A secure Azure landing zone for AI: private endpoints, managed identity, APIM token limits (Bicep)
I wrote up a reference pattern for running Azure AI Foundry workloads inside a secure landing zone: Foundry (AIServices) behind a private endpoint with the three required private DNS zones, local auth disabled in favour of managed identity, and Azure API Management in front using llm-token-limit and llm-emit-token-metric to control and measure token usage. Article: https://gisli78.substack.com/p/a-secure-azure-landing-zone-for-ai Code (Bicep, with automated tests): https://github.com/gislig/azure-ai-landing-zone Feedback welcome, especially from anyone running this pattern in production. #mvpbuzz #azure #azug #microsoft
Missing equivalent for Python MemorySearchTool and AgentMemorySettings in C# SDK
Hi Team, I am currently working with the Azure AI Foundry Agent Service (preview). I’ve been reviewing the documentation for managed long-term memory, specifically the "Automatic User Memory" features demonstrated in the Python SDK here: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/memory-usage?view=foundry&tabs=python. In Python, it is very straightforward to attach a MemorySearchTool to an agent and use AgentMemorySettings(scope="user_123") during a run. This allows the service to automatically extract, consolidate, and retrieve memories without manual intervention. However, in the https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Projects#memory-store-operations, I only see the low-level MemoryStoreClient which appears to require manual CRUD operations on memory items. My Questions: Is there an equivalent high-level AgentMemorySearchTool or similar abstraction in the current C# NuGet package (Azure.AI.Projects) that handles automatic extraction and retrieval? If not currently available, is this feature on the immediate roadmap for the .NET SDK? Are there any samples showing how to achieve "automatic" memory (where the Agent extracts facts itself) using the C# SDK without having to build a custom orchestration layer or call REST APIs directly? Any guidance on the timeline for feature parity between the Python and .NET SDKs regarding Agent Memory would be greatly appreciated. SDK Version: Azure.AI.Projects 1.2.0-beta.5
Announcing the AI Dev Days Hackathon winners
We’re excited to officially announce the winners of the AI Dev Days Hackathon! Over the course of the hackathon, developers from around the world teamed up to build AI solutions using Microsoft’s AI platform, agentic development patterns, and modern DevOps workflows. The range of ideas (and the level of build quality) made judging a fun kind of hard.Build Your Ignite Schedule: Top Sessions for Developers
Get Ready for Microsoft Ignite Welcome back to our Azure Tech Community Microsoft Ignite 2025 series! If you joined us for Your Guide to Azure Community Activations at Microsoft Ignite 2025, you already know that Microsoft Ignite isn’t just about product updates, it’s where ideas, innovation, and community come together. With hundreds of sessions across every area of Azure, it can be hard to know where to focus. That’s why we’ve curated a list of sessions tailored specifically for Developers to help you make the most of your time, strengthen your technical strategy, and get inspired for the year ahead. Use the Microsoft Ignite session scheduler feature in the session catalog to personalize your agenda, save your favorites, and organize your time at Microsoft Ignite. Make Every Minute Count: Top Recommended Sessions for Developers Microsoft Ignite moves fast, so it pays to plan your path before you arrive. By organizing your schedule around your interests and goals, you’ll be able to maximize learning opportunities, connect with peers, and leave with actionable insights. The recommendations below highlight our top picks for Developers who want to build, modernize, and innovate using Azure and AI. Reimagining software development with GitHub Copilot and AI agents (BRK105) Discover how AI is transforming the development workflow, from writing code to managing pull requests, and boosting productivity. Build AI Apps fast with GitHub and Azure AI Foundry in action (BRK110) Explore how to build, train, and deploy intelligent apps using GitHub and Azure AI Foundry in record time. Building and deploying data agents in Microsoft Fabric (THR738) Learn how to build, deploy, and manage intelligent data agents in Microsoft Fabric using curated data, Git-powered CI/CD, and best practices for enterprise-scale AI integration. CI/CD for Fabric: Accelerating Lakehouse to production in 25 minutes (THR739) Bring software-engineering rigor to your data with Microsoft Fabric—learn how to use Git-integrated deployment pipelines, parameterized deployments, and automated checks to confidently operationalize your Lakehouse from validation to production. Build A2A and MCP Systems using SWE Agents and agent‑framework (LAB513) Explore how to build, orchestrate, and deploy multi‑agent systems with the new agent‑framework, SWE Agents, and MCP—hands‑on and production‑minded. Before diving into your targeted sessions, make time for these essential moments that set the stage for everything happening at Microsoft Ignite. Opening Keynote (KEY01) Hear from Microsoft leaders as they unveil the latest innovations shaping the future of AI, cloud, and the developer ecosystem. This is the session that sets the tone for the entire event. Innovation Session: