Selecting the Right Agentic Solution on Azure
Recently, we have seen a surge in requests from customers and Microsoft partners seeking guidance on building and deploying agentic solutions at various scales. With the rise of Generative AI, replacing traditional APIs with agents has become increasingly popular. There are several approaches to building, deploying, running, and orchestrating agents on Azure. In this discussion, I will focus exclusively on Azure-specific tools, services, and methodologies, setting aside Copilot and Copilot Studio for now. This article describes the options available as of today. 1. Azure OpenAI Assistants API: This feature within Azure OpenAI Service enables developers to create conversational agents (“assistants”) based on OpenAI models (such as GPT-3.5 and GPT-4). It supports capabilities like memory, tool/function calls, and retrieval (e.g., document search). However, Microsoft has already deprecated version 1 of the Azure OpenAI Assistants API, and version 2 remains in preview. Microsoft strongly recommends migrating all existing Assistants API-based agents to the Agent Service. Additionally, OpenAI is retiring the Assistants API and advises developers to use the modern “Response” API instead (see migration detail). Given these developments, it is not advisable to use the Assistants API for building agents. Instead, you should use the Azure AI Agent Service, which is part of Azure AI Foundry. 2. Workflows with AI agents and models in Azure Logic Apps (Preview) – As the name suggests, this feature is currently in public preview and is only available with Logic Apps Standard, not with the consumption plan. You can enhance your workflow by integrating agentic capabilities. For example, in a visa processing workflow, decisions can be made based on priority, application type, nationality, and background checks using a knowledge base. The workflow can then route cases to the appropriate queue and prepare messages accordingly. Workflows can be implemented either as chat assistant or APIs. If your project is workflow-dependent and you are ready to implement agents in a declarative way, this is a great option. However, there are currently limited choices for models and regional availability. For CI/CD, there is an Azure Logic Apps Standard template available for VS Code you can use. 3. Azure AI Agent Service – Part of Azure AI Foundry, the Azure AI Agent Service allows you to provision agents declaratively from the UI. You can consume various OpenAI models (with support for non-OpenAI models coming soon) and leverage important tools or knowledge bases such as files, Azure AI Search, SharePoint, and Fabric. You can connect agents together and create hierarchical agent dependencies. SDKs are available for building agents within agent services using Python, C#, or Java. Microsoft manages the infrastructure to host and run these agents in isolated containers. The service offers role-based access control, MS Entra ID integration, and options to bring your own storage for agent states and Azure Key Vault keys. You can also incorporate different actions including invoking a Logic App instance from your agent. There is also option to trigger an agent using Logic Apps (preview). Microsoft recommends using Agent Service/Azure Foundry as the destination for agents, as further enhancements and investments are focused here. 4. Agent Orchestrators – There are several excellent orchestrators available, such as LlamaIndex, LangGraph, LangChain, and two from Microsoft—Semantic Kernel and AutoGen. These options are ideal if you need full control over agent creation, hosting, and orchestration. They are developer-only solutions and do not offer a UI (barring AutoGen Studio having some UI assistance). You can create complex, multi-layered agent connections. You can then host and run these agents in you choice of Azure services like AKS or Apps Service. Additionally, you have the option to create agents using Agent Service and then orchestrate them with one of these orchestrators. Choosing the Right Solution The choice of agentic solution depends on several factors, including whether you prefer code or no-code approaches, control over the hosting platform, customer needs, scalability, maintenance, orchestration complexity, security, and cost. Customer Need: If agents need to be part of a workflow, use AI Agents in Logic Apps; otherwise, consider other options. No-Code: For workflow-based agents, Logic Apps is suitable; for other scenarios, Azure AI Agent Service is recommended. Hosting and Maintenance: If Logic Apps is not an option and you prefer not to maintain your own environment, use Azure AI Agent Service. Otherwise, consider custom agent orchestrators like Semantic Kernel or AutoGen to build the agent and services like AKS or Apps Service to host those. Orchestration Complexity: For simple hierarchical agent connections, Azure AI Agent Service is good choice. For complex orchestration, use an agent orchestrator. Versioning - If you are concerned about versioning to ensure solid CI/CD regime then you may have to chose Agent Orchestrators. Agent Service still miss this feature clarity. We have some work-around but it is not robust implementation. Hopefully we will catch up soon with a better versioning solution. Summary: When selecting the right agentic solution on Azure, consider the latest recommendations and platform developments. For most scenarios, Microsoft advises using the Azure AI Agent Service within Azure Foundry, as it is the focus of ongoing enhancements and support. For workflow-driven projects, Azure Logic Apps with agentic capabilities may be suitable, while advanced users can leverage orchestrators for custom agent architecturesAzure Course Blueprints
