New Storage Providers for Azure Durable Functions
Azure Durable Functions now supports two new backend storage providers for storing durable runtime state, “Netherite” and Microsoft SQL Server (including full support for Azure SQL Database). These new storage options allow you to run at higher scale, with greater price-performance efficiency, and more portability compared to the default Azure Storage configuration. Any of these three storage providers can be configured without making any code changes to your existing apps.Building Durable and Deterministic Multi-Agent Orchestrations with Durable Execution
Durable Execution Durable Execution is a reliable approach to running code, designed to handle failures smoothly with automatic retries and state persistence. It is built on three core principles: Incremental Execution: Each operation runs independently and in order. State Persistence: The output of each step is durably saved to ensure progress is not lost. Fault Tolerance: If a step fails, the operation is retried from the last successful step, skipping previously completed steps. Durable Execution is particularly beneficial for scenarios requiring stateful chaining of operations, such as order-processing applications, data processing pipelines, ETL (extract, transform, load), and as we'll get into in this post, intelligent applications with AI agents. Durable execution simplifies the implementation of complex, long-running, stateful, and fault-tolerant application patterns. Technologies like Durable Functions provide a programming model that makes the implementation of these patterns straightforward. Some common stateful application patterns that require stateful chaining and are easily implemented with durable execution, like Durable Functions include: Durable Task Programming Model Before solutions like Azure Durable Functions, developers had to manually coordinate operations and maintain state using infrastructure like message queues and state stores, adding complexity to the code and increased the operational maintenance burden. Durable Functions streamlines this process by providing a programming model backed by a durable state store, enabling developers to define a series of steps to be executed in a specific order. This is called an orchestrator function. Activity functions within the orchestration function are the "steps," and the durable task runtime ensures each step is scheduled in order and executed on your compute of choice, with outputs persisted. Durable for Orchestrating Agents With the rapid advancements in AI, we are witnessing an increasing trend of scenarios that require orchestration, specifically when it comes to working with multiple AI agents within applications. These agents often work together to accomplish a larger task. Two emerging designs for these applications are deterministic agentic workflows and self-directed agentic workflows: Deterministic Agentic Workflows: Agents work together through a series of predefined steps to accomplish a larger task, leading to a deterministic result. A Deterministic Agentic Workflow orchestrates a series of predefined steps, each calling sub-agents to achieve a deterministic outcome. Self-Directed Agentic Workflows: Agents dynamically explore and determine the workflow plan as they proceed. Each approach fits different business scenarios and requirements. However, as we're learning, many scenarios benefit from deterministic outcomes, and durable execution truly shines in the deterministic agentic workflow pattern. It excels at providing efficient and reliable deterministic outcomes by following a predefined set path that maps to orchestration and activity functions. The programming model makes it extremely easy to call your agents independently and implement common agent app patterns, such as prompt chaining for function chaining and parallelization with fan-out/fan-in. For more on this, please reference this insightful blog post by my colleague Chris Gillum. Self-directed agentic workflows are advantageous for unpredictable, creative tasks where the agents can determine their plan during execution. However, this can be less efficient and lead to non-deterministic outcomes, which may cause undesirable results. When using durable execution for your agent orchestration, it enhances the resiliency of your agentic workflows. If any step fails, there’s no need to start from the beginning. Given that requests to LLMs can be expensive and may yield different outcomes, durable execution ensures that your orchestrations can recover right from their last success point. Let’s look at a specific example of where I used durable execution, specifically Azure Durable Functions to implement a multi-agent application that requires durability – The Travel Planner Assistant. The Travel Planner Assistant Travel planning inherently follows a structured sequence – selecting destinations, crafting itineraries, gathering local insights, and booking the trip. This makes it ideal for an agentic workflow with predefined steps, rather than a self-directed agentic workflow with exploration. The outcome must be deterministic – we want a complete travel itinerary and a fully booked trip. The application exposes a durable function that schedules a predefined agentic