Give your Foundry Agent Custom Tools with MCP Servers on Azure Functions
This blog post is for developers who have an MCP server deployed to Azure Functions and want to connect it to Microsoft Foundry agents. It walks through why you'd want to do this, the different authentication options available, and how to get your agent calling your MCP tools. Connect your MCP server on Azure Functions to Foundry Agent If you've been following along with this blog series, you know that Azure Functions is a great place to host remote MCP servers. You get scalable infrastructure, built-in auth, and serverless billing. All the good stuff. But hosting an MCP server is only half the picture. The real value comes when something actually uses those tools. Microsoft Foundry lets you build AI agents that can reason, plan, and take actions. By connecting your MCP server to an agent, you're giving it access to your custom tools, whether that's querying a database, calling an API, or running some business logic. The agent discovers your tools, decides when to call them, and uses the results to respond to the user. Why connect MCP servers to Foundry agents? You might already have an MCP server that works great with VS Code, VS, Cursor, or other MCP clients. Connecting that same server to a Foundry agent means you can reuse those tools in a completely different context, i.e. in an enterprise AI agent that your team or customers interact with. No need to rebuild anything. Your MCP server stays the same; you're just adding another consumer. Prerequisites Before proceeding, make sure you have the following: 1. An MCP server deployed to Azure Functions. If you don't have one yet, you can deploy one quickly by following one of the samples: Python TypeScript .NET 2. A Foundry project with a deployed model and a Foundry agent Authentication options Depending on where you are in development, you can pick what makes sense and upgrade later. Here's a summary: Method Description When to use Key-based (default) Agent authenticates by passing a shared function access key in the request header. This method is the default authentication for HTTP endpoints in Functions. Development, or when Entra auth isn't required. Microsoft Entra Agent authenticates using either its own identity (agent identity) or the shared identity of the Foundry project (project managed identity). Use agent identity for production scenarios, but limit shared identity to development. OAuth identity passthrough Agent prompts users to sign in and authorize access, using the provided token to authenticate. Production, when each user must authenticate individually. Unauthenticated Agent makes unauthenticated calls. Development only, or tools that access only public information. Connect your MCP server to your Foundry agent If your server uses key-based auth or is unauthenticated, it should be relatively straightforward to set up the connection from a Foundry agent. The Microsoft Entra and OAuth identity passthrough are options that require extra steps to set up. Check out detailed step-by-step instructions for each authentication method. At a high level, the process looks like this: Enable built-in MCP authentication : When you deploy a server to Azure Functions, key-based auth is the default. You'll need to disable that and enable built-in MCP auth instead. If you deployed one of the sample servers in the Prerequisite section, this step is already done for you. Get your MCP server endpoint URL: For MCP extension-based servers, it's https://<FUNCTION_APP_NAME>.azurewebsites.net/runtime/webhooks/mcp Get your credentials based on your chosen auth method: a managed identity configuration, OAuth credentials Add the MCP server as a tool in the Foundry portal by navigating to your agent, adding a new MCP tool, and providing the endpoint and credentials. Microsoft Entra connection required fields OAuth Identity required fields Once the server is configured as a tool, test it in the Agent Builder playground by sending a prompt that triggers one of your MCP tools. Closing thoughts What I find exciting about this is the composability. You build your MCP server once and it works everywhere: VS Code, VS, Cursor, ChatGPT, and now Foundry agents. The MCP protocol is becoming the universal interface for tool use in AI, and Azure Functions makes it easy to host these servers at scale and with security. Are you building agents with Foundry? Have you connected your MCP servers to other clients? I'd love to hear what tools you're exposing and how you're using them. Share with us your thoughts! What's next In the next blog post, we'll go deeper into other MCP topics and cover new MCP features and developments in Azure Functions. Stay tuned!
MCP Apps on Azure Functions: Quick Start with TypeScript
Azure Functions makes hosting MCP apps simple: build locally, create a secure endpoint, and deploy fast with Azure Developer CLI (azd). This guide shows you how using a weather app example. What Are MCP Apps? MCP Apps let MCP servers return interactive HTML interfaces such as data visualizations, forms, dashboards that render directly inside MCP-compatible hosts (Visual Studio Code Copilot, Claude, ChatGPT, etc.). Learn more about MCP Apps in the official documentation. Having an interactive UI removes many restrictions that plain texts have, such as if your scenario has: Interactive Data: Replacing lists with clickable maps or charts for deep exploration. Complex Setup: Use one-page forms instead of long, back-and-forth questioning. Rich Media: Embed native viewers to pan, zoom, or rotate 3D models and documents. Live Updates: Maintain real-time dashboards that refresh without new prompts. Workflow Management: Handle multi-step tasks like approvals with navigation buttons and persistent state. MCP App Hosting as a Feature Azure Functions provides an easy abstraction to help you build MCP servers without having to learn the nitty-gritty of the MCP protocol. When hosting your MCP App on Functions, you get: MCP tools (server logic): Handle client requests, call backend services, return structured data - Azure Functions manages the MCP protocol details for you MCP resources (UI payloads such as app widgets): Serve interactive HTML, JSON documents, or formatted content - just focus on your UI logic Secure HTTPS access: Built-in authentication using Azure Functions keys, plus built-in MCP authentication with OAuth support for enterprise-grade security Easy deployment with Bicep and azd: Infrastructure as Code for reliable deployments Local development: Test and debug locally before deploying Auto-scaling: Azure Functions handles scaling, retries, and monitoring automatically The weather app in this repo is an example of this feature, not the only use case. Architecture Overview Example: The classic Weather App The sample implementation includes: A GetWeather MCP tool that fetches weather by location (calls Open-Meteo geocoding and forecast APIs) A Weather Widget MCP resource that serves interactive HTML/JS code (runs in the client; fetches data via GetWeather tool) A TypeScript