Logic Apps Aviators Newsletter - September 2026
In this issue: Ace Aviator of the Month News from our product group News from our community Ace Aviator of the Month September 2026's Ace Aviator: Parth Talaviya What's your role and title? What are your responsibilities? AI-Powered Azure/.NET Solution Architect I work as an Azure/.NET Solution Architect, combining technical leadership with building a Microsoft-focused boutique company. My work mainly revolves around application modernization and migration, cloud architecture, integrations, and team leadership. I also stay hands-on with development, architecture reviews, production troubleshooting, and mentoring developers. Can you give us some insights into your day-to-day activities and what a typical day in your role looks like? My day usually starts with thinking about how we can add more value to our clients' businesses. It includes reviewing project priorities, solving technical challenges, discussing architecture, supporting developers, and collaborating with stakeholders. I mainly work across .NET, Azure, APIs, integrations, DevOps, and AI automation, so every day brings something new to learn and solve. What motivates and inspires you to be an active member of the Aviators/Microsoft community? What really motivates me is how active and supportive the Microsoft community is. People are genuinely willing to help each other, share experiences, and solve problems together. Being able to use my own experience to help someone overcome a challenge genuinely makes my day, while learning from others keeps me motivated to continuously improve. Looking back, what advice do you wish you had been given earlier that you'd now share with those looking to get into STEM/technology? You don’t have to do everything alone. In technology, especially when you are stuck on a very specific problem, it can sometimes feel that way. Asking questions, learning from the community, and collaborating with others can make a huge difference. I would also say: embrace AI early, but use it wisely. Don’t use AI only to generate code. Use it to understand concepts, challenge your thinking, explore better approaches, review your work, and become a better problem-solver. What has helped you grow professionally? Continuous learning and solving real-world challenges have helped me grow the most. Working on legacy modernization, cloud architecture, large-scale data systems, automation, and AI has taught me to think beyond writing code and understand the broader business impact. Being part of a strong technical community has also helped significantly. Whenever you are stuck, there is often someone who has faced a similar challenge and is willing to share their experience. If you had a magic wand that could create a feature in Logic Apps, what would it be and why? I would create an AI-powered Copilot troubleshooting and self-healing assistant for Logic Apps. It could analyze failed workflows, understand the execution context, identify the likely root cause, suggest a fix, and provide safe recovery options. For complex integrations, this could save significant troubleshooting time and allow developers to focus more on building solutions rather than spending hours finding where something went wrong. News from our product group Use connectors with Managed Identity in the Logic Apps Standard extension Managed Identity is Azure's built-in way to authenticate to Microsoft Entra-protected resources without storing credentials, secrets, or connection strings. Deployed Logic Apps have supported it for some time, and this post explains connector support in the Logic Apps Standard extension. Introducing dependency telemetry in Application Insights for Azure API Management policies Running API platforms at scale requires both handling load and understanding where inefficiencies occur. This post introduces dependency telemetry in Application Insights for Azure API Management policies to help teams identify performance bottlenecks. Power Azure SRE Agent with the tools it needs Azure SRE Agent is an AI-powered service designed to reduce operational toil. Teams can use it to investigate incidents, identify probable causes, and automate health-related operational work using connected tools. Give your Copilot agents real tools, without hand-wiring MCP GitHub Copilot coding agents can work independently on branches, but some tasks require access to external systems. This post shows how to equip agents with real tools without manually wiring Model Context Protocol integrations. Zonal redundancy in API management Standard v2 APIs power modern mobile experiences, microservices, AI-driven applications, and business-critical integrations. This post explains zonal redundancy in Azure API Management Standard v2 for improving resilience as customers modernize their API platforms. BizTalk Server 2020 End-of-Sale Announcement Microsoft announces that BizTalk Server 2020 and Host Integration Server 2020 sales are expected to end on March 31, 2027. Existing licensed deployments can continue to be used, while mainstream support for BizTalk Server 2020 is scheduled through April 12, 2028. An optional paid extended-mainstream support offering is planned for eligible customers through April 10, 2030, with final terms to be published later. The post recommends beginning migration planning now, evaluating Azure Logic Apps Standard or Azure Logic Apps Hybrid, inventorying dependencies, and using the Logic Apps Migration Agent to assess and convert supported workloads. Logic App Storage Inspector Logic App Storage Inspector is a read-only Kudu site extension for examining storage used by an Azure Logic App Standard application. It supports searching workflow action and trigger history by workflow, date, or text, with results exportable as CSV or JSON. Users can browse and compare workflow-definition versions, review table and queue information, and monitor health indicators and refresh status. The extension isolates access to the current Logic App site, uses asynchronous paged operations for large storage accounts, and can be installed from Kudu Site Extensions. News from our community Azure Logic Apps Automation – A Practical Infrastructure Lab with Agentic Remediation Post by João Paulo Costa João Paulo Costa tests Azure Logic Apps Automation through an after-hours virtual-machine remediation lab. The workflow uses an agent to inspect VM metadata, tags, and runtime state, then apply guardrails before deciding whether to deallocate the resource, leave it unchanged, or request review. The article contrasts agentic decision-making with deterministic workflows, documents tool and connector choices, and examines preview limitations such as runtime-state retrieval and managed-identity support. It also emphasizes constrained permissions, explicit policies, verification after actions, and negative testing for production, exemptions, missing ownership, and disabled remediation. Agentic Integration: Non-Deterministic Experience, Solid Core Post by Massimo Crippa Massimo Crippa examines whether agentic capabilities make enterprise integration non-deterministic. He separates an agent’s variable decision-making from the predictable integration layer that executes selected capabilities. The article highlights durable messaging, reliable contracts, idempotency, transactional boundaries, retries, compensation, governance, and observability as continuing requirements. It presents tools as the boundary between reasoning and execution: agents determine what should happen, while integration platforms control how operations are performed safely. Azure Logic Apps is positioned as both a deterministic integration foundation and a platform that can expose governed capabilities to emerging agentic experiences. Build AI Agents in Azure Logic Apps (Conversational + Autonomous) Video by Rafsan Huseynov Rafsan Huseynov presents a video on building