workflow (orchestration) to create a travel plan, which will then be used to book the trip. The orchestration interacts with specialized sub-agents for the first three steps. These include: Destination Recommender Agent: Provides global knowledge across thousands of locations. Itinerary Planner Agent: Creates a daily itinerary based on a deep understanding of the specific location’s logistics and seasonal considerations. Local Recommendations Agent: Offers popular attractions to visit. Orchestration Activity Function Calls - Sequential AI agent activities. Each activity is executed as a separate function with its own context. By using Durable Functions to coordinate these specialized agents, the travel planner agent creates a more accurate and comprehensive travel plan than a single generalist agent. Once the travel plan has been created, Durable Function orchestrations provide built-in support for human interaction, allowing human approval of the travel plan before proceeding to book the trip. This can be crucial in some scenario because, despite the advancements in agents and LLMs, there are still critical tasks that require human input. Relying solely on LLM decision-making without review for such important task can be risky, and human approval ensure accuracy and reliability. Seeking this approval can be a long-running operation that may encounter failures along the way. However, by leveraging Durable Functions, the application benefits from resiliency through built-in state persistence, ensuring the orchestration can resume in the event of a failure, such as downstream dependency outage or if the application restarts while waiting for approval. Demo Video emo Video Wrap up For orchestrating agents, I recommend using Durable Execution technologies like Azure Durable Functions, as they offer determinism, reliability, and efficiency. The programming model simplifies the orchestration of agents, ensuring predictable outcomes. It enhances the resiliency of agentic workflows, allowing them to recover seamlessly from their last successful point. To provide evidence of customers using Durable in real-world production applications, take a look at this Toyota case study where they are using Durable Functions for orchestrating their multi-agent application, exactly as outlined above. If you have any questions or thoughts about this, please feel free to comment below. I'd love to hear if you find this interesting or if you're already using durable execution in your agent applications.Cloud-native at Build 2023
Cloud-native development is a paradigm that aims to deliver scalable, resilient, and adaptable applications that can run on any cloud platform. Microsoft's cloud-native products, such as Azure Kubernetes Service, Azure Functions, and Azure DevOps, provide the tools and services to help developers build, deploy, and manage cloud-native applications with ease.
Preview of Durable Functions Extension v3.0.0
We have just released the preview of a new major version of the Durable Functions extension! There are two major changes introduced in this release, including upgrading to the latest version of the Azure Storage SDK (a breaking change for .NET in-process apps) and the introduction of a new partition manager.Azure Functions – Build 2025
Azure Functions – Build 2025 update With Microsoft Build underway, the team is excited to provide an update on the latest releases in Azure Functions this year. Customers are leveraging Azure Functions to build AI solutions, thanks to its serverless capabilities that scale on demand and its native integration for processing real-time data. The newly launched capabilities enable the creation of AI and agentic applications with enhanced offerings, built-in security, and a pay-as-you-go model. Real-time retrieval augmented generation, making organizational data accessible through semantic search Native event driven tool function calling with the AI Foundry Agent service Hosted Model Context Protocol servers. Support for Flex consumption plans, including zone redundancy, increased regions, and larger instance sizes. Enhanced security for applications through managed identity and networking support across all Azure Functions plans. Durable Functions to develop deterministic agentic solutions, providing control over agent processes with built-in state management for automatic retries and complex orchestration patterns, including human approvals. Read more about using durable for agents in this blog. Azure Functions has made significant investments over the past couple of years to simplify the development of secure, scalable, and intelligent applications. Learn more about the scenarios and capabilities in the documentation. Building AI apps General availability announcements Azure Functions integration with Azure AI Foundry Agent Service Integrating Azure Functions with AI Foundry Agent service enables you to build intelligent, event-driven applications that are scalable, secure, and cost-efficient. Azure Functions act as custom tools that AI agents can call to execute business logic, access secure systems, or process data dynamically in response to events like HTTP requests or queue messages. This integration allows for modular AI workflows, where agents can reason through tasks and