service layer that abstracts API calls and data transformation (runs on the server) Bidirectional communication: client-side UI calls server-side tools, receives data, renders locally Local and remote testing flow for MCP clients (via MCP Inspector, VS Code, or custom clients) How UI Rendering Works in MCP Apps In the Weather App example: Azure Functions serves getWeatherWidget as a resource → returns weather-app.ts compiled to HTML/JS Client renders the Weather Widget UI User interacts with the widget or requests are made internally The widget calls the getWeather tool → server processes and returns weather data The widget renders the weather data on the client side This architecture keeps the UI responsive locally while using server-side logic and data on demand. Quick Start Checkout repository: https://github.com/Azure-Samples/remote-mcp-functions-typescript Run locally: npm install npm run build func start Local endpoint: http://0.0.0.0:7071/runtime/webhooks/mcp Deploy to Azure: azd provision azd deploy Remote endpoint: https://.azurewebsites.net/runtime/webhooks/mcp TypeScript MCP Tools Snippet (Get Weather service) In Azure Functions, you define MCP tools using app.mcpTool(). The toolName and description tell clients what this tool does, toolProperties defines the input arguments (like location as a string), and handler points to your function that processes the request. app.mcpTool("getWeather", { toolName: "GetWeather", description: "Returns current weather for a location via Open-Meteo.", toolProperties: { location: arg.string().describe("City name to check weather for") }, handler: getWeather, }); Resource Trigger Snippet (Weather App Hook) MCP resources are defined using app.mcpResource(). The uri is how clients reference this resource, resourceName and description provide metadata, mimeType tells clients what type of content to expect, and handler is your function that returns the actual content (like HTML for a widget). app.mcpResource("getWeatherWidget", { uri: "ui://weather/index.html", resourceName: "Weather Widget", description: "Interactive weather display for MCP Apps", mimeType: "text/html;profile=mcp-app", handler: getWeatherWidget, }); Sample repos and references Complete sample repository with TypeScript implementation: https://github.com/Azure-Samples/remote-mcp-functions-typescript Official MCP extension documentation: https://learn.microsoft.com/azure/azure-functions/functions-bindings-mcp?pivots=programming-language-typescript Java sample: https://github.com/Azure-Samples/remote-mcp-functions-java .NET sample: https://github.com/Azure-Samples/remote-mcp-functions-dotnet Python sample: https://github.com/Azure-Samples/remote-mcp-functions-python MCP Inspector: https://github.com/modelcontextprotocol/inspector Final Takeaway MCP Apps are just MCP servers but they represent a paradigm shift by transforming the AI from a text-based chatbot into a functional interface. Instead of forcing users to navigate complex tasks through back-and-forth conversations, these apps embed interactive UIs and tools directly into the chat, significantly improving the user experience and the usefulness of MCP servers. Azure Functions allows developers to quickly build and host an MCP app by providing an easy abstraction and deployment experience. The platform also provides built-in features to secure and scale your MCP apps, plus a serverless pricing model so you can just focus on the business logic.Azure Functions Ignite 2025 Update
Azure Functions is redefining event-driven applications and high-scale APIs in 2025, accelerating innovation for developers building the next generation of intelligent, resilient, and scalable workloads. This year, our focus has been on empowering AI and agentic scenarios: remote MCP server hosting, bulletproofing agents with Durable Functions, and first-class support for critical technologies like OpenTelemetry, .NET 10 and Aspire. With major advances in serverless Flex Consumption, enhanced performance, security, and deployment fundamentals across Elastic Premium and Flex, Azure Functions is the platform of choice for building modern, enterprise-grade solutions. Remote MCP Model Context Protocol (MCP) has taken the world by storm, offering an agent a mechanism to discover and work deeply with the capabilities and context of tools. When you want to expose MCP/tools to your enterprise or the world securely, we recommend you think deeply about building remote MCP servers that are designed to run securely at scale. Azure Functions is uniquely optimized to run your MCP servers at scale, offering serverless and highly scalable features of Flex Consumption plan, plus two flexible programming model options discussed below. All come together using the hardened Functions service plus new authentication modes for Entra and OAuth using Built-in authentication. Remote MCP Triggers and Bindings Extension GA Back in April, we shared a new extension that allows you to author MCP servers using functions with the MCP tool trigger. That MCP extension is now generally available, with support for C#(.NET), Java, JavaScript (Node.js), Python, and Typescript (Node.js). The MCP tool trigger allows you to focus on what matters most: the logic of the tool you want to expose to agents. Functions will take care of all the protocol and server logistics, with the ability to scale out to support as many sessions as you want to throw at it. [Function(nameof(GetSnippet))] public object GetSnippet( [McpToolTrigger(GetSnippetToolName, GetSnippetToolDescription)] ToolInvocationContext context, [BlobInput(BlobPath)] string snippetContent ) { return snippetContent; } New: Self-hosted MCP Server (Preview) If you’ve built servers with official MCP SDKs and want to run them as remote cloud‑scale servers without re‑writing any code, this public preview is for you. You can now self‑host your MCP server on Azure Functions—keep your existing Python, TypeScript, .NET, or Java code and get rapid 0 to N scaling, built-in server authentication and authorization, consumption-based billing, and more from the underlying Azure Functions service. This feature complements the Azure Functions MCP extension for building MCP servers using the Functions programming model (triggers & bindings). Pick the path that fits your scenario—build with the extension or standard MCP SDKs. Either way you benefit from the same scalable, secure, and serverless platform. Use the official MCP SDKs: # MCP.tool() async def get_alerts(state: str) -> str: """Get weather alerts for a US state. Args: state: Two-letter US state code (e.g. CA, NY) """ url = f"{NWS_API_BASE}/alerts/active/area/{state}" data = await make_nws_request(url) if not data or "features" not in data: return "Unable to fetch alerts or no alerts found." if not data["features"]: return "No active alerts for this state." alerts = [format_alert(feature) for feature in data["features"]] return "\n---\n".join(alerts) Use Azure Functions Flex Consumption Plan's serverless compute using Custom Handlers in host.json: { "version": "2.0", "configurationProfile": "mcp-custom-handler", "customHandler": { "description": { "defaultExecutablePath": "python", "arguments": ["weather.py"] }, "http": { "DefaultAuthorizationLevel": "anonymous" }, "port": "8000" } } Learn more about MCPTrigger and self-hosted MCP servers at https://aka.ms/remote-mcp Built-in MCP server authorization (Preview) The built-in authentication and authorization feature can now be used for MCP server authorization, using a new preview option. You can quickly define identity-based access control for your MCP servers with Microsoft Entra ID or other OpenID Connect providers. Learn more at https://aka.ms/functions-mcp-server-authorization. Better together with Foundry agents Microsoft Foundry is the starting point for building intelligent agents, and Azure Functions is the natural next step for extending those agents with remote MCP tools. Running your tools on Functions gives you clean separation of concerns, reuse across multiple agents, and strong security isolation. And with built-in authorization, Functions enables enterprise-ready authentication patterns, from calling downstream services with the agent’s identity to operating on behalf of end users with their delegated permissions. Build your first remote MCP server and connect it to your Foundry agent at https://aka.ms/foundry-functions-mcp-tutorial. Agents Microsoft Agent Framework 2.0 (Public Preview Refresh) We’re excited about the preview refresh 2.0 release of Microsoft Agent Framework that builds on battle hardened work from Semantic Kernel and AutoGen. Agent Framework is an outstanding solution for building multi-agent orchestrations that are both simple and powerful. Azure Functions is a strong fit to host Agent Framework with the service’s extreme scale, serverless billing, and enterprise grade features like VNET networking and built-in auth. Durable Task Extension for Microsoft Agent Framework (Preview) The durable task extension for Microsoft Agent Framework transforms how you build production-ready, resilient and scalable AI agents by bringing the proven durable execution (survives crashes and restarts) and distributed execution (runs across multiple instances) capabilities of Azure Durable Functions directly into the Microsoft Agent Framework. Combined with Azure Functions for hosting and event-driven execution, you can now deploy stateful, resilient AI agents that automatically handle session management, failure recovery, and scaling, freeing you to focus entirely on your agent logic. Key features of the durable task extension include: Serverless Hosting: Deploy agents on Azure Functions with auto-scaling from thousands of instances to zero, while retaining full control in a serverless architecture. Automatic Session Management: Agents maintain persistent sessions with full conversation context that survives process crashes, restarts, and distributed execution across instances Deterministic Multi-Agent Orchestrations: Coordinate specialized durable agents with predictable, repeatable, code-driven execution patterns Human-in-the-Loop with Serverless Cost Savings: Pause for human input without consuming compute resources or incurring costs Built-in Observability with Durable Task Scheduler: Deep visibility into agent operations and orchestrations through the Durable Task Scheduler UI dashboard Create a durable agent: endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4o-mini") # Create an AI agent following the standard Microsoft Agent Framework pattern agent = AzureOpenAIChatClient( endpoint=endpoint, deployment_name=deployment_name, credential=AzureCliCredential() ).create_agent( instructions="""You are a professional content writer who creates engaging, well-structured documents for any given topic. When given a topic, you will: 1. Research the topic using the web search tool 2. Generate an outline for the document 3. Write a compelling document with proper formatting 4. Include relevant examples and citations""", name="DocumentPublisher", tools=[ AIFunctionFactory.Create(search_web), AIFunctionFactory.Create(generate_outline) ] ) # Configure the function app to host the agent with durable session management app = AgentFunctionApp(agents=[agent]) app.run() Durable Task Scheduler dashboard for agent and agent workflow observability and debugging For more information on the durable task extension for Agent Framework, see the announcement: https://aka.ms/durable-extension-for-af-blog. Flex Consumption Updates As you know, Flex Consumption means serverless without compromise. It combines elastic scale and pay‑for‑what‑you‑use pricing with the controls you expect: per‑instance concurrency, longer executions, VNet/private networking, and Always Ready instances to minimize cold starts. Since launching GA at Ignite 2024 last year, Flex Consumption has had tremendous growth with over 1.5 billion function executions per day and nearly 40 thousand apps. Here’s what’s new for Ignite 2025: 512 MB instance size (GA). Right‑size lighter workloads, scale farther within default quota. Availability Zones (GA). Distribute instances across zones. Rolling updates (Public Preview). Unlock zero-downtime deployments of code or config by setting a single configuration. See below for more information. Even more improvements including: new diagnostic settingsto route logs/metrics, use Key Vault App Config references, new regions, and Custom Handler support. To get started, review Flex Consumption samples, or dive into the documentation to see how Flex can support your workloads. Migrating to Azure Functions Flex Consumption Migrating to Flex Consumption is simple with our step-by-step guides and agentic tools. Move your Azure Functions apps or AWS Lambda workloads, update your code and configuration, and take advantage of new automation tools. With Linux Consumption retiring, now is the time to switch. For more information, see: Migrate Consumption plan apps to the Flex Consumption plan Migrate AWS Lambda workloads to Azure Functions Durable Functions Durable Functions introduces powerful new features to help you build resilient, production-ready workflows: Distributed Tracing: lets you track requests across components and systems, giving you deep visibility into orchestration and activities with support for App Insights and OpenTelemetry. Extended Sessions support in .NET isolated: improves performance by caching orchestrations in memory, ideal for fast sequential activities and large fan-out/fan-in patterns. Orchestration versioning (public preview): enables zero-downtime deployments and backward compatibility, so you can safely roll out changes without disrupting in-flight workflows Durable Task Scheduler Updates Durable Task Scheduler Dedicated SKU (GA): Now generally available, the Dedicated SKU offers advanced orchestration for complex workflows and intelligent apps. It provides predictable pricing for steady workloads, automatic checkpointing, state protection, and advanced monitoring for resilient, reliable execution. Durable Task Scheduler Consumption SKU (Public Preview): The new Consumption SKU brings