-0-0-l1 cis--l1 cis-windows-server-2016-v1-0-0-l2 cis-ws2016-l2 cis-windows-server-2012-r2-v2-2-1-l2 cis-ws2012-r2-l2 cis-rhel9-l1 cis-rhel9-l1 cis-rhel9-l1-gen2 cis-rhel-8-l1 cis-rhel-8-l2 cis-rhel8-l2 cis-rhel-7-l2 cis-rhel7-l2 cis-rhel cis-redhat7-l1-gen1 cis-redhat8-l1-gen1 cis-redhat8-l2-gen1 cis-redhat9-l1-gen1 cis-redhat9-l1-gen2 cis-ubuntu-linux-2204-l1 cis-ubuntu-linux-2204-l1 cis-ubuntu-linux-2204-l1-gen2 cis-ubuntu-linux-2004-l1 cis-ubuntu2004-l1 cis-ubuntu-linux-1804-l1 cis-ubuntu1804-l1 cis-ubuntu cis-ubuntu1804-l1 cis-ubuntulinux2004-l1-gen1 cis-ubuntulinux2204-l1-gen1 cis-ubuntulinux2204-l1-gen2 cis-oracle-linux-8-l1 cis-oracle8-l1 Apart from CIS hardened images, below are the other 59 images which will be supported by Azure Update Manager by first week of August: Publisher Offer Plan almalinux almalinux-x86_64 8_7-gen2 belindaczsro1588885355210 belvmsrv01 belvmsrv003 cloudera cloudera-centos-os 7_5 cloud-infrastructure-services rds-farm-2019 rds-farm-2019 cloud-infrastructure-services ad-dc-2019 ad-dc-2019 cloud-infrastructure-services sftp-2016 sftp-2016 cloud-infrastructure-services ad-dc-2016 ad-dc-2016 cloud-infrastructure-services hpc2019-windows-server-2019 hpc2019-windows-server-2019 cloud-infrastructure-services dns-ubuntu-2004 dns-ubuntu-2004 cloud-infrastructure-services servercore-2019 servercore-2019 cloud-infrastructure-services ad-dc-2022 ad-dc-2022 cloud-infrastructure-services squid-ubuntu-2004 squid-ubuntu-2004 cognosys sql-server-2016-sp2-std-win2016-debug-utilities sql-server-2016-sp2-std-win2016-debug-utilities esri arcgis-enterprise byol-108 byol-109 byol-111 byol-1081 byol-1091 esri arcgis-enterprise-106 byol-1061 esri arcgis-enterprise-107 byol-1071 esri pro-byol pro-byol-29 filemagellc filemage-gateway-vm-win filemage-gateway-vm-win-001 filemage-gateway-vm-win-002 github github-enterprise github-enterprise matillion matillion matillion-etl-for-snowflake microsoft-ads windows-data-science-vm windows2016 windows2016byol microsoft-dsvm ubuntu-1804 1804-gen2 netapp netapp-oncommand-cloud-manager occm-byol nginxinc nginx-plus-ent-v1 nginx-plus-ent-centos7 ntegralinc1586961136942 ntg_oracle_8_7 ntg_oracle_8_7 procomputers almalinux-8-7 almalinux-8-7 procomputers rhel-8-2 rhel-8-2 RedHat rhel 8_9 redhat rhel-byos rhel-lvm79 rhel-lvm79-gen2 rhel-lvm8 rhel-lvm82-gen2 rhel-lvm83 rhel-lvm84 rhel-lvm84-gen2 rhel-lvm85-gen2 rhel-lvm86 rhel-lvm86-gen2 rhel-lvm87-gen2 rhel-raw76 redhat rhel 8.1 redhat rhel-sap 7.4 redhat rhel-sap 7.7 redhat rhel 89-gen2 southrivertech1586314123192 tn-ent-payg Tnentpayg southrivertech1586314123192 tn-sftp-payg Tnsftppayg suse sles-sap-15-sp2-byos gen2 suse sles-15-sp5 gen2 talend talend_re_image tlnd_re thorntechnologiesllc sftpgateway Sftpgateway veeam office365backup veeamoffice365backup veeam veeam-backup-replication veeam-backup-replication-v11 zscaler zscaler-private-access zpa-con-azure Below images will be supported in September: Publisher Offer Plan aod win2019azpolicy win2019azpolicy belindaczsro1588885355210 belvmsrv03 belvmsrv001 center-for-internet-security-inc cis-rhel-7-v2-2-0-l1 cis-rhel7-l1 center-for-internet-security-inc cis-rhel-7-stig cis-rhel-7-stig center-for-internet-security-inc cis-win-2016-stig cis-win-2016-stig center-for-internet-security-inc