conversational and autonomous AI agents with Azure Logic Apps. The session describes Logic Apps as more than a background integration layer, covering orchestration, managed identities, agent loops, and the use of workflows as MCP tools. Demonstrations explore connections with Document Intelligence, Blob Storage, Microsoft Foundry, and Copilot Studio workflows, alongside conversational and autonomous agent patterns. The video also introduces a separate low-code automation experience with scoped permissions, while noting that the demonstrations use synthetic data and that the experience remains in preview. BizTalk to Logic Apps migration (three things, everyone gets wrong) Post by Brajesh Sinha Brajesh Sinha explains why simple counts of BizTalk orchestrations and maps produce unreliable Logic Apps migration estimates. One orchestration can fan out into several Azure resources, supposedly simple maps may hide substantial transformation complexity, and operational requirements are often omitted from statements of work. The article highlights the architecture, mapping, and production-readiness effort that teams frequently underestimate. It encourages migration planners to assess actual behavior and dependencies rather than relying on inventory totals, helping create more realistic timelines, scope, and delivery expectations. Hybrid Logic Apps on RKE2: a self-managed cluster with MetalLB Post by Sonny Gillissen Sonny Gillissen demonstrates how to run Azure Logic Apps Hybrid on a self-managed RKE2 Kubernetes cluster. Because RKE2 does not include a native load balancer, the walkthrough uses MetalLB to assign the ingress IP required by the deployment. It covers creating the cluster, configuring networking, connecting the environment to Azure Arc, and handling the platform-specific details needed for Logic Apps. The article provides a practical alternative for teams evaluating hybrid integration workloads outside managed Kubernetes services, extending earlier guidance for OpenShift environments. An Introduction to Logic Apps Standard SDK Video by Marcel Medina Marcel Medina shares a Coding Night ANZ recording introducing the Logic Apps Standard SDK. The session shows how the SDK brings a modern, code-first .NET development experience to Azure Logic Apps while retaining the platform’s connectors, triggers, monitoring, and managed runtime. It is aimed at developers who want familiar tooling and stronger source-driven workflow development without giving up managed integration capabilities. The post also thanks the community for its questions and participation and provides the complete session recording for anyone who missed the live event. Managed Identity in Logic Apps Standard: A Zero Trust Read Post by Steef-Jan Wiggers Steef-Jan Wiggers examines new Managed Identity support for connectors in the Logic Apps Standard local development experience. Developers can now use a consistent authentication model from development through production instead of swapping connection strings before deployment, removing a common source of unmanaged secrets. The article frames this improvement through Zero Trust while stressing that authentication alone does not replace disciplined RBAC. A working azd sample demonstrates the setup and highlights a critical application setting whose absence causes the otherwise correctly configured connection, access policy, and role assignment to fail.AI Gateway tier of Azure API Management — August 2026 updates
The August 2026 update for the AI Gateway tier (preview) of Azure API Management introduces new capabilities for observing AI workloads and governing model spend. Release highlights Richer OpenTelemetry observability: AI Gateway can now emit logs, distributed traces, token metrics, and estimated cost metrics through the OpenTelemetry Protocol. The built-in monitoring experience helps teams investigate model and MCP tool activity, policy execution, latency, errors, token consumption, and individual traces. Model cost monitoring and budget enforcement: Teams can review estimated model spend by API key and model, then use cost limit policies to set calendar-based budgets and block requests when configured limits are reached. The release also streamlines Microsoft Foundry model imports, refines provider onboarding, improves portal reliability and error guidance, expands Anthropic Messages compatibility, and adds an in-product What's new experience. For configuration guidance, important upgrade information, and the complete list of changes, read the full August 2026 release notes in the AI Gateway portal. Try the latest capabilities in the AI Gateway portal.
Introducing APIOps CLI
APIOps CLI helps teams extract, version, review, preview, and publish Azure API Management configuration through source-controlled DevOps workflows. Today, we're excited to announce APIOps CLI, a new command-line experience designed to help organizations manage Azure API Management (APIM) using modern configuration-as-code and GitOps practices. As APIs become increasingly central to digital transformation, organizations need a reliable way to manage API definitions, policies, products, diagnostics, and gateway configuration across multiple environments. APIOps CLI provides a streamlined, developer-friendly approach to extract, version, review, and publish API Management configuration through familiar DevOps workflows. Why APIOps CLI? Traditional API management processes often rely on manual configuration changes, environment-specific customizations, and limited visibility into what changed and why. As API estates grow, these approaches become difficult to scale, audit, and govern. APIOps CLI addresses these challenges by enabling teams to manage API Management configuration as source-controlled artifacts. Every change can be reviewed through pull requests, tracked through Git history, and promoted consistently across development, test, and production environments. The result is improved governance, greater reliability, better collaboration between API developers and platform operators, and a simpler path toward enterprise-scale API operations. What APIOps CLI Enables APIOps CLI provides capabilities that help organizations adopt a true APIOps model: Extract API Management configuration into local artifact files Store and version configuration in Git repositories Review changes through standard pull request workflows Publish approved artifacts back into API Management environments Promote configuration consistently across environments Scaffold GitHub Actions and Azure DevOps pipelines Support automated CI/CD deployment patterns Enable auditable, repeatable API configuration management By treating API Management configuration as code, organizations gain the same operational excellence practices that software development teams have relied on for years. A Modern GitOps Workflow for APIs The APIOps CLI workflow follows a simple yet powerful pattern: Extract configuration from an existing API Management instance. Store the generated artifacts in source control. Review and approve changes through pull requests. Run automated validation and deployment pipelines. Publish approved configuration back to target API Management environments. This approach creates a clear separation between authoring, review, approval, and deployment while maintaining a complete audit trail of API platform changes. For organizations already practicing GitOps, APIOps CLI integrates naturally into existing development workflows and governance processes. Built for Real-World Enterprise Scenarios APIOps CLI is designed to support customers operating at enterprise scale. Common use cases include: Migrating away from manual API Management administration Standardizing deployments across multiple environments Establishing controlled promotion paths from development to production Implementing governance and compliance requirements Supporting