Overview The Course Blueprint is a comprehensive visual guide to the Azure ecosystem, integrating all the resources, tools, structures, and connections covered in the course into one inclusive diagram. It enables students to map out and understand the elements they've studied, providing a clear picture of their place within the larger Azure ecosystem. It serves as a 1:1 representation of all the topics officially covered in the instructor-led training. Formats available include PDF, Visio, Excel, and Video. Links: Each icon in the blueprint has a hyperlink to the pertinent document in the learning path on Learn. Layers: You have the capability to filter layers to concentrate on segments of the course Integration: The Visio Template+ for expert courses like SC-100 and AZ-305 includes an additional layer that enables you to compare SC-100, AZ-500, and SC-300 within the same diagram. Similarly, you can compare any combination of AZ-305, AZ-700, AZ-204, and AZ-104 to identify differences and study gaps. Since SC-300 and AZ-500 are potential prerequisites for the expert certification associated with SC-100, and AZ-204 or AZ-104 for the expert certification associated with AZ-305, this comparison is particularly useful for understanding the extra knowledge or skills required to advance to the next level. Advantages for Students Defined Goals: The blueprint presents learners with a clear vision of what they are expected to master and achieve by the course’s end. Focused Learning: By spotlighting the course content and learning targets, it steers learners’ efforts towards essential areas, leading to more productive learning. Progress Tracking: The blueprint allows learners to track their advancement and assess their command of the course material. Topic List: A comprehensive list of topics for each slide deck is now available in a downloadable .xlsx file. Each entry includes a link to Learn and its dependencies. Download links Associate Level PDF Visio Contents Video Overview AZ-104 Azure Administrator Associate R: 12/14/2023 U: 04/16/2025 Blueprint Visio Excel Mod 01 AZ-204 Azure Developer Associate R: 11/05/2024 U: 11/11/2024 Blueprint Visio Excel AZ-500 Azure Security Engineer Associate R: 01/09/2024 U: 10/10/2024 Blueprint Visio+ Excel AZ-700 Azure Network Engineer Associate R: 01/25/2024 U: 11/04/2024 Blueprint Visio Excel SC-200 Security Operations Analyst Associate R: 04/03/2025 U:04/09/2025 Blueprint Visio Excel SC-300 Identity and Access Administrator Associate R: 10/10/2024 Blueprint Excel Specialty PDF Visio AZ-140 Azure Virtual Desktop Specialty R: 01/03/2024 U: 02/27/2025 Blueprint Visio Excel Expert level PDF Visio AZ-305 Designing Microsoft Azure Infrastructure Solutions R: 05/07/2024 U: 02/05/2025 Blueprint Visio+ AZ-104 AZ-204 AZ-700 AZ-140 Excel SC-100 Microsoft Cybersecurity Architect R: 10/10/2024 U: 04/09/2025 Blueprint Visio+ AZ-500 SC-300 SC-200 Excel Skill based Credentialing PDF AZ-1002 Configure secure access to your workloads using Azure virtual networking R: 05/27/2024 Blueprint Visio Excel AZ-1003 Secure storage for Azure Files and Azure Blob Storage R: 02/07/2024 U: 02/05/2024 Blueprint Excel Subscribe if you want to get notified of any update like new releases or updates. Author: Ilan Nyska, Microsoft Technical Trainer My email ilan.nyska@microsoft.com LinkedIn https://www.linkedin.com/in/ilan-nyska/ I’ve received so many kind messages, thank-you notes, and reshares — and I’m truly grateful. But here’s the reality: 💬 The only thing I can use internally to justify continuing this project is your engagement — through this survey https://lnkd.in/gnZ8v4i8 ⏳ Unless I receive enough support via this short survey, the project will be sunset. Thank you for your support! ___ Benefits for Trainers: Trainers can follow this plan to design a tailored diagram for their course, filled with notes. They can construct this comprehensive diagram during class on a whiteboard and continuously add to it in each session. This evolving visual aid can be shared with students to enhance their grasp of the subject matter. Explore Azure Course Blueprints! | Microsoft Community Hub Visio stencils Azure icons - Azure Architecture Center | Microsoft Learn ___ Are you curious how grounding Copilot in Azure Course Blueprints transforms your study journey into smarter, more visual experience: 🧭 Clickable guides that transform modules into intuitive roadmaps 🌐 Dynamic visual maps revealing how Azure services connect ⚖️ Side-by-side comparisons that clarify roles, services, and security models Whether you're a trainer, a student, or just certification-curious, Copilot becomes your shortcut to clarity, confidence, and mastery. Navigating Azure Certifications with Copilot and Azure Course Blueprints | Microsoft Community HubAI for Operations - Copilot Agent Integration
Solution ideas The original framework introduced several Logic App and Function App patterns for SQL BPA, Update Manager, Cost Management, Anomaly Detection, and Smart Doc creation. In this article we add two Copilot Studio Agents, packaged in the GitHub repository Microsoft Azure AI for Operation Framework, designed to be deployed in a dedicated subscription (e.g., OpenAI-CoreIntegration): Copilot FinOps Agent – interactive cost & usage analysis Copilot Update Manager Agent – interactive patch status & one-time updates Architecture Copilot FinOps Agent A Copilot Studio agent that lets stakeholders chat in natural language to retrieve, compare, and summarise cost data—without leaving Teams. Dataflow # Stage Description Initial Trigger User message (Teams / Copilot Studio web) invoke topic The conversation kicks off the topic “Analyze Azure Costs”. 1 Pre-Processing Power Automate flow captures tenant ID, subscription filters, date range. 2 Cost Query Azure Cost Management APIs pull actual and previous spend, returning JSON rows (service name, cost €). 3 OpenAI Analysis Data is analyzed by OpenAI\Copilot Agent following the flow structure. 4 Response Formatting Copilot Studio flow format the output as a table. 5 Chat Reply Copilot agent posts the insight list. Users can ask any kind of question related the FinOps topic. Components Microsoft Copilot Studio (Developer licence) – low-code agent designer Power Automate Premium – orchestrates REST calls, prompt assembly, file handling Azure Cost Management + Billing – source of spend data (Rest API) Azure OpenAI Service – GPT-4o and o3-mini reasoning & text generation Microsoft Teams – chat surface for Q&A, cards, and adaptive actions Potential use cases Finance teams asking “Why did VM spend jump last week?” Engineers requesting a monthly cost overview before sprint planning Leadership dashboards that can be drilled into via natural-language chat Copilot Update Manager Agent A Copilot Studio agent that surfaces patch compliance and can trigger ad-hoc One-Time Updates for selected VMs directly from the chat. Dataflow # Stage Description Initial Trigger User message (Teams / Copilot Studio web) invoke topic. The conversation kicks off the topic “Analyze Azure Costs”. 