cis-windows-server-2012-r2-v2-2-1-l1 cis-ws2012-r2-l1 cloudrichness rockey_linux_image rockylinux86 Credativ Debian 8 microsoftdynamicsnav dynamicsnav 2017 microsoftwindowsserver windowsserver-hub 2012-r2-datacenter-hub 2016-datacenter-hub MicrosoftWindowsServer WindowsServer-HUB 2016-Datacenter-HUB ntegralinc1586961136942 ntg_cbl_mariner_2 ntg_cbl_mariner_2_gen2 openvpn openvpnas access_server_byol rapid7 nexpose-scan-engine nexpose-scan-engine rapid7 rapid7-vm-console rapid7-vm-console suse sles 12-sp3 suse sles-15-sp1-basic gen1 suse sles-15-sp2-basic gen1 suse sles-15-sp3-basic gen1 gen2 suse sles-15-sp4-basic gen2 suse sles-sap 12-sp3 15 gen2-15 suse sles-sap-byos 15 suse SLES-SAP-BYOS 15 suse sles-sap-15-sp1-byos gen1 Tenable tenablecorenessus tenablecorenessusbyolAnnouncing General Availability for Azure Resource Graph (ARG) GET/LIST API
ARG GET/LIST API delivers 10X higher throttling quotas to callers compared to ARG query unlocking a more scalable, resilient way to perform resource lookups in Azure. ARG GET/LIST API is a new platform capability within Azure Resource Graph that provides a high-performance experience for both Point GET and collection GET requests. A key advantage of this capability is its ability to significantly reduce READ throttling for high volume calls efficiently. This is made possible through intelligent control plane routing based on a query parameter controlled by the caller. When a specific query parameter is included, requests are automatically directed to this optimized ARG GET/LIST backend. When the parameter is omitted, requests flow to the Resource provider âensuring flexibility and backward compatibility. What Challenge Are We Addressing? Azure Read Throttling is a significant challenge for many customers. When services hit throttling limits, applications may experience performance degradation, elevated latency, or even failed requestsâissues that can disrupt critical workloads and customer operations. The ARG GET/LIST API is designed to directly address this problem. By routing GET and LIST calls through Azure Resource Graphâs scalable indexing infrastructure and intelligent control-plane routing, it dramatically reduces the likelihood of read throttling. Best of all, it follows the ARM control plane GET APIs request response contract, allowing you to benefit from improved performance and reliability with minimal effort, appending the flag âuseResourceGraph=trueâ. When to use Azure Resource Graph (ARG) GET/LIST API The ARG GET/LIST API is designed for scenarios where you need to retrieve a single resource by its ID or list resources of the same type within a defined scopeâwhether that's a subscription, resource group, or parent resource. You should consider using the ARG GET/LIST API if your service fits into one or more of the following categories: High Volume of GET Calls Within a Single Scope: Your service issues a large number of GET requests targeting resources within a single subscription or resource group, without the need for cross-subscription queries, complex filters, or joins. Risk of Throttling or Quota Competition: Your service produces a high volume of requests and may encounter issues such as:: Experience throttling during sudden traffic spikes. Quota competition, where other workloads in the same