platform engineering and API platform teams Managing large inventories of APIs, products, policies, and configurations Enabling self-service API development with centralized governance Whether you're operating a single API Management instance or managing a large multi-team API platform, APIOps CLI provides a foundation for consistent and repeatable operations. Integrated with Your Existing Toolchain APIOps CLI works alongside the tools teams already use: GitHub Azure DevOps Azure Pipelines GitHub Actions Azure CLI Existing Git repositories and branching strategies The tool can generate CI/CD scaffolding to accelerate adoption, helping teams move from manual operations to automated deployments with less effort. Open Source and Community Driven APIOps CLI is available as an open-source project under the Azure GitHub organization. The repository includes source code, architecture guidance, command documentation, CI/CD examples, walkthroughs, troubleshooting guidance, and reference material. By making the project open and community-driven, we are enabling customers, partners, and contributors to participate directly in the evolution of Azure API Management DevOps practices. Getting Started Getting started is straightforward: Install the APIOps CLI package. Authenticate with Azure. Extract an existing API Management instance into local artifacts. Commit those artifacts to a Git repository. Review and approve changes through pull requests. Publish approved changes back to Azure API Management. We recommend beginning with a non-production environment to establish your workflow, validate governance processes, and familiarize teams with the configuration-as-code model. Looking Ahead APIs have become a strategic asset for every organization. As API estates continue to expand, successful teams will increasingly adopt automation, governance, and GitOps practices to maintain speed without sacrificing control. APIOps CLI is an important step in that journey. It provides a modern foundation for managing Azure API Management configurations with the same rigor, automation, and reliability that organizations expect from modern software delivery practices. We invite you to explore APIOps CLI, try it in your environment, share feedback, and join us in shaping the future of API operations on Azure. Resources APIOps CLI GitHub repository: https://github.com/Azure/apiops-cli/tree/main Microsoft Learn: Manage API Management configuration with APIOps CLIZonal redundancy in API management Standard v2
APIs are the backbone of modern applications, powering everything from mobile experiences and microservices to AI-driven applications and business-critical integrations. As customers continue to modernize their platforms on Azure, they increasingly expect their API infrastructure to remain available even in the face of datacenter-level disruptions. With zone redundancy in Standard v2, Azure API Management now enables customers to increase resilience against Availability Zone failures while continuing to benefit from the simplicity, performance, and cost efficiency of the v2 platform. Why Zone Redundancy Matters Azure Availability Zones are physically separate locations within an Azure region, each with independent power, cooling, and networking infrastructure. By distributing API Management resources across multiple zones, organizations can reduce the impact of a single datacenter failure and improve service continuity for their APIs. Until now, customers who required built-in zone-level resiliency often needed to evaluate higher-end deployment options. With this enhancement, Standard v2 customers can now deploy API gateways across Availability Zones and benefit from improved reliability while maintaining the streamlined operational model of the v2 platform. What’s New Zone Redundancy for Standard v2 extends the platform's resiliency by distributing service capacity across multiple Availability Zones within a supported Azure region. Key benefits include: Higher Availability: API traffic continues to flow even if a single Availability Zone experiences an outage. Built-in Resiliency: Redundancy is provided at the platform layer, reducing the need for customers to design and manage complex intra-region failover solutions. Production-Ready Reliability: Customers can confidently run critical API workloads on Standard v2 with stronger availability guarantees. Operational Simplicity: The service automatically manages capacity distribution, health monitoring, and recovery behavior across zones. Cost-Effective Resilience: Customers gain zone-level protection without requiring an enterprise-tier deployment model. Built on the Modern v2 Platform The v2 platform was designed from the ground up to provide a faster, more reliable, and more scalable API Management experience. Standard v2 already delivers capabilities such as rapid deployment, simplified networking, workspace support, and flexible scaling. Zone Redundancy further strengthens the platform by expanding its reliability story for production workloads. This announcement builds on our broader investment in making Azure API Management more accessible to a wider range of organizations, from digital-native startups to large enterprises modernizing their application estates. Ideal Scenarios Zone Redundancy in Standard v2 is particularly valuable for customers who: Run business-critical APIs that must remain available during datacenter incidents. Consolidate multiple application workloads behind a single API gateway. Expose APIs consumed by mobile, partner, and customer-facing applications. Support AI applications and agent-based architectures that depend on highly available API endpoints. For organizations adopting modern cloud and AI native architectures, this capability helps ensure that API infrastructure remains aligned with broader application resiliency strategies. A Foundation for Reliable AI and API Platforms As AI-powered applications continue to proliferate, APIs increasingly become the critical connection layer between models, agents, business systems, and data platforms. Downtime at the API layer can have a direct impact on application availability, customer experience, and business operations. By bringing zone redundancy to Standard v2, we are making it easier for organizations to build highly resilient API platforms that can serve as the foundation for next-generation AI and digital transformation initiatives. Getting Started Zone Redundancy for Standard v2 can be enabled in supported Azure regions, allowing customers to deploy API Management with built-in protection against Availability Zone failures. We recommend reviewing your application's overall resiliency architecture, including backend redundancy, traffic management, and disaster recovery requirements, to maximize the benefits of zone-resilient API infrastructure. Enable Zone Redundancy in the Azure Portal Getting started with Zone Redundancy in Azure API Management Standard v2 is straightforward and can be configured during service creation. Create a New Standard v2 Instance with Zone Redundancy Sign in to the Azure portal. Select Create a Resource and search for Azure API Management. Choose Standard v2 as the service tier. Select a region that supports Availability Zones. In the Availability Zones section, enable Zone Redundancy. Review and create the service. After deployment, Azure API Management automatically distributes service capacity across multiple Availability Zones within the selected region, helping maintain API availability during a zone-level outage. Looking Ahead This release represents another step in our ongoing investment in the Azure API Management v2 platform. We remain committed to delivering the reliability, scalability, security, and developer experiences that organizations expect from a modern API management service. We are excited to see what our customers build with a more resilient Standard v2 platform and look forward to your feedback as you continue modernizing and scaling your API ecosystems on Azure. Learn more by visiting the Azure API Management documentation and exploring the latest reliability guidance for API Management deployments.Give your Copilot agents real tools, without hand-wiring MCP