trigger specific functions as needed—ideal for scenarios like customer support, document processing, or automated insights—without the need to manage infrastructure. Learn more Public preview announcements Remote Model Context Protocol (MCP) Model Context Protocol (MCP) is a way for apps to provide capabilities and context to a large language model. A key feature of MCP is the ability to define tools that AI agents can leverage to accomplish whatever tasks they’ve been given. MCP servers can be run locally, but remote MCP servers are important for sharing tools that work at cloud scale. The preview of triggers and bindings allow you to build tools using remote MCP with server-sent events (SSE) with Azure Functions. Azure Functions lets you author focused, event-driven logic that scales automatically in response to demand. You just write code reflecting unique requirements of your tools, and Functions will take care of the rest. Learn more. Azure OpenAI Trigger and Bindings preview update The Azure OpenAI extension has been updated to support managed identity, the latest OpenAI SDK, support for Azure Cosmos DB for NoSQL as a vector store, and customer feedback improvements. Retrieval Augmented Generation (Bring your own data for semantic search) Data ingestion with Functions bindings. Automatic chunking and embeddings creation. Store embeddings in vector database including AI Search, Cosmos DB for MongoDB, Cosmos DB for NoSQL, and Azure Data Explorer. Binding that takes prompts, retrieves documents, sends to OpenAI LLM, and returns to user. Text completion for content summarization and creation Input binding that takes prompt and returns response from LLM. Chat assistants Input and output binding to chat with LLMs. Output binding to retrieve chat history from persisted storage. Skills trigger that is registered and called by LLM through natural language. Learn more. Flex consumption General availability announcements New regions for Azure Functions Flex consumption Beyond the already generally available regions, you can now create Flex Consumption apps in the following regions: Australia Southeast Brazil South Canada Central Central India Central US France Central Germany West Central Italy North Japan East Korea Central North Central US Norway East South Africa North South India Spain Central UAE North Uk West West Central US West Europe West US Pricing for each region will be available by July 1 st . To learn more, see View Currently Supported regions. Public preview announcements Azure Functions Flex Consumption now supports availability zones and 512 MB instances You can now enable availability zones for your Flex Consumption apps during create or post-create. You can also choose the 512 MB instance memory size. Availability zones are physically separate groups of datacenters within each Azure region. When one zone fails, services can fail over to one of the remaining zones. When availability zones are enabled, instances are distributed across availability zones for increased reliability. Availability zones preview is initially available in the following regions: Australia East Canada Central Central India East Asia Germany West Central Italy North Norway East South Africa North Sweden Central West US 3 UAE North UK South To learn more about availability zones, see reliability in Azure Functions Moreover, Azure Functions now allows you to choose 512 MB in addition to 2048 MB and 4096 MB as the memory instance size for your Flex Consumption apps. This enables you to further cost optimize your apps that require less resources, and allows your apps to scale out further within the default quota. To learn more about instance sizes, see Flex Consumption plan instance memory. Azure Functions on Azure Container Apps General availability announcements We are excited to introduce a new, streamlined method for running Azure Functions directly in Azure Container Apps (ACA). This powerful integration allows you to leverage the full features and capabilities of Azure Container Apps while benefiting from the simplicity of auto-scaling provided by Azure Functions For customers who want to deploy and manage their Azure Functions using the native capabilities of Azure Container Apps, we have recently released the ability to use Azure Functions on Azure Container Apps environment to deploy your multitype services to a cloud-native solution designed for centralized management and serverless scale. Azure Function’s host, runtime, extensions, and Azure Function apps can be developed and deployed as containers using familiar Functions tooling including Core Tools, AzCLI/Portal/code-to-cloud with GitHub actions and DevOps tasks into the Container Apps compute environment. This enables centralized networking, observability, and configuration boundaries for multitype application development when building microservices. Azure Functions on Azure Container Apps can be integrated with DAPR, scaled using KEDA and provisioned to a highly performant serverless plan. This allows you to maximize productivity with a serverless container service built for microservices, robust autoscaling, and fully managed infrastructure. Learn more. Triggers and Bindings General availability announcements Azure SQL trigger for Azure Functions You can now