1 Pre-Processing Flow validates RBAC and captures target scope (subscription / RG / VM). 2 Patch Status Query Azure Update Manager & Resource Graph query patchassessmentresources for KBs, severities, pending counts. 3 OpenAI Report GPT-4o - o3-mini generates: • VM-level summary (English) • General Overview 4 Adaptive Card Power Automate builds an Adaptive Card listing non-compliant VMs with “One-time Update”- "No action" buttons. 5a User Action – Review User inspects details or asks follow-up questions. 5b User Action – Patch Now Clicking One-time Update calls Update Manager REST API to start a One-Time Update job. 6 Confirmation Agent posts job ID, live status, and final success / error summary. Components Microsoft Copilot Studio – conversational front-end Power Automate Premium – API orchestration & status polling Azure Update Manager – compliance data & patch execution Azure OpenAI Service – explanation & remediation text Microsoft Teams – Adaptive Cards with action buttons Potential use cases Service owners getting a daily compliance digest with the ability to remediate on demand Security officers validating zero-day patch rollout status via chat Help-desk agents triaging “Is VM X missing critical updates?” without opening the Azure portal Prerequisites Resource Quantity Notes Copilot Studio Developer licence 1 Assign in Microsoft 365 Admin Center Power Automate Premium licence 1 user Needed for HTTP, Azure AD, OpenAI connectors Microsoft Teams 1 user Chat interface Azure subscription 1 Dedicated OpenAI-CoreIntegration recommended GitHub repo latest Microsoft Azure AI for Operation Framework Copilot Agent Copilot Studio User Experience Deployment steps (high level) Assign licences – Copilot Studio Developer + Power Automate Premium Create Copilot Studio Agent New Agent → Skip to configure → fill basics → Create → Settings → disable GenAI orchestration Import topics Copilot topic Update Manager (link to configuration file) Copilot topic FinOps (link to configuration file) Publish & share the agent to Teams. Verify permission scopes for Cost Management and Update Manager APIs. Start chatting! Feel free to clone the GitHub repo, adapt the topics to your tag taxonomy or FinOps dashboard structure, and let us know in the comments how Copilot Agents are transforming your operational workflows and... Stay Tuned for the next updates! Contributors Principal authors Tommaso Sacco | Cloud Solutions Architect Simone Verza | Cloud Solution Architect Special thanks Carmelo Ferrara | Director CSA Antonio Sgrò | Sr CSA Manager Marco Crippa | Sr CSA ManagerLesson Learned #529: Troubleshooting Application Slowness Using SSMS Copilot
Some days ago, I worked on a support case where a customer reported application slowness affecting multiple users. Instead of jumping into traces or manually writing diagnostic queries, we used SSMS Copilot to investigate the issue. I would like to share with you how we diagnosed and understood the root cause. To illustrate the case, let’s walk through a simplified example: we create a new table, and right after that, we add a new column to it. CREATE TABLE Ejemplo2 (ID INT) BEGIN TRANSACTION ALTER TABLE dbo.eJEMPLO2 ADD NuevoCampo INT NULL Using SQL Server Management Studio and Copilot we executed the following prompt: Please, provide all currently running or suspended sessions. Include session ID, status, command, wait type (if any), application_name, wait time, and current SQL text. We got the following results: I executed multiple times the same prompt and always the session ID 67 is in suspended mode and Wait_type LCK_M_SCH_S, for this reason, I run a new prompt: Please, provide all sessions that are currently blocked by another session. Include session ID, the blocking session ID, wait type, and the blocked SQL text . At the end, I found that the session 51 is blocking the session ID 67 and for this reason, I run a new prompt: do we any have active transaction pending for commit for the session ID 51. So, I understand that the Session ID 51 has a transaction open, so, let's ask the details of the session 51, with a new prompt: Please, show the most recent SQL statement executed by session ID 51, even if the session is currently sleeping or not running any active request. Include the session status and login name as well. Use sys.dm_exec_connections and most_recent_sql_handle to retrieve the query text if necessary. Well, we identified the problem, the session ID 67 is running a SELECT * FROM sys.Ejemplo2 but it's beging blocked by the session 51. Session ID 51 hasn’t finished its transaction, and now we either need to commit, rollback, or kill that session, especially if we don’t have access to the application that owns it. Before resolving the issue, I asked Copilot an additional prompt: Please, explain why session ID 67 is currently waiting. Include the wait type, and explanation of that, the resource being waited on, how long it has been waiting (seconds), and the SQL text. Also identify if another session is blocking it. The name of the object and schema Please, provide recommendations to prevent or reduce this kind of blocking situation in the future, based on the current wait type and blocking scenario observed with session ID 67. Please, summarize all current blocking chains in the system. Include blocking session IDs, blocked session IDs, wait types, wait durations, login names, and SQL statements involved.Boosting Productivity with Ansys RedHawk-SC and Azure NetApp Files Intelligent Data Infrastructure
Discover how integrating Ansys Access with Azure NetApp Files (ANF) is revolutionizing cloud-based engineering simulations. This article reveals how organizations can harness enterprise-grade storage performance, seamless scalability, and simplified deployment to supercharge Ansys RedHawk-SC workloads on Microsoft Azure. Unlock faster simulations, robust data management, and cost-effective cloud strategies—empowering engineering teams to innovate without hardware limitations. Dive in to learn how intelligent data infrastructure is transforming simulation productivity in the cloud!