You’re running agents in the GitHub Copilot app - maybe three at once, each on its own branch, each working a task while you steer. It’s a good way to work, right up until an agent needs to reach outside your repo. It needs to read a SharePoint library, file a Salesforce record, or check an Outlook calendar, and suddenly it’s blind. An agent is only as capable as the tools you hand it. The usual way to hand it one of those tools is to wire up a Model Context Protocol (MCP) server by hand: find the endpoint, paste it into config, add an auth header, keep the token alive, and hope you got the casing right. Then your teammate does the same thing on their machine, and the next person after that. The MCP Connectors canvas removes the hand-wiring. It’s a plugin for the GitHub Copilot app: it lists the hosted MCP servers already published in your Azure Connector Namespace, and you connect one to Copilot by selecting it - no URL, no header, no local proxy. Where the servers come from Connector Namespace is the managed Azure service on the other end. It provides a list of curated MCP Servers that you can use to quickly create and manage MCPs connections. It also does the work you’d rather not: it stores and rotates the credentials, applies retry and throttling policies, and scales the server. Your machine never holds a raw secret related to the end systems that MCP touches, and access is governed by access policies, allowing fine grained control on who can use the MCP servers. So, when you connect a server from the canvas, you’re not standing up infrastructure. The server already exists, already authenticated, already managed. You’re pointing Copilot at it. Prerequisites GitHub Copilot app with canvas extension support. An Azure subscription with permission to cerate and view the namespace and to create connections and hosted managed MCP server configurations. Connector Namespace is in preview, and availability varies by region. Install From the GitHub Copilot app, add the following prompt. Install connector-namespaces v1.2.0 for my user account, reload it, verify it is running, then open the MCP Connectors canvas. Open the Connector Namespace canvas in a new session Once you install if you need to open the Connector Namespace in a new session, just request it in a prompt: Open the MCP Connectors canvas. Connect an MCP server Open the MCP Connectors canvas. Select Sign in to Azure, then choose a subscription and Connector Namespace. Browse or search the MCP servers grouped under Microsoft and Partners. Select Connect and complete the connector’s separate authentication or consent flow. Confirm the server appears under My MCPs. Restart GitHub Copilot so a new session loads the added tools. ℹ️Note That last step matters more than it look. Copilot loads an agent’s tools when a session starts, so a server you add mid-session shows up in the next one, not the one you’re in. Manage connected servers My MCPs shows the servers connected to Copilot. Sandbox opens a connected server in the Connector Namespace playground, so you can try a tool call yourself before you let an agent lean on it. Disconnect removes the MCP registration from your MCP configuration. The MCP is still available in Connector namespace to be added again later. Also if you are sharing the connector namespace with other developers, this only remove the your local configuration. Connect adds configured MCP connectors that are not registered locally yet to your local MCP configuration. Delete from namespace… removes the MCP registration from your MCP configuration and deletes the MCP connection from your connector namespace. If you are sharing the connector namespace with other developers this will affect any developer using this MCP connector – be careful when using this. Switch namespace switches the active subscription or namespace - useful when you keep dev and production connections apart. How it works and security The extension registers each server directly in Copilot’s user-scoped MCP configuration. There’s no local MCP proxy sitting in the tool path between the agent and the server. The canvas itself runs on loopback. Your Azure tokens stay in process memory, the selected namespace coordinates may be cached locally, and gateway credentials are sent only to the server’s configured HTTPS endpoints. The connector’s own secrets never reach your machine. That’s the namespace’s job, which is also why an admin can rotate or revoke a connection in one place instead of chasing down every developer’s config. Where can I use my connected MCP servers You manage the servers from the canvas, but they don’t only work there. The canvas writes to Copilot’s user-scoped MCP configuration at ~/.copilot/mcp-config.json, and the GitHub Copilot app is built on GitHub Copilot CLI. Connect a server once and it’s available to both. Agent sessions in the app. Any session on your machine picks the server up, whatever repository or branch it’s working on, because the config is user-scoped rather than per-project. Copilot CLI. The same server shows up in the terminal. Run copilot mcp list to confirm it, or /mcp show SERVER-NAME in an interactive session to see the tools it exposes. Two things to know: Tools load when a session starts, so connect first and start the session second. A project-level .mcp.json or .github/mcp.json takes precedence over your user config when the names collide, which is what you want if a repository pins its own version of a server. VS Code is the exception. Copilot Chat there reads .vscode/mcp.json, a separate file the CLI doesn’t use, so a server you connect in the canvas won’t appear in VS Code until you configure it there too. Where this all sits today The GitHub Copilot app went generally available in June 2026, and canvases shipped with it, so neither is a preview feature. The MCP Connectors canvas is a published extension you install at personal scope. You get the version we ship and tested, not one an agent generates on the fly from a prompt. Two caveats. GitHub doesn't publish a compatibility guarantee for the canvas extension format, so an app update may need a matching update from us. Re-run the install commands above to pick it up if you see any issues. On the Azure side, Connector Namespace is still in preview. Check the documentation for more details on this service outside the GitHub Copilot App support. If you're on Copilot Business or Enterprise, check with your admin that the GitHub Copilot app policy is enabled. It's on by default, and it's separate from the Copilot CLI policy. Try it Install the plugin, point it at a namespace, and connect one server your agents keep reaching for: the SharePoint library, the CRM, whatever it is. Then watch an agent actually use it in the next session. Tell me where the canvas saved you a config headache and where it got in your way. That’s the feedback we act on before general availability.Power Azure SRE Agent with the tools it needs