build application logic in azure function apps consumption plan that can scale apps to zero and up driven by the data from Azure SQL database. Azure SQL trigger for Azure Functions allows you to use nearly any SQL database enabled with change tracking to develop and scale event-driven applications using Azure Functions. Invoking an Azure Function from changes to an Azure SQL table is now possible through the Azure SQL trigger for Azure Functions in all plans for Azure Functions supported languages. Azure SQL trigger for Azure Functions enables you, with nearly any SQL database enabled with change tracking, to develop and scale event-driven applications using Azure Functions. The Azure SQL trigger is compatible with Azure SQL Database, Azure SQL Managed Instance, and SQL Server and can be developed with all Azure Functions supported languages for all plans. With input and output bindings for SQL already in GA, you can quickly write Azure Functions that read and write from your databases. Together with triggers and input/output bindings, the SQL extension for Azure Functions provides you improved efficiency with low-code/no-code database interactions and enables those who are looking to migrate their applications to Azure the ability to participate in modern architectures. Learn more. Bind to Blob Storage types from the Azure SDK for Python Azure Functions triggers and bindings enable you to easily integrate event and data sources with function applications. This feature enables you to use types from service SDKs and frameworks, providing more capability beyond what is currently offered. Specifically, SDK type bindings for Azure Storage Blob enable the following key scenarios: Downloading and uploading blobs of large sizes, reducing current memory limitations and GRPC limits. Improved performance by using blobs with Azure Functions To learn more, see SDK type bindings for Azure Blob Storage in Python Azure Functions support for HTTP streams in Python Azure Functions support for HTTP streams in Python is now GA. With this feature, customers can stream HTTP requests to and responses from their Function Apps, using function exposed FastAPI request and response APIs. Previously with HTTP requests, the amount of data that could be transmitted was limited at the SKU instance memory size. With HTTP streaming, large amounts of data can be processed with chunking. This feature enables new scenarios including processing large data streaming OpenAI responses and delivering dynamic content. You can leverage this feature for use cases where real time exchange and interaction between client and server over HTTP connections is needed. Additionally, FastAPI response types are supported with this feature. To learn more, see HTTP streams in Azure Functions using Python. Public preview announcements Bind to types in Azure Functions for Azure Service Bus, Azure Cosmos DB and Azure Event Hubs in Python Azure Functions triggers and bindings enable you to easily integrate event and data sources with function applications. This feature enables you to use types from service SDKs and frameworks, providing more capability beyond what is currently offered. Azure Service Bus: You can now interact with the ServiceBusReceivedMessage type from the SDK, offering more advanced functionality compared to the previous ServiceBusMessage type. To learn more, see SDK type bindings for Service Bus in Python. Azure Cosmos DB: SDK type bindings for Azure Cosmos DB enable the following key scenarios: Interacting with Cosmos DB instances seamlessly (databases, containers, and documents), reducing current memory limitations and GRPC limits. Improved performance by using Cosmos DB with Azure Functions To learn more, see SDK type bindings for Cosmos DB in Python. Azure Event Hubs: Azure Event Hubs SDK type bindings enable you to use types from service SDKs and frameworks, providing more capability beyond what is currently offered. To learn more, see SDK type bindings for Event Hubs in Python. SDK type bindings in Azure Functions for Azure Blob Storage in Java SDK type bindings for Azure Blob Storage enable the following key scenarios: Downloading and uploading blobs of large sizes, reducing current memory limitations and GRPC limits. Enabling advanced operations including partial reads, parallel uploads, and direct property manipulations. Improved performance by using blobs with Azure Functions To learn more, see SDK type bindings for Azure Blob Storage in Java. SDK type bindings for Azure Blob storage in Node.js The new SDK bindings capability in Azure Functions allows you to work directly with the Azure SDK types like BlobClient and ContainerClient instead of raw data when developing in JavaScript or TypeScript. This provides access to the SDK methods when working with blobs. You can learn more in the SDK type bindings for Azure Blob storage in the documentation. Language updates General availability announcements Python 3.12 You can now develop functions using Python 3.12 locally and deploy them to all Azure Functions plans. Python 3.12 builds on the performance enhancements that were first released with Python 3.11 and adds several performance and language readability features in the interpreter. You can now take advantage of these new features and