serverless, pay-as-you-go orchestration to dynamic and variable workloads. It delivers the same orchestration capabilities with flexible billing, making it easy to scale intelligent applications as needed. For more information see: https://aka.ms/dts-ga-blog OpenTelemetry support in GA Azure Functions OpenTelemetry is now generally available, bringing unified, production-ready observability to serverless applications. Developers can now export logs, traces, and metrics using open standards—enabling consistent monitoring and troubleshooting across every workload. Key capabilities include: Unified observability: Standardize logs, traces, and metrics across all your serverless workloads for consistent monitoring and troubleshooting. Vendor-neutral telemetry: Integrate seamlessly with Azure Monitor or any OpenTelemetry-compliant backend, ensuring flexibility and choice. Broad language support: Works with .NET (isolated), Java, JavaScript, Python, PowerShell, and TypeScript. Start using OpenTelemetry in Azure Functions today to unlock standards-based observability for your apps. For step-by-step guidance on enabling OpenTelemetry and configuring exporters for your preferred backend, see the documentation. Deployment with Rolling Updates (Preview) Achieving zero-downtime deployments has never been easier. The Flex Consumption plan now offers rolling updates as a site update strategy. Set a single property, and all future code deployments and configuration changes will be released with zero-downtime. Instead of restarting all instances at once, the platform now drains existing instances in batches while scaling out the latest version to match real-time demand. This ensures uninterrupted in-flight executions and resilient throughput across your HTTP, non-HTTP, and Durable workloads – even during intensive scale-out scenarios. Rolling updates are now in public preview. Learn more at https://aka.ms/functions/rolling-updates. Secure Identity and Networking Everywhere By Design Security and trust are paramount. Azure Functions incorporates proven best practices by design, with full support for managed identity—eliminating secrets and simplifying secure authentication and authorization. Flex Consumption and other plans offer enterprise-grade networking features like VNETs, private endpoints, and NAT gateways for deep protection. The Azure Portal streamlines secure function creation, and updated scenarios and samples showcase these identity and networking capabilities in action. Built-in authentication (discussed above) enables inbound client traffic to use identity as well. Check out our updated Functions Scenarios page with quickstarts or our secure samples gallery to see these identity and networking best practices in action. .NET 10 Azure Functions now supports .NET 10, bringing in a great suite of new features and performance benefits for your code. .NET 10 is supported on the isolated worker model, and it’s available for all plan types except Linux Consumption. As a reminder, support ends for the legacy in-process model on November 10, 2026, and the in-process model is not being updated with .NET 10. To stay supported and take advantage of the latest features, migrate to the isolated worker model. Aspire Aspire is an opinionated stack that simplifies development of distributed applications in the cloud. The Azure Functions integration for Aspire enables you to develop, debug, and orchestrate an Azure Functions .NET project as part of an Aspire solution. Aspire publish directly deploys to your functions to Azure Functions on Azure Container Apps. Aspire 13 includes an updated preview version of the Functions integration that acts as a release candidate with go-live support. The package will be moved to GA quality with Aspire 13.1. Java 25, Node.js 24 Azure Functions now supports Java 25 and Node.js 24 in preview. You can now develop functions using these versions locally and deploy them to Azure Functions plans. Learn how to upgrade your apps to these versions here In Summary Ready to build what’s next? Update your Azure Functions Core Tools today and explore the latest samples and quickstarts to unlock new capabilities for your scenarios. The guided quickstarts run and deploy in under 5 minutes, and incorporate best practices—from architecture to security to deployment. We’ve made it easier than ever to scaffold, deploy, and scale real-world solutions with confidence. The future of intelligent, scalable, and secure applications starts now—jump in and see what you can create!Announcing a flexible, predictable billing model for Azure SRE Agent
Billing for Azure SRE Agent will start on September 1, 2025. Announced at Microsoft Build 2025, Azure SRE Agent is a pre-built AI agent for root cause analysis, uptime improvement, and operational cost reduction. Learn more about the billing model and example scenarios.The Durable Task Scheduler Consumption SKU is now Generally Available
Today, we're excited to announce that the Durable Task Scheduler Consumption SKU has reached General Availability. Developers can now run durable workflows and agents on Azure with pay-per-use pricing, no storage to manage, no capacity to plan, and no idle costs. Just create a scheduler, connect your app, and start orchestrating. Whether you're coordinating AI agent workflows, processing event-driven pipelines, or running background jobs, the Consumption SKU is ready to go. GET STARTED WITH THE DURABLE TASK SCHEDULER CONSUMPTION SKU Since launching the Consumption SKU in public preview last November, we've seen incredible adoption and have incorporated feedback from developers around the world to ensure the GA release is truly production ready. “The Durable Task Scheduler has become a foundational piece of what we call ‘workflows’. It gives us the reliability guarantees we need for processing financial documents and sensitive workflows, while keeping the programming model straightforward. The combination of durable execution, external event correlation, deterministic idempotency, and the local emulator experience has made it a natural fit for our event-driven architecture. We have been delighted with the consumption SKUs cost model for our lower environments.”