Modernizing Loan Processing with Gen AI and Azure AI Foundry Agentic Service
Scenario Once a loan application is submitted, financial institutions must process a variety of supporting documents—including pay stubs, tax returns, credit reports, and bank statements—before a loan can be approved. This post-application phase is often fragmented and manual, involving data retrieval from multiple systems, document verification, eligibility calculations, packet compilation, and signing. Each step typically requires coordination between underwriters, compliance teams, and loan processors, which can stretch the processing time to several weeks. This solution automates the post-application loan processing workflow using Azure services and Generative AI agents. Intelligent agents retrieve and validate applicant data, extract and summarize document contents, calculate loan eligibility, and assemble structured, compliant loan packets ready for signing. Orchestrated using Azure AI Foundry, the system ensures traceable agent actions and responsible AI evaluations. Final loan documents and metrics are stored securely for compliance and analytics, with Power BI dashboards enabling real-time visibility for underwriters and operations teams. Architecture: Workflow Description: The loan processing architecture leverages a collection of specialized AI agents, each designed to perform a focused task within a coordinated, intelligent workflow. From initial document intake to final analytics, these agents interact seamlessly through an orchestrated system powered by Azure AI Foundry, GPT-4o, Azure Functions and the Semantic Kernel. The agents not only automate and accelerate individual stages of the process but also communicate through an A2A layer to share critical context—enabling efficient, accurate, and transparent decision-making across the pipeline. Below is a breakdown of each agent and its role in the system. It all begins at the User Interaction Layer, where a Loan Processor or Underwriter interacts with the web application. This interface is designed to be simple, intuitive, and highly responsive to human input. As soon as a request enters the system, it’s picked up by the Triage Agent, powered by GPT-4o or GPT-4o-mini. This agent acts like a smart assistant that can reason through the problem and break it down into smaller, manageable tasks. For example, if the user wants to assess a new applicant, the Triage Agent identifies steps like verifying documents, calculating eligibility, assembling the loan packet, and so on. Next, the tasks are routed to the Coordinator Agent, which acts as the brains of the operation. Powered by Azure Functions & Sematic Kernel, this agent determines the execution order, tracks dependencies, and assigns each task to the appropriate specialized agent. The very first action that the Coordinator Agent triggers is the Applicant Profile Retrieval Agent. This agent taps into Azure AI Search, querying the backend to retrieve all relevant data about the applicant — previous interactions, submitted documents, financial history, etc. This rich context sets the foundation for the steps that follow. Once the applicant profile is in place, the Coordinator Agent activates a set of specialized agents, as outlined to perform specialized tasks as per the prompt received in the interaction layer. Below is the list of specialized agents: a. Documents Verification Agent: This agent checks and verifies the authenticity and completeness of applicant-submitted documents as part of the loan process. Powered by: GPT-4o b. Applicant Eligibility Assessment Agent: It evaluates whether the applicant meets the criteria for loan eligibility based on predefined rules and document content. Powered by: GPT-4o c. Loan Calculation Agent: This agent computes loan values and terms based on the applicant’s financial data and eligibility results. Powered by: GPT-4o d. Loan Packet Assembly Agent: This agent compiles all verified data into a complete and compliant loan packet ready for submission or signing. Powered by: GPT-4o e. Loan Packet Signing Agent: It handles the digital signing process by integrating with DocuSign and ensures all necessary parties have executed the loan packet. Powered by: GPT-4o f. Analytics Agent: This agent connects with Power BI to update applicant status and visualize insights for underwriters and processors. Powered by: GPT-4o Components Here are the key components of your Loan Processing AI Agent Architecture: Azure OpenAI GPT-4o/GPT 4o mini: Advanced multimodal language model. Used to summarize, interpret, and generate insights from documents, supporting intelligent automation. Empowers agents in this architecture with contextual understanding and reasoning. Azure AI Foundry Agent Service: Agent orchestration framework. Manages the creation, deployment, and lifecycle of task-specific agents—such as classifiers, retrievers, and validators—enabling modular execution across the loan processing workflow. Semantic Kernel: Lightweight orchestration library. Facilitates in-agent coordination of functions and plugins. Supports memory, chaining of LLM prompts, and integration with external systems to enable complex, context-aware behavior in each agent. Azure Functions: Serverless compute for handling triggers such as document uploads, user actions, or decision checkpoints. Initiates agent workflows, processes events, and maintains state transitions throughout the loan processing pipeline. Azure Cosmos DB: Globally distributed NoSQL database used for agent memory and context persistence. Stores conversation history, document embeddings, applicant profile snapshots, and task progress for long running or multi-turn workflows. Agentic Content Filters: Responsible AI mechanism for real-time filtering. Evaluates and blocks sensitive or non-compliant outputs generated by agents using customizable guardrails. Agentic Evaluations: Evaluation framework for agent workflows. Continuously tests, scores, and improves agent outputs using both automatic and human-in-the-loop metrics. Power BI: Business analytics tool that visualizes loan processing stages, agent outcomes, and applicant funnel data. Enables real-time monitoring of agent performance, SLA adherence, and operational bottlenecks for decision makers. Azure ML Studio: Code-first development environment for building and training machine learning models in Python. Supports rapid iteration, experimentation, and deployment of custom models that can be invoked by agents. Security Considerations: Web App: For web applications, access control and identity management can be done using App Roles, which determine whether a user or application can sign in or request an access token for a web API. For threat detection and mitigation, Defender for App Service leverages the scale of the cloud to identify attacks targeting apps hosted on Azure App Service. Azure AI Foundry: Azure AI Foundry supports robust identity management using Azure Role-Based Access Control (RBAC) to assign roles within Microsoft Entra ID, and it supports Managed Identities for secure resource access. Conditional Access policies allow organizations to enforce access based on location, device, and risk level. For network security, Azure AI Foundry supports Private Link, Managed Network Isolation, and Network Security Groups (NSGs) to restrict resource access. Data is encrypted in transit and at rest using Microsoft-managed keys or optional Customer-Managed Keys (CMKs). Azure Policy enables auditing and enforcing configurations for all resources deployed in the environment. Additionally, Microsoft Entra Agent ID, which extends identity management and access capabilities to AI agents. Now, AI agents created within Microsoft Copilot Studio and Azure AI Foundry are automatically assigned identities in a Microsoft Entra directory centralizing agent and user management in one solution. AI Security Posture Management can be used to assess the security posture of AI workloads. Purview APIs enable Azure AI Foundry and developers to integrate data security and compliance controls into custom AI apps and agents. This includes enforcing policies based on how users interact with sensitive information in AI applications. Purview Sensitive Information Types can be used to detect sensitive data in user prompts and responses when interacting with AI applications. Cosmos DB: Azure Cosmos DB enhances network security by supporting access restrictions via Virtual Network (VNet) integration and secure access through Private Link. Data protection is reinforced by integration with Microsoft Purview, which helps classify and label sensitive data, and Defender for Cosmos DB to detect threats and exfiltration attempts. Cosmos DB ensures all data is encrypted in transit using TLS 1.2+ (mandatory) and at rest using Microsoft-managed or customer-managed keys (CMKs). Power BI: Power BI leverages Microsoft Entra ID for secure identity and access management. In Power BI embedded applications, using Credential Scanner is recommended to detect hardcoded secrets and migrate them to secure storage like Azure Key Vault. All data is encrypted both at rest and during processing, with an option for organizations to use their own Customer-Managed Keys (CMKs). Power BI also integrates with Microsoft Purview sensitivity labels to manage and protect sensitive business data throughout the analytics lifecycle. For additional context, Power BI security white paper - Power BI | Microsoft Learn Related Scenarios Financial Institutions: Banks and credit unions can streamline customer onboarding by using agentic services to autofill account paperwork, verify identity, and route data to compliance systems. Similarly, signing up for credit cards and applying for personal or business loans can be orchestrated through intelligent agents that collect user input, verify eligibility, calculate offers, and securely generate submission packets—just like in the proposed loan processing model. Healthcare: Healthcare providers can deploy a similar agentic architecture to simplify patient intake by pre-filling forms, validating insurance coverage in real-time, and pulling medical history from existing systems securely. Agents can reason over patient inputs and coordinate backend workflows, improving administrative efficiency and enhancing the patient experience. University Financial Aid/Scholarships: Universities can benefit from agentic orchestration for managing financial aid processes—automating the intake of FAFSA or institutional forms, matching students with eligible scholarships, and guiding them through complex application workflows. This reduces manual errors and accelerates support delivery to students. Car Dealerships’ Financial Departments: Agentic systems can assist car dealerships in handling non-lot inventory requests, automating the intake and validation of custom vehicle orders. Additionally, customer loan applications can be processed through AI agents that handle verification, calculation, and packet assembly—mirroring the structure in the loan workflow above. Commercial Real Estate: Commercial real estate firms can adopt agentic services to streamline property research, valuations, and loan application workflows. Intelligent agents can pull property data, fill out required financial documents, and coordinate submissions, making real estate financing faster and more accurate. Law: Law firms can automate client onboarding with agents that collect intake data, pre-fill compliance documentation, and manage case file preparation. By using AI Foundry to coordinate agents for documentation, verification, and assembly, legal teams can reduce overhead and increase productivity. Contributors: This article is maintained by Microsoft. It was originally written by the following contributors. Principal authors: Manasa Ramalinga| Principal Cloud Solution Architect – US Customer Success Oscar Shimabukuro Kiyan| Senior Cloud Solution Architect – US Customer Success Abed Sau | Principal Cloud Solution Architect – US Customer Success Matt Kazanowsky | Senior Cloud Solution Architect – US Customer SuccessGranting Azure Resources Access to SharePoint Online Sites Using Managed Identity