enhancements when creating serverless applications on Azure Functions. Learn more. Azure Functions support for Java 21 LTS Azure Functions support for Java 21 is now generally available. You can now develop apps using Java 21 locally and deploy them to all Azure Functions plans on Linux and Windows. For more info: Updating your app to Java 21. Learn more about Java 21 More information about Azure Functions Supported Languages Durable Functions General availability announcements Durable functions v3 in Azure Functions Durable functions extension v3 in Azure Functions is now generally available. Major improvements in this new major version include improved cost efficiency for usage of Azure Storage v2 accounts and an upgrade to the latest Azure Storage SDKs, as well as the .NET Framework used by the extension. For more info: https://learn.microsoft.com/azure/azure-functions/durable/durable-functions-versions Public preview announcements Durable task scheduler Durable task scheduler is a new storage provider for Durable Functions. It is designed to address the challenges and gaps identified by our customers with existing bring-your-own storage options. Over the past few months, since the initial limited early access launch of the durable task scheduler, we’ve been working closely with our customers to understand their requirements and ensure they are fully supported in using durable task scheduler successfully. We’ve also strengthened the fundamentals by Expanding regional availability Finalizing APIs Ensuring high reliability, scalability and built-in security Adding support for all durable functions programming languages This is the preferred managed backend solution for customers who require high performance, enhanced monitoring of stateful orchestrations, or find managing bring-your-own storage accounts too cumbersome. It is the ideal choice for stateful functions (durable functions) in Azure Functions. Learn more. GitHub Copilot for Azure to develop your functions in VS Code With the GitHub Copilot for Azure extension, you can now generate complete Azure Functions code just by describing what you want — directly in VS Code. Using GitHub Copilot in agent mode, simply prompt it with your desired app logic, and it writes Functions code that follows Azure Functions’ best practices automatically. This means you don’t have to start from scratch — GitHub Copilot ensures your functions use the latest programming models, event-driven triggers, secure auth defaults, and recommended development patterns, so you can build scalable, production-ready apps faster and with confidence. Coming soon: Azure Functions deployment and infrastructure best practices, including Bicep generation, to streamline your entire Functions development lifecycle. Install GitHub Copilot for Azure to try it out today. Managed Identity support during application creation Azure Functions continues to invest in best practices to ensure customers can provide built-in security for their applications. Support for using managed identity is available for working with the required storage account used by Azure Functions as well as supported extensions. You can now configure managed identity directly in the portal when creating a function app to reduce the need for secrets. You can learn more about security in Azure Functions in the documentation. OpenTelemetry Support in Azure Functions Public Preview We're excited to announce significant improvements to OpenTelemetry support in Azure Functions, expanding on the limited preview announced last year. These enhancements deliver better observability, improved performance, and more detailed insights into your function executions, helping you diagnose issues faster and optimize your applications more effectively. These updates make it easier to monitor and troubleshoot your serverless apps with clearer, more relevant insights. To get started, enable OpenTelemetry in your function app and check out the latest documentation. We would love to hear feedback on these new capabilities and your overall experience with Functions so we can make sure we meet all your needs. You can click on the “Send us your feedback” button from the overview page of your function app. Thanks for all your feedback from the Azure Functions Team.Announcing Native Azure Functions Support in Azure Container Apps
Azure Container Apps is introducing a new, streamlined method for running Azure Functions directly in Azure Container Apps (ACA). This integration allows you to leverage the full features and capabilities of Azure Container Apps while benefiting from the simplicity of auto-scaling provided by Azure Functions. With the new native hosting model, you can deploy Azure Functions directly onto Azure Container Apps using the Microsoft.App resource provider by setting “kind=functionapp” property on the container app resource. You can deploy Azure Functions using ARM templates, Bicep, Azure CLI, and the Azure portal. Get started today and explore the complete feature set of Azure Container Apps, including multi-revision management, easy authentication, metrics and alerting, health probes and many more. To learn more, visit: https://aka.ms/fnonacav2Announcing Workflow in Azure Container Apps with the Durable task scheduler – Now in Preview!