What is the Azure SRE Agent Azure SRE Agent is an AI-powered service designed to reduce operational toil. Teams can use it to: Investigate incidents and identify probable causes. Automate health checks, compliance reviews, and other scheduled work. Answer questions such as “What changed before this service became degraded?” Propose remediations while allowing teams to require human approval. An effective investigation rarely depends on one source of information. An alert might originate in Azure Monitor, while deployment history lives in source control, telemetry stored in another observability platform, and incident records in a service-management tool. Without access to those systems, you must retrieve and transfer the information manually, adding context switching and slowing diagnosis. MCP servers can give SRE Agent tools to query telemetry, inspect deployments, retrieve database records, look up incidents, etc. SRE Agent provides native connection for some servers such as GitHub, Datadog, New Relic, and Splunk. Connector Namespace makes it easier to host additional remote MCP servers you want the agent to use. Removing remote MCP server hosting burden Connecting SRE Agent to an existing remote endpoint is straightforward. Hosting that endpoint yourself is not. You must deploy the server, provide secure HTTPS infrastructure, configure authentication, manage downstream credentials, scale the runtime, monitor its health, recover failed instances, and maintain it over time. These responsibilities are necessary, but the value is in the server’s tools not in operating another service. Azure Connector Namespace is a fully managed service for hosting connectors and MCP servers. You select the server you need and let the namespace handles the operational and maintenance tasks. The offering is currently in preview. See documentation for supported regions and other preview considerations. You’ll find a wide variety of servers in the Connector Namespace’s catalog. Some examples of useful servers for the SRE agent include: Database servers such as Azure SQL and Azure Cosmos DB Source control and CI/CD servers like GitLab Incident management servers like Jira and PagerDuty A note on what's currently in development: We're building “bring-your-own” server support, allowing you to supply your own server image while the namespace handles hosting and operations. Please keep an eye out for the blog post about this! Deploy server and connect it to SRE Agent The following example deploys the SQL MCP server in Connector Namespace and connects it to Azure SRE Agent. 1. Server deployment Prerequisite: Install the Azure Developer CLI (azd). Clone the sql-server-samples repo: git clone https://github.com/microsoft/sql-server-samples.git Navigate to the azure-sql-mcp sample cd sql-server-samples/samples/applications/azure-sql-mcp From the azure-sql-mcp folder, run the following to log into your Azure subscription and then deploy the server and related resources: azd auth login azd up The last command will prompt for the following before deployment: Prompt Suggested value Explanation Enter a unique environment name mcp-dev This name added as prefix to Azure resources created Select an Azure Subscription Pick your subscription Resources will deploy under this subscription Enter value for connectorNamespaceIdentityType UserAssigned User assigned identity is recommended as it’s not tied to resource lifecycle Enter value for deployerLoginName Enter your Azure subscription login email To give your identity access to the MCP server Enter value for the location Pick a supported region Supported regions: West Central US, Central US, East Asia, North Europe Once deployment finishes, copy the MCP endpoint for use later. It looks similar to: https://<app-name>.<region>.logic.azure.com/api/connectorGateways/123abc456defg7890/mcpServerConfigs/sql-mcp/mcp (Optional) Test deployed server in Visual Studio Code GitHub Copilot: Open command palette > search MCP: Add server > pick HTTP > enter MCP endpoint and server name > pick Local Workspace. Inside .vscode/mcp.json, click Start above server name, then allow authentication with Microsoft in the popup and log into Azure subscription account. 2. Configure MCP connector in SRE Agent Connector Namespace does not create the connection in SRE Agent. Add the server endpoint through SRE Agent’s existing MCP connection experience. Open the Azure SRE Agent portal On the left menu, go to Builder > Connectors, and select + Add connector Under Choose a connector, select the MCP tab, choose MCP server, and select Next Configure the connector: Field Value Name A descriptive name for the server Connection type Streamable-HTTP URI The hosted server endpoint from Connector Namespace Authentication method Managed identity (Selecting managed identity automatically creates an identity for the connector.) Azure AD token scope https://apihub.azure.com/.default Select Next. Before testing the connection, grant the managed identity access to the MCP server. 3. Authorize the managed identity Open the Azure portal, search for the managed identity by name. In the identity’s Overview page, click JSON View (top right) and copy the tenantId and principalId. The principal ID is also called the object ID. Open Connector Namespace portal and search for the deployed namespace. Inside the namespace, navigate to the MCP Connectors tab on the left, then select the SQL MCP server. Inside the MCP server, click Access Policies, then select Add Access Policy. Enter the tenant ID and principal ID, then select Create. 4. Test and finish the connection Return to Azure SRE Agent portal and select Test connection. After the test succeeds, select the server tools the agent should use. Select Add connector. Establishing the connection can take a minute. Select Refresh at the top of the connectors page until its status changes to Connected. The agent can now use the selected server tools in chat threads. The azd deployment from previous created and seeded a SQL database with sample blog post data, so you can ask something like: What are the top blog posts? For more details, see MCP connectors and tools in Azure SRE Agent. Focus on the server, not its infrastructure MCP servers can give Azure SRE Agent access to the additional systems it needs to investigate incidents and perform operational work effectively. However, operating every remote server yourself introduces infrastructure, security, and maintenance responsibilities that distract from that goal. Connector Namespace removes much of that friction. Your primary question becomes “Which MCP server do I want to host?” rather than “How will I deploy, secure, scale, monitor, and maintain it?” Once deployed, the hosted endpoint can be added to Azure SRE Agent through its existing MCP connection experience. That gives teams a straightforward path to extending the agent with more operational tools, without turning MCP server hosting into another platform they must build and run. Try Connector Namespace with Azure SRE Agent and share your feedback! Resources Azure SRE Agent Overview Set up an MCP connector in Azure SRE Agent Connector Namespace Overview Hosted MCP servers in Connector NamespaceAI Gateway tier of API Management now in public preview
Today, we are introducing the AI Gateway tier of Azure API Management, now in public preview. It gives platform teams a purpose-built experience built specifically for AI workloads - publishing and governing models and MCP servers. Controls are configured through policy cards rather than XML and expressions, and the portal experience and control plane are structured around models, MCP servers, and tools rather than APIs. (For brevity, we refer to the AI Gateway tier as AI Gateway throughout the rest of this article.) AI Gateway is built on Azure API Management, bringing proven operational capabilities to AI workloads. The resource runs in your subscription, uses your Entra tenant, and sends telemetry to destinations you control. The operating model will be familiar to existing API Management customers, but the interface is built around AI workloads. The AI Gateway tier is intended for teams that want this focused experience; other API Management tiers remain the right choice when organizations also need general-purpose API management or capabilities not included in the AI Gateway experience. A practical model for platform teams The AI Gateway gives platform teams a shared place to manage models, MCP servers, policies, and observability destinations, with access controlled through