– Emily Lewis, CarMax What is the Durable Task Scheduler? If you're new to the Durable Task Scheduler, we recommend checking out our previous blog posts for a detailed background: Announcing Limited Early Access of the Durable Task Scheduler Announcing Workflow in Azure Container Apps with the Durable Task Scheduler Announcing Dedicated SKU GA & Consumption SKU Public Preview In brief, the Durable Task Scheduler is a fully managed orchestration backend for durable execution on Azure, meaning your workflows and agent sessions can reliably resume and run to completion, even through process failures, restarts, and scaling events. Whether you’re running workflows or orchestrating durable agents, it handles task scheduling, state persistence, fault tolerance, and built-in monitoring, freeing developers from the operational overhead of managing their own execution engines and storage backends. The Durable Task Scheduler works across Azure compute environments: Azure Functions: Using the Durable Functions extension across all Function App SKUs, including Flex Consumption. Azure Container Apps: Using the Durable Functions or Durable Task SDKs with built-in workflow support and auto-scaling. Any compute: Azure Kubernetes Service, Azure App Service, or any environment where you can run the Durable Task SDKs (.NET, Python, Java, JavaScript). Why choose the Consumption SKU? With the Consumption SKU you’re charged only for actions dispatched, with no minimum commitments or idle costs. There’s no capacity to size or throughput to reserve. Create a scheduler, connect your app, and you’re running. The Consumption SKU is a natural fit for workloads with unpredictable or bursty usage patterns: AI agent orchestration: Multi-step agent workflows that call LLMs, retrieve data, and take actions. Users trigger these on demand, so volume is spiky and pay-per-use avoids idle costs between bursts. Event-driven pipelines: Processing events from queues, webhooks, or streams with reliable orchestration and automatic checkpointing, where volumes spike and dip unpredictably. API-triggered workflows: User signups, form submissions, payment flows, and other request-driven processing where volume varies throughout the day. Distributed transactions: Retries and compensation logic across microservices with durable sagas that survive failures and restarts. What's included in the Consumption SKU at GA The Consumption SKU has been hardened based on feedback and real-world usage during the public preview. Here's what's included at GA: Performance Up to 500 actions per second: Sufficient throughput for a wide range of workloads, with the option to move to the Dedicated SKU for higher-scale scenarios. Up to 30 days of data retention: View and manage orchestration history, debug failures, and audit execution data for up to 30 days. Built-in monitoring dashboard Filter orchestrations by status, drill into execution history, view visual Gantt and sequence charts, and manage orchestrations (pause, resume, terminate, or raise events), all from the dashboard, secured with Role-Based Access Control (RBAC). Identity-based security The Consumption SKU uses Entra ID for authentication and RBAC for authorization. No SAS tokens or access keys to manage, just assign the appropriate role and connect. Get started with the Durable Task Scheduler today The Consumption SKU is available now Generally Available. Provision a scheduler in the Azure portal, connect your app, and start orchestrating. You only pay for what you use. Documentation Getting started Samples Pricing Consumption SKU docs We'd love to hear your feedback. Reach out to us by filing an issue on our GitHub repository
Introducing Skills in Azure API Center
The problem Modern applications depend on more than APIs. A single AI workflow might call an LLM, invoke an MCP tool, integrate a third-party service, and reference a business capability spanning dozens of endpoints. Without a central inventory, these assets become impossible to discover, easy to duplicate, and painful to govern. Azure API Center — part of the Azure API Management platform — already catalogs models, agents, and MCP servers alongside traditional APIs. Skills extend that foundation to cover reusable AI capabilities. What is a Skill? As AI agents become more capable, organizations need a way to define and govern what those agents can actually do. Skills are the answer. A Skill in Azure API Center is a reusable, registered capability that AI agents can discover and consume to extend their functionality. Each skill is backed by source code — typically hosted in a Git repository — and describes what it does, what APIs or MCP servers it can access, and who owns it. Think of skills as the building blocks of AI agent behavior, promoted into a governed inventory alongside your APIs, MCP servers, models, and agents. Example: A "Code Review Skill" performs automated code reviews using static analysis. It is registered in API Center with a Source URL pointing to its GitHub repo, allowed to access your code analysis API, and discoverable by any AI agent in your organization. How Skills work in API Center Skills can be added to your inventory in two ways: registered manually through the Azure portal, or synchronized automatically from a connected Git repository. Both approaches end up in the same governed catalog, discoverable through the API Center portal. Option 1: Register a Skill manually Use the Azure portal to register a skill directly. Navigate to Inventory > Assets in your API center, select + Register an asset > Skill, and fill in the registration form. Figure 2: Register a skill form in the Azure portal. The form captures everything needed to make a skill discoverable and governable: Field Description Title Display name for the skill (e.g. Code Review Skill). Identification Auto-generated URL slug based on the title. Editable. Summary One-line description of what the skill does. Description Full detail on capabilities, use cases, and expected behavior. Lifecycle stage Current state: Design, Preview, Production, or Deprecated. Source URL Git repository URL for the skill source code. Allowed tools The APIs or MCP servers from your inventory this skill is permitted to access. Enforces governance at the capability level. License Licensing terms: MIT, Apache 2.0, Proprietary, etc. Contact information Owner or support contact for the skill. Governance note: The Allowed tools field is key for AI governance. It explicitly defines which APIs and MCP servers a skill can invoke — preventing uncontrolled access and making security review straightforward. Option 2: Sync Skills from a Git repository For teams managing skills in source control, API Center can integrate directly with a Git repository and synchronize skill information automatically. This is the recommended approach for teams practicing GitOps or managing many skills at scale. Figure 3: Integrating a Git repository to sync skills automatically into API Center. When you configure a Git integration, API Center: Creates an Environment representing the repository as a source of skills Scans for files matching the configured pattern (default: **/skill.md) Syncs matching skills into your inventory and keeps them current as the repo changes For private repositories, a Personal Access Token (PAT) stored in Azure Key Vault is used for authentication. API Center's managed identity retrieves the PAT securely — no credentials are stored in the service itself. Tip: Use the Automatically configure managed identity and assign permissions option when setting up the integration if you haven't pre-configured a managed identity. API Center handles the Key Vault permissions automatically. Discovering Skills in your catalog Once registered — manually or via Git — skills appear in the Inventory > Assets page alongside all other asset types. Linked skills (synced from Git) are visually identified with a link icon, so teams can see at