We are thrilled to announce the durable workflow capabilities in Azure Container Apps with the Durable task scheduler (preview). This new feature brings powerful workflow capabilities to Azure Container Apps, enabling developers to build and manage complex, durable workflows as code with ease. What is Workflow and the Durable task scheduler? If you’ve missed the initial announcement of the durable task scheduler, please see these existing blog posts: https://aka.ms/dts-early-access https://aka.ms/dts-public-preview In summary, the Durable task scheduler is a fully managed backend for durable execution. Durable Execution is a fault-tolerant approach to running code, designed to handle failures gracefully through automatic retries and state persistence. It is built on three core principles: Incremental Execution: Each operation is executed independently and in order. State Persistence: The output of each step is saved to ensure progress is not lost. Fault Tolerance: If a step fails, the operation is retried from the last successful step, skipping previously completed steps. Durable Execution is especially advantageous for scenarios that require stateful chaining of operations, commonly known as workflow or orchestrations. A few scenarios include: Transactions Order Processing Workflows Infrastructure Management Deployment Pipelines AI / ML and Data Engineering Data Processing Pipelines and ETL Intelligent Applications with AI Agent Orchestrations Workflow in Azure Container Apps The Durable task scheduler features a managed workflow engine responsible for scheduling workflow execution and persisting workflow state. Additionally, it includes an out-of-the-box monitoring and management dashboard, making it easy for developers to debug and manage workflows on demand. You can author your workflows as code using the Durable Task SDKs, which currently support .NET, Python, and Java. Support for JavaScript and Go is on the roadmap. The Durable Task SDKs are lightweight, unopinionated, and designed to be portable across compute environments. To get started with the Durable Task Scheduler on Azure Container Apps: Import the Durable Task SDK for your preferred language and author your workflows. Provision a Durable Task Scheduler resource in your Azure environment. Connect your application to the Durable Task Scheduler backend for workflow orchestration and state persistence. Note: The Durable task scheduler is also available with Durable Functions that are deployed to Azure Container Apps. For more information on choosing the right workflow framework, please see this document: Key Benefits of using the Durable task scheduler for workflow task execution: Azure Managed: The Durable Task Scheduler provides dedicated resources that are fully managed by Azure. Orchestration and entity state management are completely built in. High Performance: The Durable Task Scheduler offers superior performance, efficiently managing high orchestration and task scheduling. Scalability: Manage sudden bursts of events with the ability to auto-scale your container app replicas using a built-in scaler, ensuring reliable, and efficient processing of orchestration work-items across your container app workers. Simplified Monitoring: With the built-in monitoring dashboard, developers can easily track the progress of their workflow, view activity durations, and manage workflows instances. Ease of Use: Author workflows as code using the Durable Task SDKs or Azure Durable Functions and connect directly to the Durable Task Scheduler backend. Security Best Practices: Uses identity-based authentication with Role-Based Access Control (RBAC) for enterprise-grade authorization, eliminating the need for SAS tokens or access keys. Versioning: Version workflows to support iterative changes without compatibility issues – enabling zero-downtime deployments. (Currently available in the .NET SDK; support for other SDKs is coming soon). Scheduling: Trigger workflows on a recurring interval, ideal for time-based automation. (Currently available in the .NET SDK; support for other SDKs is coming soon). Disaster Recovery: Ensure workflows can recover gracefully from failures from disasters, such as outages. (Coming soon). Get Started Today For more on the workflow capabilities using the Durable Task Scheduler in Azure Container Apps, see the official documentation here. To get started with workflow in Azure Container Apps, visit the quickstarts here. For more Azure Container Apps updates at Build 2025, refer to this blog: https://aka.ms/aca/whats-new-blog-build-2025