Azure RBAC. For example, a central platform group can connect a set of approved models and tools and publish them for application teams. The application teams can test those assets in the test console and build against them without routing every change through the central group. The platform group still owns the shared guardrails and can see how the assets are being used. After an asset is published, developers can create a named runtime key and begin calling the gateway immediately. Bring the models and tools you already use Most organizations don't standardize on a single model provider. Different models are selected based on quality, latency, cost, geography, or specialized capabilities. The preview supports models from Microsoft Foundry including OpenAI, Anthropic, Mistral, and other Foundry hosted models, as well as models hosted in AWS Bedrock, Google Vertex AI, OpenAI, and Anthropic. A guided wizard simplifies importing models from Microsoft Foundry. Other providers can be added by configuring a connection, with backend authentication configured as part of that connection. All published models are available under the same stable endpoint. Applications continue to use supported API formats such as OpenAI Chat Completions and Responses or Anthropic Messages directly or via SDKs. The AI Gateway extends governance beyond models to the MCP servers and tools agents use to interact with enterprise systems. You can expose an existing MCP server over SSE or Streamable HTTP, turn all or selected operations from a REST API into an MCP server by uploading its OpenAPI specification, or use more than 1,400 connector-backed tools from the Power Platform and Logic Apps library. You can also federate multiple MCP servers behind a single server, so an agent connects once and sees the tools across those servers. Backend authentication supports an API key, OAuth client credentials, managed identity, or mTLS. Governance that's built in Organizations need consistent governance across models and MCP servers without requiring every application team to implement those capabilities independently. The AI Gateway portal presents governance policies through an intuitive card-based experience rather than requiring policy XML. The same policies are expressed as JSON properties, making them easy to manage as infrastructure as code and to audit and enforce across a fleet with Azure Policy. In the public preview, those cards cover request and token rate limits, token quotas, Azure AI Content Safety, and fallback to a secondary model. Policies are applied per asset, making it clear which controls protect each model or MCP server. OpenTelemetry-based token metrics The AI Gateway emits token-usage metrics through OpenTelemetry, with attributes following GenAI and cloud semantic conventions. Metrics can be sent to Application Insights, Datadog, Splunk, Grafana Cloud, or another OTLP endpoint. The portal provides a monitoring view over Application Insights data. Better together: Microsoft Foundry and AI Gateway With AI Gateway, teams can extend the same governance controls, for example token rate limits and quotas, across models hosted in Microsoft Foundry and models hosted elsewhere. Foundry and non-Foundry models are published through gateway-managed endpoints, giving applications and agents a consistent way to access governed models regardless of where they are hosted. Foundry-hosted agents can consume curated sets of tools from Foundry toolboxes, with access to the underlying MCP servers and APIs governed through AI Gateway. Together, Microsoft Foundry and AI Gateway cover the enterprise application lifecycle: Foundry for building and running AI applications, and AI Gateway for publishing, governing, and observing models, tools, and MCP servers across your AI estate. The new AI Gateway tier will soon be available through the gateway experience in Microsoft Foundry portal. We are working toward a seamless, integrated AI Gateway experience within Foundry portal and will share more about that work separately. Available today in public preview The AI Gateway tier is available today at no cost in public preview in East US 2 and Sweden Central. Pricing will be shared separately. To provision a resource, add a model or MCP server, and make a first call click this to go to the AI Gateway tier portal and try it. If you prefer to start from code, use a sample to deploy all the required resources for a Foundry-hosted agent configured to access its model and tools through AI Gateway. We look forward to your feedback as we continue to rapidly evolve AI Gateway.Productize, observe, version, and automate MCP servers in Azure API Management
Introduction As organizations move from AI-assisted applications to agentic workflows, MCP servers are becoming a critical integration layer between agents, tools, APIs, data sources, and enterprise systems. Azure API Management already helps teams bring MCP servers under enterprise governance. But as MCP adoption scales, platform teams need more than basic exposure. They need a way to package MCP servers for the right consumers, understand tool usage in detail, manage changes safely, and automate configuration across environments. These are familiar API management challenges — and the same patterns that organizations already use for APIs can now be applied more deeply to MCP servers. We are excited to announce new generally available capabilities for MCP server management in Azure API Management: Add MCP servers to products to package and govern MCP capabilities for specific consumers MCP tool observability to trace tool usage, logs, errors, and payload context MCP server versioning to run multiple versions side by side and manage change safely Management API and Bicep support to automate MCP server configuration as part of CI/CD workflows Together, these capabilities extend MCP server management in Azure API Management and help make MCP servers first-class managed resources — productized, observable, versionable, and automatable. Why MCP server management matters MCP gives agents a standard way to connect with tools and external capabilities. That standardization is powerful, but it also introduces a new operational surface for enterprises. Without a management layer, teams can quickly run into questions such as: Which MCP servers are approved for use? Who can access each server? How do we expose MCP servers to different developer or agent audiences? How do we monitor tool calls, latency, errors, and cost? How do we run preview and production versions side by side? How do we automate MCP server configuration across environments? These are not just developer experience questions. They are enterprise governance questions. With Azure API Management, MCP servers can now be managed using the same core patterns organizations already use for APIs: products, subscriptions, policies, observability, versioning, and automation. What’s new 1. Add MCP servers to products Azure API Management products are a proven way to package APIs for consumption. With this release, you can now add one or more MCP servers to APIM products as well. This makes it easier to expose MCP capabilities to specific consumers, teams, applications, or agent experiences using familiar product-based governance. For example, a platform team can create a product for internal agents that includes approved MCP servers such as: Customer profile lookup Order status retrieval Knowledge base search Ticket creation Workflow automation tools By adding MCP servers to products, teams can use familiar controls such as subscriptions, quotas, approval workflows, and access management to govern how MCP capabilities are consumed. Why it matters: MCP servers are no longer isolated endpoints. They can be bundled, governed, and delivered as secure, consumable products. 