subscription consume shared quota limits, causing your service to be throttled. Bursty traffic patterns, where large volume of GET requests are issued within a short time window, increasing the chance of throttling. Need for High Availability and Faster Performance: Your service depends on consistent; low-latency GET operations for either single-resource lookups or listing resources within a specific scope Note: The ARG GET/LIST API is currently supported only for resources in the resources and computeresources tables. Using the ARG GET/LIST API To get started with the ARG GET/LIST API, begin by assessing whether your scenario aligns with the recommended calling patterns and throttling considerations described earlier. Once confirmed, simply append the parameter &useResourceGraph=true to your eligible GET/LIST API calls. This flag routes your request through the Azure Resource Graph GET/LIST API backend, allowing you to take advantage of its optimized performance and query efficiency. No calls will route to ARG GET/LIST backend automatically. The switch is entirely in the userâs controlâthe call will route to ARG GET/LIST API only when you explicitly include the useResourceGraph=true parameter in your request. Follow the ARG GET/LIST API contract here - Azure Resource Graph GET/LIST API Guidance - Azure Resource Graph | Microsoft Learn Letâs walk through a simple example of retrieving a Virtual Machine (VM) along with its InstanceView through ARG Query vs. ARM API vs. ARG GET/LIST API to show the difference in the calling experience. Using an ARG Query (via ARG Explorer) In ARG Explorer, you can use Kusto Query Language (KQL) to query resources. A sample query to retrieve a specific VM looks like this: Resources | where type =~ 'microsoft.compute/virtualmachines' | where id =~ '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/microsoft.compute/virtualmachines/{vm}' This query filters the Resource Graph index to return the VM resource. Using the ARM (Compute RP) API The equivalent ARM API call to retrieve the VM with InstanceView is: GET https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/microsoft.compute/virtualmachines/{vm}?api-version=2024-07-01&$expand=instanceView This hits the Compute Resource Provider, pulls the VM state, and expands the instanceView section. Using the ARG GET/LIST API ARG GET/LIST APIs that follow the same request structure as ARMâbut with an additional flag that routes the call through ARG: GET https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/microsoft.compute/virtualmachines/{vm}?api-version=2024-07-01&$expand=instanceView&useResourceGraph=true The important distinction here is the useResourceGraph=true parameter, which routes the call through ARM to serve the response through ARGâs GET/LIST backend. Sample Response - You can find more examples in our documentation - Azure Resource Graph GET/LIST API Guidance - Azure Resource Graph | Microsoft Learn Video Walkthrough Increase Throttling Quota via Azure Resource Graph Learn More Azure Resource Graph GET/LIST API Overview Known Limitations Frequently Asked Questions Share Your Feedback For questions and feedback, you can reach us at Azure Resource Graph team⯠Share Product feedback and ideas with us atâŻAzure Governance ¡ Community Happy Querying!