When integrating Azure resources like Logic Apps, Function Apps, or Azure VMs with SharePoint Online, you often need secure and granular access control. Rather than handling credentials manually, Managed Identity is the recommended approach to securely authenticate to Microsoft Graph and access SharePoint resources. High-level steps: Step 1: Enable Managed Identity (or App Registration) Step 2: Grant Sites.Selected Permission in Microsoft Entra ID Step 3: Assign SharePoint Site-Level Permission Step 1: Enable Managed Identity (or App Registration) For your Azure resource (e.g., Logic App): Navigate to the Azure portal. Go to the resource (e.g., Logic App). Under Identity, enable System-assigned Managed Identity. Note the Object ID and Client ID (you’ll need the Client ID later). Alternatively, use an App Registration if you prefer a multi-tenant or reusable identity. How to register an app in Microsoft Entra ID - Microsoft identity platform | Microsoft Learn Step 2: Grant Sites.Selected Permission in Microsoft Entra Open Microsoft Entra ID > App registrations. Select your Logic App’s managed identity or app registration. Under API permissions, click Add a permission > Microsoft Graph. Select Application permissions and add: Sites.Selected Click Grant admin consent. Note: Sites.Selected ensures least-privilege access — you must explicitly allow site-level access later. Step 3: Assign SharePoint Site-Level Permission SharePoint Online requires site-level consent for apps with Sites.Selected. Use the script below to assign access. Note: You must be a SharePoint Administrator and have the Sites.FullControl.All permission when running this. PowerShell Script: # Replace with your values $application = @{ id = "{ApplicationID}" # Client ID of the Managed Identity displayName = "{DisplayName}" # Display name (optional but recommended) } $appRole = "write" # Can be "read" or "write" $spoTenant = "contoso.sharepoint.com" # Sharepoint site host $spoSite = "{Sitename}" # Sharepoint site name # Site ID format for Graph API $spoSiteId = $spoTenant + ":/sites/" + $spoSite + ":" # Load Microsoft Graph module Import-Module Microsoft.Graph.Sites # Connect with appropriate permissions Connect-MgGraph -Scope Sites.FullControl.All # Grant site-level permission New-MgSitePermission -SiteId $spoSiteId -Roles $appRole -GrantedToIdentities @{ Application = $application } That's it, Your Logic App or Azure resource can now call Microsoft Graph APIs to interact with that specific SharePoint site (e.g., list files, upload documents). You maintain centralized control and least-privilege access, complying with enterprise security standards. By following this approach, you ensure secure, auditable, and scalable access from Azure services to SharePoint Online — no secrets, no user credentials, just managed identity done right.Azure NetApp Files solutions for three EDA Cloud-Compute scenarios
Table of Contents Abstract Introduction EDA Cloud-Compute scenarios Scenario 1: Burst to Azure from on-premises Data Center Scenario 2: “24x7 Single Set Workload” Scenario 3: "Data Center Supplement" Summary Abstract Azure NetApp Files (ANF) is transforming Electronic Design Automation (EDA) workflows in the cloud by delivering unparalleled performance, scalability, and efficiency. This blog explores how ANF addresses critical challenges in three cloud compute scenarios: Cloud Bursting, 24x7 All-in-Cloud, and Cloud-based Data Center Supplement. These solutions are tailored to optimize EDA processes, which rely on high-performance NFS file systems to design advanced semiconductor products. With the ability to support clusters exceeding 50,000 cores, ANF enhances productivity, shortens design cycles, and eliminates infrastructure concerns, making it the default choice for EDA workloads in Azure. Additionally, innovations such as increased L3 cache and the transition to DDR5 memory enable performance boosts of up to 60%, further accelerating the pace of chip design and innovation. Co-authors: Andy Chan, Principal Product Manager Azure NetApp Files Arnt de Gier, Technical Marketing Engineer Azure NetApp Files Introduction Azure NetApp Files (ANF) solutions support three major cloud compute scenarios running Electronic Design Automation (EDA) in Azure: Cloud Bursting 24x7 All-in-Cloud Cloud based Data Center Supplement ANF solutions can address the key challenges associated with each scenario. By providing an optimized solution stack for EDA engineers ANF will increase productivity and shorten design cycles, making ANF the de facto standard file system for running EDA workloads in Azure. Electronic Design Automation (EDA) processes are comprised of a suite of software tools and workflows used to design semiconductor products such as advanced computer processors (chips) which are all in need of high performance NFS file system solutions. The increasing demand for chips with superior performance, reduced size, and lower power consumption (PPA) is driven by today's rapid pace of innovation to power workloads such as AI. To meet this growing demand, EDA tools require numerous nodes and multiple CPUs (cores) in a cluster. This is where Azure NetApp Files (ANF) comes into play with its high-performance, scalable file system. ANF ensures that data is efficiently delivered to these compute nodes. This means a single cluster—sometimes encompassing more than 50,000 cores—can function as a unified entity, providing both scale-out performance and consistency which is essential for designing advanced semiconductor