a glance which skills are source-controlled. From the API Center portal, developers and other stakeholders can browse the full skill catalog, filter by lifecycle stage or type, and view detailed information about each skill — including its source URL, allowed tools, and contact information. Figure 4: Skills catalog in API Center portal, showing registered skills and the details related to the skill. Developer experience: The API Center portal gives teams a self-service way to discover approved skills without needing to ask around or search GitHub. The catalog becomes the authoritative source of what's available and what's allowed. Why this matters for AI development teams Skills close a critical gap in AI governance. As organizations deploy AI agents, they need to know — and control — what those agents can do. Without a governed skill registry, capability discovery is ad hoc, reuse is low, and security review is difficult. By bringing skills into Azure API Center alongside APIs, MCP servers, models, and agents, teams get: A single inventory for all the assets AI agents depend on Explicit governance over which resources each skill can access via Allowed tools Automated, source-controlled skill registration via Git integration Discoverability for developers and AI systems through the API Center portal Consistent lifecycle management — Design through Production to Deprecated API Center, as part of the Azure API Management platform and the broader AI Gateway vision, is evolving into the system of record for AI-ready development. Skills are the latest step in that direction. Available now Skills are available today in Azure API Center (preview). To register your first skill: Sign in to the Azure portal and navigate to your API Center instance In the sidebar, select Inventory > Assets Select + Register an asset > Skill Fill in the registration form and select Create → Register and discover skills in Azure API Center (docs) → Set up your API Center portal → Explore the Azure API Management platformBuilding the agentic future together at JDConf 2026
JDConf 2026 is just weeks away, and I’m excited to welcome Java developers, architects, and engineering leaders from around the world for two days of learning and connection. Now in its sixth year, JDConf has become a place where the Java community compares notes on their real-world production experience: patterns, tooling, and hard-earned lessons you can take back to your team, while we keep moving the Java systems that run businesses and services forward in the AI era. This year’s program lines up with a shift many of us are seeing first-hand: delivery is getting more intelligent, more automated, and more tightly coupled to the systems and data we already own. Agentic approaches are moving from demos to backlog items, and that raises practical questions: what’s the right architecture, where do you draw trust boundaries, how do you keep secrets safe, and how do you ship without trading reliability for novelty? JDConf is for and by the people who build and manage the mission-critical apps powering organizations worldwide. Across three regional livestreams, you’ll hear from open source and enterprise practitioners who are making the same tradeoffs you are—velocity vs. safety, modernization vs. continuity, experimentation vs. operational excellence. Expect sessions that go beyond “what” and get into “how”: design choices, integration patterns, migration steps, and the guardrails that make AI features safe to run in production. You’ll find several practical themes for shipping Java in the AI era: connecting agents to enterprise systems with clear governance; frameworks and runtimes adapting to AI-native workloads; and how testing and delivery pipelines evolve as automation gets more capable. To make this more concrete, a sampling of sessions would include topics like Secrets of Agentic Memory Management (patterns for short- and long-term memory and safe retrieval), Modernizing a Java App with GitHub Copilot (end-to-end upgrade and migration with AI-powered technologies), and Docker Sandboxes for AI Agents (guardrails for running agent workflows without risking your filesystem or secrets). The goal is to help you adopt what’s new while hardening your long lived codebases. JDConf is built for community learning—free to attend, accessible worldwide, and designed for an interactive live experience in three time zones. You’ll not only get 23 practitioner-led sessions with production-ready guidance but also free on-demand access after the event to re-watch with your whole team. Pro tip: join live and get more value by discussing practical implications and ideas with your peers in the chat. This is where the “how” details and tradeoffs become clearer. JDConf 2026 Keynote Building the Agentic Future Together Rod Johnson, Embabel | Bruno Borges, Microsoft | Ayan Gupta, Microsoft The JDConf 2026 keynote features Rod Johnson, creator of the Spring Framework and founder of Embabel, joined by Bruno Borges and Ayan Gupta to explore where the Java ecosystem is headed in the agentic era. Expect a practitioner-level discussion on how frameworks like Spring continue to evolve, how MCP is changing the way agents interact with enterprise systems, and what Java developers should be paying attention to right now. Register. Attend. Earn. Register for JDConf 2026 to earn Microsoft Rewards points, which you can use for gift cards, sweepstakes entries, and more. Earn 1,000 points simply by signing up. When you register for any regional JDConf 2026 event with your Microsoft account, you'll automatically receive these points. Get 5,000 additional points for attending live (limited to the first 300 attendees per stream). On the day of your regional event, check in through the Reactor page or your email confirmation link to qualify. Disclaimer: Points are added to your Microsoft account within 60 days after the event. Must register with a Microsoft account email. Up to 10,000 developers eligible. Points will be applied upon registration and attendance and will not be counted multiple times for registering or attending at different events. Terms | Privacy JDConf 2026 Regional Live Streams Americas – April 8, 8:30 AM – 12:30 PM PDT (UTC -7) Bruno Borges hosts the Americas stream, discussing practical agentic Java topics like memory management, multi-agent system design, LLM integration, modernization with AI, and dependency security. Experts from Redis, IBM, Hammerspace, HeroDevs, AI Collective, Tekskills, and Microsoft share their insights. Register for Americas → Asia-Pacific – April 9, 10:00 AM – 2:00 PM SGT (UTC +8) Brian Benz and Ayan Gupta co-host the APAC stream, highlighting Java frameworks and practices for agentic delivery. Topics include Spring AI, multi-agent orchestration, spec-driven development, scalable DevOps, and legacy modernization, with speakers from Broadcom, Alibaba, CERN, MHP (A Porsche Company), and Microsoft. Register for Asia-Pacific → Europe, Middle East and Africa – April 9, 9:00 AM – 12:30 PM GMT (UTC +0) The EMEA stream, hosted by Sandra