Optimize Your Cloud Environment Using Agentic AI
In todayâs cloud-first world, optimization is no longer a luxuryâitâs a strategic imperative. As IT professionals and developers navigate increasingly complex environments, the need to reduce costs, improve sustainability, and accelerate decision-making has never been more urgent. At Ignite 2025, Microsoft is introducing a new wave of agentic capabilities within Azure Copilotâone of the key capabilities includes the optimization agent, designed to help you identify, validate, and act on opportunities to streamline cloud operations. For FinOps teams, this agent becomes especially powerful, enabling cost governance, carbon insights, and actionable recommendations to maximize financial efficiency at scale. From Complexity to Clarity For users familiar with Azureâs cost and performance tools, the new operations center experience in the Azure Portal provides a unified agentic experience to monitor spend and carbon emissions side by side, surface the most critical optimization opportunities, and seamlessly trigger actions by invoking the Optimization agentâbringing governance, efficiency, and sustainability into one streamlined experience. Whatâs New in Optimization The optimization agent in Azure Copilot empowers teams to: Identify top actions prioritized by impact, cost savings, and ease of implementation. Evaluate cost and carbon impacts side-by-side, helping you make informed decisions that align with financial and sustainability goals. Validate recommendations with supporting evidence, current / projected utilization trends, and alternative SKU choices. Accelerate implementation with step-by-step guidance and agentic workflows that reduce toil and increase confidence. These capabilities are designed to scale FinOps impact, enabling collaboration across engineering, finance, procurement, and sustainability teamsâall within a unified experience. A Day in the Life: FinOps in Action Letâs step into the shoes of a FinOps practitioner at a large enterprise navigating the complexities of cost management. Itâs Monday morning. Over the weekend, a set of development VMs were left running, quietly accumulating costs. The optimization agentâa capability within Azure Copilotâsurfaces a top action: resize or shut down the idle resources. With a few clicks, the practitioner reviews the supporting evidence, including usage trends, cost impact, and carbon footprint. The agent offers visibility over alternative SKUs and guides the practitioner through a step-by-step implementationâall within the same interface. But it doesnât stop there. For teams that prefer automation or scripting, the agent also generates Azure CLI and PowerShell scripts tailored to the recommended action. This gives practitioners flexibility: they can execute changes directly in the portal or integrate scripts into their existing workflows for repeatability and scale. The experience is seamlessâevery recommendation is actionable, verifiable, and aligned with enterprise policy. By midweek, the practitioner has implemented multiple optimizations without leaving the console or writing custom code. Each action is logged for audit visibility, ensuring compliance and transparency across the organization. What used to take hours of manual investigation and coordination now happens in minutes, freeing the team to focus on strategic initiatives rather than firefighting cost overruns. Why It Matters These arenât just featuresâtheyâre answers to the pain points customers have been voicing for years. Cost visibility and predictability: Azure Copilot centralizes insights across subscriptions, helping teams avoid surprise bills and understand where every dollar goes. Resource inefficiencies: The optimization agent proactively identifies underutilized resources and guide teams to act before costs escalate. Scalability and complexity: Azure Copilotâs unified experience simplifies operations for even the most complex setups. Azure Copilot isnât just simplifying cloud operationsâitâs transforming how teams collaborate, govern, and optimize. Get Started at Ignite At Ignite 2025, youâll get hands-on with Azure Copilotâs optimization capabilities. Explore how intelligent assistance can help you: Reduce cloud costs Improve sustainability metrics Strengthen governance and compliance Drive better outcomesâfaster Azure Copilot: turning cloud operations into intelligent collaboration. Sign up for the Agents in Azure Copilot Limited (Preview) and try the experience today.[Public Preview] Introducing Customizable Security Baseline Policies in Machine Configuration