products. ANF is the most performance optimized NFS storage in Azure making it the De facto solution for EDA workloads. According to Philip Steinke, AMD's Fellow of CAD Infrastructure and Physical Design, the main priority is to maximize the productivity of chip designers by eliminating infrastructure concerns related to compute and file system expansion typically experienced with on-premises deployments that require long planning cycles and significant capital expenditure. In register-transfer level (RTL) simulations, Microsoft Azure showcased that moving to a CPU with greater amounts of L3 Cache can give EDA users a performance boost of up to 60% for their workloads. This improvement is attributed to increased L3 cache, higher clock speeds (instructions per cycle), and the transition from DDR4 to DDR5 memory. Azure’s commitment to providing high-performing, on-demand HPC (High-Performance Computing) infrastructure is a well-known advantage and has become the primary reason EDA companies are increasingly adopting Azure for their chip design needs. In this paper, three different scenarios of Azure for EDA are explored, namely “Cloud Bursting”, “24x7 Single Set Workload” and “Data Center Supplement” as a reference framework to help guide engineer’s Azure for EDA journey. EDA Cloud-Compute scenarios The following sections delve into three key scenarios that address the computational needs of EDA workflows: “Cloud Bursting,” “24x7 Single Set Workload,” and “Data Center Supplement.” Each scenario highlights how Azure's robust infrastructure, combined with high-performance solutions like Azure NetApp Files, enables engineering teams to overcome traditional limitations, streamline chip design processes, and significantly enhance productivity. Scenario 1: Burst to Azure from on-premises Data Center An EDA workload is made up of a series of workflows where certain steps are bursty which can lead to incidents in semiconductor project cycles where compute demand exceeds the on-premises HPC server cluster capacity. Many EDA customers have been bursting to Azure to speed up their engineering projects. In one example, a total of 120,000 cores were deployed serving in many clusters, all were well supported with the high-performance capabilities of ANF. As design projects approach completion, the design is continuously and incrementally modified to fix bugs, synthesis and timing issues, optimization of area, timing and power, resolving issues associated with manufacturing design rule checks, etc. When design changes are made, many if not all the design steps must be re-run to ensure the change did not break the design. As a result, “design spins” or “large regression” jobs will put a large compute demand on the HPC server cluster. This leads to long job scheduler queues (IBM LSF and Univa Grid Engine are two common schedulers for EDA) where jobs wait to be dispatched to run on an available compute node. Competing project schedules are another reason HPC server cluster demands can exceed on-premises fixed capacity. Most engineering divisions within a company share infrastructure resources across teams and projects which inevitably leads to oversubscription of compute capacity and long job queues resulting in production delays. Bursting EDA jobs into Azure with its available compute capacity, is a way to alleviate these delays. For example, Azure’s latest CPU offering can deliver up to 47% shorter turnaround times for RTL simulation than on-premises. Engineering management tries to increase productivity with effective use of their EDA tool licensing. Utilizing Azure's on-demand compute resources and high-performance storage solutions like Azure NetApp Files, enables engineering teams to accelerate design cycles and reduce Non-recurring Engineering (NRE) costs, enhancing productivity significantly. For “burst to Azure” scenarios that allow engineers quick access to compute resources to finish a job without worrying about the underlying NFS infrastructure and traditional complex management overhead, ANF delivers: High Performance: up to 826,000 IOPS per large volume, serving the data for the most demanding simulations with ease to reduce turn-around-time. Scalability: As EDA projects advance, the data generated can grow exponentially. ANF provides large-capacity single namespaces with volumes up to 2PiB, enabling your storage solution to scale seamlessly, while supporting compute clusters with more than 50,000 cores. Ease of Use: ANF is designed for simplicity, with SaaS-like user experience, allowing deployment and management with a few clicks or API automation. Since storage deployment can be done rapidly, engineering to access their EDA HPC hardware quickly for their jobs. Cost-Effectiveness: ANF offers cool access, which transparently moves ‘cold’ data blocks to lower-cost Azure Storage. Additionally, Reserved Capacity (RC) can provide significant cost savings compared to pay-as-you-go pricing, further reducing the high upfront CapEx costs and long procurement cycle associated with on-premises storage solutions. Use the ANF effective pricing estimator to estimate your savings. Reliability and Security: ANF provides enterprise-grade data management and security features, ensuring that your critical EDA data is protected and available when you need it with key management and encryption built-in. Scenario 2: “24x7 Single Set Workload” As Azure for EDA matured over time and the value of providing engineers with available and faster HPC Infrastructure is becoming more widely shared, more users are now moving a entire sets of workloads into Azure that run 24x7. In addition to SPICE or RTL simulations, one such set of workloads is "digital signoff” with the same goal of increasing productivity. Scenario 1 concerns cloud bursting which involves batch processes with high performance and rapid deployment, whereas Scenario 2 involves operating a set of