Ahlgrimm, will address the implementation of agentic Java in production environments. Topics include self-improving systems utilizing Spring AI, Docker sandboxes for agent workflow management, Retrieval-Augmented Generation (RAG) pipelines, modernization initiatives from a national tax authority, and AI-driven CI/CD enhancements. Presentations will feature experts from Broadcom, Docker, Elastic, Azul Systems, IBM, Team Rockstars IT, and Microsoft. Register for EMEA → Make It Interactive: Join Live Come prepared with an actual challenge you’re facing, whether you’re modernizing a legacy application, connecting agents to internal APIs, or refining CI/CD processes. Test your strategies by participating in live chats and Q&As with presenters and fellow professionals. If you’re attending with your team, schedule a debrief after the live stream to discuss how to quickly use key takeaways and insights in your pilots and projects. Learning Resources Java and AI for Beginners Video Series: Practical, episode-based walkthroughs on MCP, GenAI integration, and building AI-powered apps from scratch. Modernize Java Apps Guide: Step-by-step guide using GitHub Copilot agent mode for legacy Java project upgrades, automated fixes, and cloud-ready migrations. AI Agents for Java Webinar: Embedding AI Agent capabilities into Java applications using Microsoft Foundry, from project setup to production deployment. Java Practitioner’s Guide: Learning plan for deploying, managing, and optimizing Java applications on Azure using modern cloud-native approaches. Register Now JDConf 2026 is a free global event for Java teams. Join live to ask questions, connect, and gain practical patterns. All 23 sessions will be available on-demand. Register now to earn Microsoft Rewards points for attending. Register at JDConf.comTake Control of Every Message: Partial Failure Handling for Service Bus Triggers in Azure Functions
The Problem: All-or-Nothing Batch Processing in Azure Service Bus Azure Service Bus is one of the most widely used messaging services for building event-driven applications on Azure. When you use Azure Functions with a Service Bus trigger in batch mode, your function receives multiple messages at once for efficient, high-throughput processing. But what happens when one message in the batch fails? Your function receives a batch of 50 Service Bus messages. 49 process perfectly. 1 fails. What happens? In the default model, the entire batch fails. All 50 messages go back on the queue and get reprocessed, including the 49 that already succeeded. This leads to: Duplicate processing — messages that were already handled successfully get processed again Wasted compute — you pay for re-executing work that already completed Infinite retry loops — if that one "poison" message keeps failing, it blocks the entire batch indefinitely Idempotency burden — your downstream systems must handle duplicates gracefully, adding complexity to every consumer This is the classic all-or-nothing batch failure problem. Azure Functions solves it with per-message settlement. The Solution: Per-Message Settlement for Azure Service Bus Azure Functions gives you direct control over how each individual message is settled in real time, as you process it. Instead of treating the batch as all-or-nothing, you settle each message independently based on its processing outcome. With Service Bus message settlement actions in Azure Functions, you can: Action What It Does Complete Remove the message from the queue (successfully processed) Abandon Release the lock so the message returns to the queue for retry, optionally modifying application properties Dead-letter Move the message to the dead-letter queue (poison message handling) Defer Keep the message in the queue but make it only retrievable by sequence number This means in a batch of 50 messages, you can: Complete 47 that processed successfully Abandon 2 that hit a transient error (with updated retry metadata) Dead-letter 1 that is malformed and will never succeed All in a single function invocation. No reprocessing of successful messages. No building failure response objects. No all-or-nothing. Why This Matters 1. Eliminates Duplicate Processing When you complete messages individually, successfully processed messages are immediately removed from the queue. There's no chance of them being redelivered, even if other messages in the same batch fail. 2. Enables Granular Error Handling Different failures deserve different treatments. A malformed message should be dead-lettered immediately. A message that failed due to a transient database timeout should be abandoned for retry. A message that requires manual intervention should be deferred. Per-message settlement gives you this granularity. 3. Implements Exponential Backoff Without External Infrastructure By combining abandon with modified application properties, you can track retry counts per message and implement exponential backoff patterns directly in your function code, no additional queues or Durable Functions required. 4. Reduces Cost You stop paying for redundant re-execution of already-successful work. In high-throughput systems processing millions of messages, this can be a material cost reduction. 5. Simplifies Idempotency Requirements When successful messages are never redelivered, your downstream systems don't need to guard against duplicates as aggressively. This reduces architectural complexity and potential for bugs. Before: One Message = One Function Invocation Before batch support, there was no cardinality option, Azure Functions processed each Service Bus message as a separate function invocation. If your queue had 50 messages, the runtime spun up 50 individual executions. Single-Message Processing (The Old Way) import { app, InvocationContext } from '@azure/functions'; async function processOrder( message: unknown, // ← One message at a time, no batch context: InvocationContext ): Promise<void> { try { const order = message as Order; await processOrder(order); } catch (error) { context.error('Failed to process message:', error); // Message auto-complete by default. throw error; } } app.serviceBusQueue('processOrder', { connection: 'ServiceBusConnection', queueName: 'orders-queue', handler: processOrder, }); What this cost you: 50 messages on the queue Old (single-message) New (batch + settlement) Function invocations 50 separate invocations 1 invocation Connection overhead 50 separate DB/API connections 1 connection, reused across batch Compute cost 50× invocation overhead 1× invocation overhead Settlement control Binary: throw or don't 4 actions per message Every message paid the full price of a function invocation, startup, connection setup, teardown. At scale (millions of messages/day), this was a significant cost and latency penalty. And when a message failed, your only option was to throw (retry the whole message) or swallow the error (lose it silently). Code Examples Let's see how this looks across all three major Azure Functions language stacks. Node.js (TypeScript with @ azure/functions-extensions-servicebus) import '@azure/functions-extensions-servicebus'; import { app, InvocationContext } from '@azure/functions'; import { ServiceBusMessageContext, messageBodyAsJson } from '@azure/functions-extensions-servicebus'; interface Order { id: string; product: string; amount: number; } export async function processOrderBatch( sbContext: ServiceBusMessageContext, context: InvocationContext ): Promise<void> { const { messages, actions } = sbContext; for (const message of messages) { try { const order = messageBodyAsJson<Order>(message); await processOrder(order); await actions.complete(message); // ✅ Done } catch (error) { context.error(`Failed ${message.messageId}:`, error); await actions.deadletter(message); // ☠️ Poison } } } app.serviceBusQueue('processOrderBatch', { connection: 'ServiceBusConnection', queueName: 'orders-queue', sdkBinding: true, autoCompleteMessages: false, cardinality: 'many', handler: processOrderBatch, }); Key points: Enable sdkBinding: true and autoCompleteMessages: false to gain manual settlement control ServiceBusMessageContext provides both the messages array and actions object Settlement actions: complete(), abandon(), deadletter(), defer() Application properties can be passed to abandon() for retry tracking Built-in helpers like messageBodyAsJson<T>() handle Buffer-to-object parsing Full sample: serviceBusSampleWithComplete Python (V2 Programming Model) import json import logging from typing import List import azure.functions as func import azurefunctions.extensions.bindings.servicebus as servicebus app = func.FunctionApp(http_auth_level=func.AuthLevel.FUNCTION) @app.service_bus_queue_trigger(arg_name="messages", queue_name="orders-queue", connection="SERVICEBUS_CONNECTION", auto_complete_messages=False, cardinality="many") def process_order_batch(messages: List[servicebus.ServiceBusReceivedMessage], message_actions: servicebus.ServiceBusMessageActions): for message in messages: try: order = json.loads(message.body) process_order(order) message_actions.complete(message) # ✅ Done except Exception as e: logging.error(f"Failed {message.message_id}: {e}") message_actions.dead_letter(message) # ☠️ Poison def process_order(order): logging.info(f"Processing order: {order['id']}") Key points: Uses azurefunctions.extensions.bindings.servicebus for SDK-type bindings with ServiceBusReceivedMessage Supports both queue and topic triggers with cardinality="many" for batch processing Each message exposes SDK properties like body, enqueued_time_utc, lock_token, message_id, and sequence_number Full sample: servicebus_samples_settlement .NET (C# Isolated Worker) using Azure.Messaging.ServiceBus; using Microsoft.Azure.Functions.Worker; public class ServiceBusBatchProcessor(ILogger<ServiceBusBatchProcessor> logger) { [Function(nameof(ProcessOrderBatch))] public async Task ProcessOrderBatch( [ServiceBusTrigger("orders-queue", Connection = "ServiceBusConnection")] ServiceBusReceivedMessage[] messages, ServiceBusMessageActions messageActions) { foreach (var message in messages) { try { var order = message.Body.ToObjectFromJson<Order>(); await ProcessOrder(order); await messageActions.CompleteMessageAsync(message); // ✅ Done } catch (Exception ex) { logger.LogError(ex, "Failed {MessageId}", message.MessageId); await messageActions.DeadLetterMessageAsync(message); // ☠️ Poison } } } private Task ProcessOrder(Order order) => Task.CompletedTask; } public record Order(string Id, string Product, decimal Amount); Key points: Inject ServiceBusMessageActions directly alongside the message array Each message is individually settled with CompleteMessageAsync, DeadLetterMessageAsync, or AbandonMessageAsync Application properties can be modified on abandon to track retry metadata Full sample: ServiceBusReceivedMessageFunctions.cs
Help wanted: Refresh articles in Azure Architecture Center (AAC)
I’m the Project Manager for architecture review boards (ARBs) in the Azure Architecture Center (AAC). We’re looking for subject matter experts to help us improve the freshness of the AAC, Cloud Adoption Framework (CAF), and Well-Architected Framework (WAF) repos. This opportunity is currently limited to Microsoft employees only. As an ARB member, your main focus is to review, update, and maintain content to meet quarterly freshness targets. Your involvement directly impacts the quality, relevance, and direction of Azure Patterns & Practices content across AAC, CAF, and WAF. The content in these repos reaches almost 900,000 unique readers per month, so your time investment has a big, global impact. The expected commitment is 4-6 hours per month, including attendance at weekly or bi-weekly sync meetings. Become an ARB member to gain: Increased visibility and credibility as a subject‑matter expert by contributing to Microsoft‑authored guidance used by customers and partners worldwide. Broader internal reach and networking without changing roles or teams. Attribution on Microsoft Learn articles that you own. Opportunity to take on expanded roles over time (for example, owning a set of articles, mentoring contributors, or helping shape ARB direction). We’re recruiting new members across several ARBs. Our highest needs are in the Web ARB, Containers ARB, and Data & Analytics ARB: The Web ARB focuses on modern web application architecture on Azure—App Service and PaaS web apps, APIs and API Management, ingress and networking (Application Gateway, Front Door, DNS), security and identity, and designing for reliability, scalability, and disaster recovery. The Containers ARB focuses on containerized and Kubernetes‑based architectures—AKS design and operations, networking and ingress, security and identity, scalability, and reliability for production container platforms. The Data & Analytics ARB focuses on data platform and analytics architectures—data ingestion and integration, analytics and reporting, streaming and real‑time scenarios, data security and governance, and designing scalable, reliable data solutions on Azure. We’re also looking for people to take ownership of other articles across AAC, CAF, and WAF. These articles span many areas, including application and solution architectures, containers and compute, networking and security, governance and observability, data and integration, and reliability and operational best practices. You don’t need to know everything—deep expertise in one or two areas and an interest in keeping Azure architecture guidance accurate and current is what matters most. Please reply to this post if you’re interested in becoming an ARB member, and I’ll follow up with next steps. If you prefer, you can email me at v-jodimartis@microsoft.com. Thanks! 🙂A Practical Path Forward for Heroku Customers with Azure
On February 6, 2026, Heroku announced it is moving to a sustaining engineering model focused on stability, security, reliability, and ongoing support. Many customers are now reassessing how their application platforms will support today’s workloads and future innovation. Microsoft is committed to helping customers migrate and modernize applications from platforms like Heroku to Azure.