Background: Azure Machine Configuration remains committed to enabling greater security and simplicity in at-scale server management for all Azure customers. Machine Configuration (previously known as Azure Policy Guest Configuration) enables both built-in and custom configuration as code allowing you to audit and configure OS, app, and workload level settings at scale, both for machines running in Azure and hybrid Azure Arc-enabled servers. Weâre excited to announce Public Preview support for Customizable Security Baselines in Azure Policy and Machine Configuration. This feature empowers you to tailor industry security benchmarksâsuch as CIS benchmarks for Linux or Azure Security Baselines for Windows and Linux âto align with your organizationâs unique compliance standards across both Azure and Arc-connected machines. This feature builds on top of our existing audit baseline capabilities for Windows and Linux. Now you can create, parameterize, and assign custom baselines at scale, enabling continuous compliance visibility across your entire environment. Learn more about how to get started here: Customize Security Baselines with Azure Policy and Machine Configuration. What's New? Customizable security baselines in Azure Policy and Machine Configuration bring a powerful new way to assess, monitor, and improve your security posture across both Windows and Linux servers. Built on industry benchmarks such as the Center for Internet Security (CIS) and Microsoftâs own Azure Compute Security Baselines, this capability enables you to adapt compliance frameworks to your organizationâs specific needs â all while maintaining a consistent governance model across Azure and hybrid environments. By passing custom baseline parameters directly into Azure Policy, you can represent internal controls at scale, ensuring that compliance reflects your enterpriseâs unique standards and regulatory requirements. This cloud-native approach embodies Microsoftâs Secure by Design and Secure by Default principles â ensuring your workloads stay compliant, wherever they run. Key Scenarios Baseline Customization Tailor your security standards through the Modify Settings wizard under Policy > Machine Configuration. You can: Enable, exclude, or adjust rules from existing benchmarks Apply organization-specific parameters Export your custom configuration as a downloadable JSON file Each baseline JSON file serves as a reusable, declarative artifactâideal for policy-as-code workflows, version control, and CI/CD integration. Assign Audit Policies When you assign a baseline via Azure Policy, it automatically: Evaluates configurations against your defined standards Reports compliance in near real time Surfaces findings in Azure Policy, Azure Resource Graph, and the Guest Assignments view This integrated visibility helps IT administrators, security teams, and auditors track compliance status with minimal overhead. Integration and Automation Security baselines integrate seamlessly into your DevOps pipelines and configuration management workflows. Each baseline produces a declarative settings catalog (JSON) that can be versioned and deployed using: Azure CLI ARM templates Bicep CI/CD automation This ensures reproducible, traceable compliance configurations across environments. Supported Standards Standard Description CIS Linux Benchmarks Official CIS Benchmarks for Azure-endorsed Linux distributions, matching the latest CIS versions. Azure Compute Security Baseline for Windows Applies security controls for Windows Server 2022 and 2025, aligned with Azure Compute guidance. Azure Compute Security Baseline for Linux Enforces consistent controls aligned with Azure Compute recommendations. Availability Customizable security baselines are available in all public Azure regions. NOTE: Support for Azure Government and Sovereign Clouds will be added in a future release. These environments are not included in the current Public Preview. Getting Started Prerequisites Before you begin: Deploy the Azure Machine Configuration prerequisite policy initiative. (This installs the required Guest Configuration extension on supported VMs.) Ensure your Azure subscription or management group includes supported Windows or Linux VMs. Have sufficient permissions (Owner or Resource Policy Contributor) to create and assign custom policy definitions. Step-by-Step Guidance Select a baseline from the Machine Configuration tab in Azure Policy. Modify settings to enable, exclude, or parameterize rules to match your internal policies. Download JSON to export your customized baseline configuration file for programmatic and repeatable customization. Assign the policy which can be deployed through the Azure portal, CLI, or your CI/CD pipeline. Review compliance results to track outcomes in Azure Policy, Azure Resource Graph, or the Guest Assignments page. Learn More Azure Machine Configuration security baselines official documentation CIS Benchmark for Linux documentation Azure Windows Baseline and Azure Linux Baseline documentation Please note that the use of Azure Machine Configuration on Azure Arc-enabled servers will incur a charge.Empower Smarter AI Agent Investments