Your AI Apps and Agent Factory (BRK1706) Join Microsoft engineering leaders as they explore the foundational AI capabilities powering the Microsoft Cloud. Learn how Azure AI services, Copilot experiences, and responsible AI frameworks come together to help developers and organizations innovate faster Innovation Session: Agents at work: Shaping the future of business (BRK1708) Learn how AI-powered agents and digital workers are reshaping collaboration and productivity. This session showcases real-world use cases across Microsoft 365, Dynamics, and Azure ecosystems. Plan Smarter with the Session Scheduler With the “add to schedule” feature in the session catalog you’ll be able to: Browse and filter all Microsoft Ignite sessions by topic, product, or persona. Save your favorite sessions to build a personalized schedule. Set reminders and block time for networking, community booths, and demos. Stay Connected with the Azure Tech Community Your Microsoft Ignite journey doesn’t stop when the sessions end, the conversation continues across the Azure Tech Community. Share the sessions you’re most excited about using #MSIgnite #AzureTechCommunity, tag azure, and connect with other developers exploring the future of cloud management and security. Follow the Azure Tech Community for real-time updates, announcements, and product news throughout Ignite. See You at Microsoft Ignite 2025 With the right plan in place, every session becomes an opportunity to learn, grow, and connect. Explore these recommendations, save your favorites, and get ready for an unforgettable Microsoft Ignite 2025 experience.Your Guide to Azure Community Activations at Microsoft Ignite 2025
Microsoft Ignite 2025 is right around the corner! From November 18–21 in San Francisco, we’re excited to bring the Azure community together for four days of learning, connection, and fun! Whether you’re joining us onsite at the Moscone Center or tuning in online, the Community Space will be buzzing with MVP meetups, interactive theater sessions, and plenty of opportunities to network. This is the first in a series of posts highlighting what you can expect at Microsoft Ignite. Today, we’re spotlighting Azure Community activations across Infrastructure, AI, Data, and MVP programs. In upcoming posts, we’ll dive deeper into sessions tailored for IT professionals, developers, and even first-time attendees. Azure Infrastructure Microsoft Ignite is packed with practical insights to help you migrate, modernize, and secure workloads. Learn how to Troubleshoot AKS networking with Agentic AI and strengthen your AI workload resiliency with Azure’s networking stack. Dive into migration best practices with sessions on moving data for analytics, lessons from Azure MVPs, and community insights from real-world projects. And don’t miss the fan favorite: Learn Infrastructure-as-Code through Minecraft —where cloud automation meets creativity. AI & Agents If you’re passionate about AI, the community sessions will put you at the center of what’s next. Connect with peers at the Global AI Community meetup. Get a sneak peek at what’s coming with Azure AI Insiders. Hear directly from MVPs and Microsoft leaders on shaping the future of Azure AI Foundry and how AI is transforming customer innovation. Azure Data For those focused on data, Microsoft Ignite is your chance to learn, influence, and connect. Share your feedback on SQL Server Management Studio and Copilot in SSMS. Bring your toughest questions to a Q&A with Azure Data Leadership. And join the community to explore real-world data intelligence solutions and career-building opportunities across the data ecosystem. MVP Program Interested in becoming a Microsoft MVP or expanding your community impact? Learn how to get nominated and grow your influence in So you want to become an MVP? Join program leads and MVPs to hear their stories in Becoming an MVP in Azure, AI, or the Data Platform. These sessions are the perfect place to start if you’re looking to give back and level up your community journey. Stay Connected Year-Round The conversations doesn't stop after Microsoft Ignite. Join the communities that keep the learning going: AKS Community (Infrastructure) Azure AI Foundry (AI) Global AI Community (AI) Fabric Community (Data) Azure Data Community (Data) Fellow Developers And this is just the beginning. Microsoft Ignite is packed with opportunities to learn from experts, connect with peers, and explore what’s next with Azure. Stay tuned for our upcoming posts, where we’ll share curated session highlights designed for different audiences to help you make the most of your Microsoft Ignite experience. 👉 Be sure to mark your calendar, start building your schedule, and get ready to be inspired at Microsoft Ignite 2025The brand new Azure AI Agent Service at your fingertips