2. MCP tool observability As agents use MCP servers to discover and invoke tools, teams need more than basic traffic visibility. They need end-to-end trace context for each agent-to-tool interaction. With MCP observability in Azure API Management, teams can inspect key MCP-specific details, including: Operation context: whether the request was a tools/list or tools/call operation Session context: the MCP session ID through gen_ai.conversation.id Client context: MCP client name and version Protocol context: MCP protocol name and version Server context: MCP server name and version Access context: authentication type and API type Tool context: tool name and tool type for tool invocation traces Error context: error type and error message when a call fails Payload context: tool invocation arguments and results when payload logging is enabled This is especially important for agentic workflows, where a single user request may trigger multiple tool calls across different systems. With APIM, MCP traffic can be traced, inspected, and monitored using the same operational practices teams already use across their API estate. Why it matters: MCP servers are not just accessible through APIM — they are observable. Platform teams can trace tool calls, inspect errors, and understand MCP usage with the same operational discipline they expect from managed APIs. 3. Expose multiple MCP versions Enterprise teams need safe ways to evolve MCP servers over time. With MCP server versioning in Azure API Management, you can expose multiple versions of the same MCP server side by side. This allows teams to run a stable GA version while introducing a preview or next version for early adopters. For example: v1 can serve the majority of production traffic. v2 can be exposed to a subset of consumers for testing. Teams can monitor adoption, errors, latency, and behavior. Once the new version is validated, v2 can be promoted with confidence. This pattern is especially useful when MCP tools evolve, schemas change, new capabilities are added, or teams want to validate agent behavior before rolling changes out broadly. Why it matters: MCP servers can now follow a safer lifecycle model: preview, validate, route, promote, and retire. 4. Management API and Infrastructure as Code MCP server management also needs to work at enterprise scale. With Management API and Infrastructure as Code support, teams can provision and configure MCP servers programmatically through Azure API Management APIs and automation pipelines. This allows platform teams to define MCP server resources as part of repeatable deployment workflows using tools such as Bicep, Terraform, ARM, REST APIs, and CI/CD pipelines. Teams can automate configuration for: MCP server endpoints Runtime and transport settings Authentication configuration Metadata and ownership Versioning Product association Policies Environment promotion This is critical for organizations that need consistent MCP governance across development, test, staging, and production environments. Why it matters: MCP server management can now be automated, reviewed, deployed, and governed like the rest of your API platform. How these capabilities work together Individually, each capability solves an important operational need. Together, they create a complete management model for MCP servers in Azure API Management. A platform team can: Register or expose MCP servers through Azure API Management. Package them into products for specific consumers. Apply access controls, subscriptions, quotas, and policies. Observe tool-level usage, latency, errors, traces, and cost. Run multiple versions side by side. Promote changes safely. Automate deployment through APIs and Infrastructure as Code. This brings the full API management playbook to MCP. Instead of treating MCP servers as unmanaged agent extensions, organizations can operate them as governed enterprise resources. Example scenario Imagine a company building internal copilots for customer support, sales, and operations. Each copilot needs access to different tools: Customer lookup Order history Case management Knowledge search Refund workflows Escalation workflows With MCP and Azure API Management, the platform team can expose these capabilities as MCP servers and organize them into products. The customer support copilot can subscribe to the support product. The sales copilot can subscribe to the sales product. Early adopters can be routed to a preview version of a tool. Operations teams can monitor usage, errors, latency, traces, and cost. Platform teams can automate the entire setup across environments. The result is a more governed and scalable way to bring MCP-based tools into enterprise agent workflows. Getting started To get started with MCP server management in Azure API Management: Create or identify an MCP server you want to expose through Azure API Management. Add the MCP server as a managed resource in APIM. Add the MCP server to an APIM product. Configure access, subscriptions, quotas, and approval workflows. Enable observability to monitor tool-level usage and traces. Use versioning to manage preview and production versions. Use the Management API or Infrastructure as Code to automate configuration. Conclusion MCP is quickly becoming an important standard for connecting agents to tools and enterprise capabilities. But for MCP to succeed in production, organizations need more than connectivity. They need governance, lifecycle management, observability, and automation. With these new MCP server management capabilities in Azure API Management, platform teams can manage MCP servers using the same trusted patterns they already use for APIs. MCP servers are now first-class APIM resources — productized, observable, versionable, and automatable. We are excited to see how customers use these capabilities to build the next generation of governed, enterprise-ready agentic applications.New AI gateway capabilities in Azure API Management
Multi-model, multi-protocol AI applications are quickly becoming the norm. Teams are mixing OpenAI, Anthropic, and Vertex AI models, exposing tools through MCP, and wiring agents together with A2A. As that surface grows, so does the work of keeping it secure, observable, and consistent. Our ongoing strategy for the AI gateway capabilities in Azure API Management centers on that problem: providing one place to manage models, MCP tools, and agents, no matter which provider or protocol is behind them. The updates below are the latest steps in that direction. Unified Model API (preview) The headline change in this release: the Unified Model API lets clients speak one API format — OpenAI Chat Completions — while API Management transforms requests to the backend provider, whether that's a model using OpenAI Chat Completions or Anthropic Messages API. By centralizing model access behind a single API layer, you can: Standardize on a single API format for clients, independently from the formats used by backend models. Unify observability, security, and governance with policies that apply across model providers. Configure failover across model providers. Decouple client-facing model names from backend model names using aliases. Learn more about the unified model API. Model aliases Model aliases give clients a stable, provider-neutral name to use when calling a model. By assigning an alias like gpt or claude-sonnet, you decouple the client-facing model name from the actual backend deployment. That makes a few common operations a lot easier: Upgrading a model. Update the alias target to point at a new version — no client code changes required. A/B tests. Shift traffic between backends behind the same alias using API Management's load balancing capabilities. Vendor swaps. Replace one provider with another without touching application code. Model discovery Developers can discover available models by calling the /models endpoint of the Unified Model API. API Management returns the list of model aliases, so apps and tools can adapt to