This curated series of modules is designed to equip technical and business decision-makers, including IT, developers, engineers, AI engineers, administrators, solution architects, business analysts, and technology managers, with the practical knowledge and guidance needed to make cost-conscious decisions at every stage of the AI agent journey. From identifying high-impact use cases and understanding cost drivers, to forecating ROI, adopting best practices, designing scalable and effective architectures, and optimizing ongoing investments, this learning path provides actionable guidance for building, deploying, and managing AI agents on Azure with confidence. Whether youâre just starting your AI journey or looking to scale enterprise adoption, these modules will help you align innovation with financial discipline, ensuring your AI agent initiatives deliver sustainable value and long-term success. Discover the full learning path here: aka.ms/Cost-Efficient-AI-Agents Explore the sections below for an overview of each module included in this learning path, highlighting the core concepts, practical strategies, and actionable insights designed to help you maximize the value of AI agent investments on Azure: Module 1: Identify and Prioritize High-Impact, Cost-Effective AI Agent Use Cases The journey begins with a strategic approach to selecting AI agent use cases that maximize business impact and cost efficiency. This module introduces a structured framework for researching proven use cases, collaborating across teams, and defining KPIs to evaluate feasibility and ROI. Youâll learn how to target âquick winsâ while ensuring alignment with organizational goals and resource constraints. Explore this module Module 2: Understand the Key Cost Drivers of AI Agents Building on the foundation of use case selection, Module 2 dives into the core cost drivers of AI agent development and operations on Azure. It covers infrastructure, integration, data quality, team expertise, and ongoing operational expenses, offering actionable strategies to optimize spending at every stage. The module emphasizes right-sizing resources, efficient data preparation, and leveraging Microsoft tools to streamline development and ensure sustainable, scalable success. Explore this module Module 3: Forecast the Return on Investment (ROI) of AI agents With a clear understanding of costs, the next step is to quantify value. Module 3 empowers both business and technical leaders with practical frameworks for forecasting and communicating ROI, even without a finance background. Through step-by-step guides and real-world examples, youâll learn to measure tangible and intangible outcomes, apply NPV calculations, and use sensitivity analysis to prioritize AI investments that align with broader organizational objectives. Explore this module Module 4: Implement Best Practices to Empower AI Agent Efficiency and Ensure Long-Term Success To drive efficiency and governance at scale, Module 4 introduces essential frameworks such as the AI Center of Excellence (CoE), FinOps, GenAI Ops, the Cloud Adoption Framework (CAF), and the Well-Architected Framework (WAF). These best practices help organizations accelerate adoption, optimize resources, and foster operational excellence, ensuring AI agents deliver measurable value, remain secure, and support sustainable enterprise growth. Explore this module Module 5: Maximize Cost Efficiency by Choosing the Right AI Agent Development Approach Selecting the right development approach is critical for balancing speed, customization, and cost. In Module 5, youâll learn how to align business needs and technical skills with SaaS, PaaS, or IaaS options, empowering both business users and developers to efficiently build, deploy, and manage AI agents. The module also highlights how Microsoft Copilot Studio, Visual Studio, and Azure AI Foundry can help your organization achieve its goals. Explore this module Module 6: Architect Scalable and Cost-Efficient AI Agent Solutions on Azure As your AI initiatives grow, architectural choices become paramount. Module 6 explores how to leverage Azure Landing Zones and reference architectures for secure, well-governed, and cost-optimized deployments. It compares single-agent and multi-agent systems, highlights strategies for cost-aware model selection, and details best practices for governance, tagging, and pricing, ensuring your AI solutions remain flexible, resilient, and financially sustainable. Explore this module Module 7: Manage and Optimize AI Agent Investments on Azure The learn path concludes with a focus on operational excellence. Module 7 provides guidance on monitoring agent performance and spending using Azure AI Foundry Observability, Azure Monitor Application Insights, and Microsoft Cost Management. Learn how to track key metrics, set budgets, receive real-time alerts, and optimize resource allocation, empowering your organization to maximize ROI, stay within budget, and deliver ongoing business value. Explore this module Ready to accelerate your AI agent journey with financial confidence? Start exploring the new learning path and unlock proven strategies to maximize the cost efficiency of your AI agents on Azure, transforming innovation into measurable, sustainable business success. Get started todayCloud and AI Cost Efficiency: A Strategic Imperative for Long-Term Business Growth
In this blog, weâll explore why cost efficiency is a top priority for organizations today, how Azure Essentials can help address this challenge, and provide an overview of Microsoftâs solutions, tools, programs, and resources designed to help organizations maximize the value of their cloud and AI investments.