Intro Azure AI Agent Service is a game-changer for developers. This fully managed service empowers you to build, deploy, and scale high-quality, extensible AI agents securely, without the hassle of managing underlying infrastructure. What used to take hundreds of lines of code can now be achieved in just a few lines! So here it is, a web application that streamlines document uploads, summarizes content using AI, and provides seamless access to stored summaries. This article delves into the architecture and implementation of this solution, drawing inspiration from our previous explorations with Azure AI Foundry and secure AI integrations. Architecture Overview Our Azure AI Agent Service WebApp integrates several Azure services to create a cohesive and scalable system: Azure AI Projects & Azure AI Agent Service: Powers the AI-driven summarization and title generation of uploaded documents. Azure Blob Storage: Stores the original and processed documents securely. Azure Cosmos DB: Maintains metadata and summaries for quick retrieval and display. Azure API Management (APIM): Manages and secures API endpoints, ensuring controlled access to backend services. This architecture ensures a seamless flow from document upload to AI processing and storage, providing users with immediate access to summarized content. Azure AI Agent Service – Frontend Implementation The frontend of the Azure AI Agent Service WebApp is built using Vite and React, offering a responsive and user-friendly interface. Key features include: Real-time AI Chat Interface: Users can interact with an AI agent for various queries. Document Upload Functionality: Supports uploading documents in various formats, which are then processed by the backend AI services. Document Repository: Displays a list of uploaded documents with their summaries and download links. This is the main UI , ChatApp.jsx. We can interact with Chat Agent for regular chat, while the keyword “upload:” activates the hidden upload menu. Azure AI Agent Service – Backend Services The backend is developed using Express.js, orchestrating various services to handle: File Uploads: Accepts documents from the frontend and stores them in Azure Blob Storage. AI Processing: Utilizes Azure AI Projects to extract text, generate summaries, and create concise titles. Metadata Storage: Saves document metadata and summaries in Azure Cosmos DB for efficient retrieval. One of the Challenges was to not recreate the Agents each time our backend reloads. So a careful plan is configured, with several files – modules for the Azure AI Agent Service interaction and Agents creation. The initialization for example is taken care by a single file-module: const { DefaultAzureCredential } = require('@azure/identity'); const { SecretClient } = require('@azure/keyvault-secrets'); const { AIProjectsClient, ToolUtility } = require('@azure/ai-projects'); require('dotenv').config(); // Keep track of global instances let aiProjectsClient = null; let agents = { chatAgent: null, extractAgent: null, summarizeAgent: null, titleAgent: null }; async function initializeAI(app) { try { // Setup Azure Key Vault const keyVaultName = process.env.KEYVAULT_NAME; const keyVaultUrl = `https://${keyVaultName}.vault.azure.net`; const credential = new DefaultAzureCredential(); const secretClient = new SecretClient(keyVaultUrl, credential); // Get AI connection string const secret = await secretClient.getSecret('AIConnectionString'); const AI_CONNECTION_STRING = secret.value; // Initialize AI Projects Client aiProjectsClient = AIProjectsClient.fromConnectionString( AI_CONNECTION_STRING, credential ); // Create code interpreter tool (shared among agents) const codeInterpreterTool = ToolUtility.createCodeInterpreterTool(); const tools = [codeInterpreterTool.definition]; const toolResources = codeInterpreterTool.resources; console.log('🚀 Creating AI Agents...'); // Create chat agent agents.chatAgent = await aiProjectsClient.agents.createAgent("gpt-4o-mini", { name: "chat-agent", instructions: "You are a helpful AI assistant that provides clear and concise responses.", tools, toolResources }); console.log('✅ Chat Agent created'); // Create extraction agent agents.extractAgent = await aiProjectsClient.agents.createAgent("gpt-4o-mini", { name: "extract-agent", instructions: "Process and clean text content while maintaining structure and important information.", tools, toolResources }); console.log('✅ Extract Agent created'); // Create summarization agent agents.summarizeAgent = await aiProjectsClient.agents.createAgent("gpt-4o-mini", { name: "summarize-agent", instructions: "Create concise summaries that capture main points and key details.", tools, toolResources }); console.log('✅ Summarize Agent created'); // Create title agent agents.titleAgent = await aiProjectsClient.agents.createAgent("gpt-4o-mini", { name: "title-agent", instructions: `You are a specialized title generation assistant. Your task is to create titles for documents following these rules: 1. Generate ONLY the title text, no additional explanations 2. Maximum length of 50 characters 3. Focus on the main topic or theme 4. Use proper capitalization (Title Case) 5. Avoid special characters and quotes 6. Make titles clear and descriptive 7. Respond with nothing but the title itself Example good responses: Digital Transformation Strategy 2025 Market Analysis: Premium Chai Tea Cloud Computing Implementation Guide Example bad responses: "Here's a title for your document: Digital Strategy" (no explanations needed) This document appears to be about digital transformation (just the title needed) The title is: Market Analysis (no extra text)`, tools, toolResources }); console.log('✅ Title Agent created'); // Store in app.locals app.locals.aiProjectsClient = aiProjectsClient; app.locals.agents = agents; console.log('✅ All AI Agents initialized successfully'); return { aiProjectsClient, agents }; } catch (error) { console.error('❌ Error initializing AI:', error); throw error; } } // Export both the initialization function and the shared instances module.exports = { initializeAI, getClient: () => aiProjectsClient, getAgents: () => agents }; Our backend utilizes 4 agents, creating the Azure AI Agent Service Agents and we will find them in the portal, when the Backend deploys At the same time, each interaction is stored and managed as thread and that’s how we are interacting with the Azure AI Agent Service. Deployment and Security of Azure AI Agent Service WebApp Ensuring secure and efficient deployment is crucial. We’ve employed: Azure API Management (APIM): Secures API endpoints, providing controlled access and monitoring capabilities. Azure Key Vault: Manages sensitive information such as API keys and connection strings, ensuring data protection. Every call to the backend service is protected with Azure API Management Basic Tier. We have only the required endpoints pointing to the matching Endpoints of our Azure AI Agent Service WebApp backend. Also we are storing the AIConnectionString variable in Key Vault and we can move all Variables in Key Vault as well, which i recommend ! Get started with Azure AI Agent Service To get started with Azure AI Agent Service, you need to create an Azure AI Foundry hub and an Agent project in your Azure subscription. Start with the quickstart guide if it’s your first time using the service. You can create a AI hub and project with the required resources. After you create a project, you can deploy a compatible model such as GPT-4o. When you have a deployed model, you can also start making API calls to the service using the SDKs. There are already 2 Quick-starts available to get your Azure AI Agent Service up and running, the Basic and the Standard. I have chosen the second one the Standard plan, since we have a WebApp, and the whole Architecture comes very handy ! We just added the CosmosDB interaction and the API Management to extend to an enterprise setup ! Our own Azure AI Agent Service deployment, allows us to interact with the Agents, and utilize tools and functions very easy. Conclusion By harnessing the power of Azure’s cloud services, we’ve developed a scalable and efficient web application that simplifies document management through AI-driven processing. This solution not only enhances productivity but also ensures secure and organized access to essential information. References Azure AI Agent Service Documentation What is Azure AI Agent Service Azure AI Agent Service Quick starts Azure API Management Azure AI Foundry Azure AI Foundry Inference Demo
Overview of SR-CNN algorithm in Azure Anomaly Detector
Author: Tony Xing (@XingGuodong), AI Platform, C + AI In the last blog “Introducing Azure Anomaly Detector API”, I didn't provide enough details on one of the algorithms. As the algorithm paper was in the publishing process. The paper was accepted by KDD 2019 for oral presentation later, and this blog serves as an overview of the SR-CNN algorithm and for more details user can always read the paper. By the way, we have a 2-minute video here. Problem definition Before we go into details, let us revisit the problem definition of time series anomaly detection. Challenges For any time-series anomaly detection system that is operating in production with a large scale, there are quite a few challenges, especially on the three areas below: 1. Lack of labels - As you can imagine, with signals generated from clients, services, and sensors every second, the huge amount of volume makes it infeasible to manually label the data. 2. Generalization - With real-world data, there are so many different types of time series with different characteristics, which make it hard to generalize and find a silver bullet to solve all the problems. Some examples can be found in the figure below. 3. Efficiency - For any online anomaly detection system, efficiency is one of the key challenges. The system is expected to have low compute cost and low latency for serving. Inspiration In the computer vision domain, there is this concept called “visual saliency detection”. Saliency is what "stands out" in a photo or scene, enabling our eye-brain to quickly focus on the most important regions, as shown in figures below. Fig. Original image Fig. The salient part of the original image When we look at the time series chart, the most dominant and stand-out part is the anomalies. This similarity is where we got the inspiration and it turned out to generate great results. Algorithm Our solution then borrowed Spectral Residual (SR) from the visual saliency detection domain, then apply CNN on the results produced by the SR model As you can see from the algorithm architecture, after SR transformation, the transformed result magnifies the anomalies and the resulting signal is easier to generalize, therefore it provides us a way to training CNN with synthetic data. Spectral Residual The spectral residual algorithm consists of three major steps: Fourier Transform to get the log amplitude spectrum Calculation of spectral residual Inverse Fourier Transform that transforms the sequence back to the spatial domain Benefits SR is unsupervised, efficient, and has good generality. The problem becomes much easier based on the output of the SR model. We can train CNN on the SR output using fully synthetic data with simple synthetic rule Randomly select several points in the saliency map and calculate the injection value to replace the original point. Result We have performed online and offline experimentation, it outperformed state-of-the-arts consistently on open datasets and internal production datasets.