workloads with additional ANF capabilities for data security and user control needs. QoS support: ANF's QoS function fine-tunes storage utilization by establishing a direct correlation between volume size (quota) and performance, which set storage limit an EDA tool or workload may have access to. Snapshot data protection: As more users are using Azure resources, data protection is crucial. ANF snapshots protect primary data often and efficiently for fast recovery from corruption or loss, by restoring a volume to a snapshot in seconds or by restoring individual files from a snapshot. Enabling snapshots is recommended for user home directories and group shares for this reason as well. Large volume support: A set of workloads generates greater output than a single workload, and as such ANF’s large volume support is a feature that’s being widely adopted by EDA users of this scenario. ANF now supports single volumes up to 2PiB in size, allowing a more fine-tuned management of user’s storage footprint. Cool access: Cool access is an ANF feature that enables better cost control because only data that is being worked on at any given time remains in the hot tier. This functionality enables inactive data blocks from the volume and volume snapshots to be transferred from the hot tier to an Azure storage account (the cool tier), saving cost. Because EDA workloads are known to be metadata heavy, ANF does not relocate metadata to the cool tier, ensuring that metadata operations operate as expected. Dynamic capacity pool resizing: Cloud compute resources can be dynamically allocated. To support this deployment model, Azure NetApp Files (ANF) also offers dynamic pool resizing, which further enhances Azure-for-EDA's value proposition. If the size of the pool remains constant but performance requirements fluctuate, enabling dynamic provisioning and deprovisioning of capacity pools of different types provides just-in-time performance. This approach lowers costs during periods when high performance is not needed. Reserved Capacity: Azure allows compute resources to be reserved as a way to guarantee access to that capacity and allowing you to receive significant cost savings compared to the standard "pay-as-you-go" pricing model. This Azure offering is available to ANF. A reservation in 100-TiB and 1-PiB units per month for a one- or three-year term for a particular service level within a region is now available. Scenario 3: "Data Center Supplement" This scenario builds on Scenarios 1 and 2, while Scenario 3 involves EDA users expanding their workflow into Azure as their data center. In this scenario, a mixed EDA flow is hosted with tools from several EDA ISVs, spanning frontend, backend, and Analog mixed signal are being deployed. EDA Companies such as d-Matrix were able to design an entire AI chip, all in Azure as an example of Scenario 3. In this data center supplement scenario, data mobility and additional data life cycle management solutions are essential. Once again, Azure NetApp Files (ANF) rises to the challenge by offering additional features within its solution stack Backup support: ANF has a policy-based backup feature that uses AES-256-bit encryption during the encoding of the received backup data. Backup frequency is defined by a policy. Cross-region replication: ANF data can be replicated asynchronously between Azure NetApp Files volumes (source and destination) with cross-region replication. The source and destination volumes must be deployed in different Azure regions. The service level for the destination capacity pool might be the same or different, allowing customers to fine-tune their data protection demands as efficiently as possible. Cross-zone replication: Similar to the Azure NetApp Files cross-region replication feature, the cross-zone replication (CZR) capability provides data protection between volumes in different availability zones. You can asynchronously replicate data from an Azure NetApp Files volume (source) in one availability zone to another Azure NetApp Files volume (destination) in another availability zone. This capability enables you to fail over your critical application if a zone-wide outage or disaster happens. BC/DR: Users can construct their own solution based on their own goals by using a variety of BC/DR templates that include snapshots, various replication types, failover capabilities, backup, and support for REST API, Azure CLI, and Terraform. Summary The integration of ANF into the EDA workflow addresses the limitations of traditional on-premises infrastructure. By leveraging the latest CPU generations and Azure's on-demand HPC infrastructure, EDA users can achieve significant performance gains and improve productivity, all while being connected by the most optimized, performant file system that’s simple to deploy and support. The three Azure for EDA scenarios—Cloud Bursting, 24x7 Single Set Workload, and Data Center Supplement—showcase Azure's adaptability and effectiveness in fulfilling the changing needs of the semiconductor industry. As a result, ANF has become the default NFS solution for EDA in Azure, allowing businesses to innovate even faster.Streamlining data discovery for AI/ML with OpenMetadata on AKS and Azure NetApp Files
This article contains a step-by-step guide to deploying OpenMetadata on Azure Kubernetes Service (AKS), using Azure NetApp Files for storage. It also covers the deployment and configuration of PostgreSQL and OpenSearch databases to run externally from the Kubernetes cluster, following OpenMetadata best practices, managed by NetApp® Instaclustr®. This comprehensive tutorial aims to assist Microsoft and NetApp customers in overcoming the challenges of identifying and managing their data for AI/ML purposes. By following this guide, users will achieve a fully functional OpenMetadata instance, enabling efficient data discovery, enhanced collaboration, and robust data governance.