what the platform team has published — without out-of-band documentation. Anthropic and Vertex AI models (GA) AI gateway policies and observability now work with Anthropic and Google Vertex AI models, alongside the providers we already support. You can: Apply runtime policies such as content safety, token limits, and semantic caching to Anthropic and Vertex AI traffic. Collect logs, traces, and metrics for these models in the same place as the rest of your AI traffic. If you're running a multi-provider setup, you no longer need a separate governance story for each vendor. Learn more about AI gateway capabilities in API Management. Anthropic API operations in Microsoft Foundry import When you import a Microsoft Foundry resource as an API in Azure API Management, the import now creates operations for Anthropic APIs alongside the existing model APIs. In a few clicks, you can stand up an API that mediates traffic to Foundry models using either the OpenAI or Anthropic API format — no manual operation definitions needed — and then apply the same policies, security, and observability you use for the rest of your AI traffic. Learn more about Microsoft Foundry import. Token metrics for additional token types (preview) Token tracking used to stop at prompt, completion, and total tokens. Modern models add cached, reasoning, and thinking tokens, which can make up a significant share of token consumption, cost, and latency. API Management now logs metrics for these additional token types into Application Insights, across API formats (OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages API) and providers (Microsoft Foundry, OpenAI, Amazon Bedrock, Google Vertex AI, and others). With richer signals, your cost dashboards, budget alerts, and capacity planning can actually reflect how today's models behave. Learn more about token metrics. Content safety for MCP and A2A (GA) The llm-content-safety policy now covers MCP and A2A traffic in addition to LLM traffic. That includes MCP tool-call arguments, MCP response text, and A2A payloads. A couple of related improvements: llm-content-safety can now be configured directly as an outbound policy. Two new attributes — window-size and window-overlap-size — let you tune how messages exceeding the Azure Content Safety limit of 10,000 characters are chunked and forwarded for validation, balancing detection sensitivity with Azure Content Safety call volume. The result is one consistent safety policy across LLM, MCP, and A2A flows instead of stitching together custom filters per protocol. Learn more about the content safety policy. A2A APIs (GA) Support for Agent-to-Agent (A2A) APIs in API Management is now generally available. Agent APIs can now be governed with the same policies, identity, and observability you use for the rest of your APIs. What you can do with A2A APIs in API Management: Mediate JSON-RPC runtime operations to your agent backend with full policy support — including the content safety improvements above. Expose and manage agent cards, automatically transformed by API Management to represent the managed agent API. Log traces to Application Insights using OpenTelemetry GenAI semantic conventions for deep correlation between API and agent execution traces. What's new in GA, on top of the preview: Available in classic tiers, in addition to v2 tiers — bring A2A governance to existing API Management resources without migrating tiers. Richer diagnostic logging for A2A APIs, giving more actionable telemetry for monitoring and troubleshooting agent traffic. Learn more about A2A support in API Management. Related: Bring Your Own Model in Foundry Agent Service (GA) Last month, Bring Your Own Model (BYOM) in Foundry Agent Service went GA. BYOM lets enterprise teams route Foundry agent model calls through their own infrastructure — typically for compliance, governance, or to reuse an existing model gateway. This pairs naturally with the AI gateway capabilities in Azure API Management. Put API Management in front of your models, apply the policies and observability described above, and have Foundry agents call through it — getting consistent governance for both your direct AI traffic and your agent workloads. Get started Together, these updates make Azure API Management a more complete AI gateway: consistent governance, security, and observability across models from various providers, MCP tools, and agent interactions. Some of these features are still rolling out. They will first become available in v2 tiers of API Management and in the AI release channel for classic tiers, then continue rolling out to the rest of classic tier resources over the following weeks. Get started with the unified model API or explore the AI gateway capabilities in API Management.🎉 Automation just became a team sport. Meet Azure Logic Apps Automation.
Low barrier to entry. Built for production. Now in Public Preview There's a moment that plays out in almost every organization right now. Someone closest a business problem - a retail ops lead, a finance analyst, a security analyst looks at a repetitive process and thinks, this should just run itself. For most of computing history, turning that idea into reality required specialized skills, significant setup, and engineering resources that were often focused elsewhere. AI is changing that. Today, people can describe what they want in natural language and watch working solutions take shape. The bottleneck is no longer generating an idea for automation. It's turning that idea into something secure, governed, and reliable enough to run in production. The demos are everywhere. The question organizations are increasingly asking is the harder one: which of these can we actually run in production? That's exactly the shift we built for. Today at Microsoft Build we're introducing Azure Logic Apps Automation, a new Logic Apps SKU that delivers the experience of a modern SaaS product for creating and running workflow automations. It makes it easier for teams to get started quickly while preserving the security, governance, reliability, and scale organizations expect from Azure. It's open to builders of every kind, available now in public preview at https://auto.azure.com. New experience, same enterprise engine The goal was straightforward: simplify the experience of building and running automations without compromising the enterprise foundation underneath. Logic Apps Automation provides a managed experience where compute, model endpoints, knowledge services, and execution environments are available out of the box. Teams can focus on solving business problems rather than assembling infrastructure and services. We also introduced a dedicated SaaS experience designed around productivity and collaboration. Administrators establish governance and policies, while builders can quickly begin creating workflows without requiring deep Azure expertise. "The redesigned experience lets me build AI-based solutions in record time. This platform will serve as the glue in most modern solutions.", Mick Badran, Founder & Director at SolveIT.Today [LA Automation Early Adopter] What we kept is just as important. Logic Apps Automation is built on the same Azure Logic Apps platform organizations trust today. The reliability, scale, security, governance, and operational maturity remain the foundation. The experience is simpler, but the platform underneath is the same proven technology customers rely on every day. Low barrier to entry. Built for production. We mean both halves of that sentence. Build like a startup, ship like an enterprise Building an automation is only part of the full application journey. As solutions move from experimentation to production, along with simple experience, organizations need security, governance, networking, identity, and operational controls to ensure those automations can be trusted at scale.Logic Apps Automation is designed for both realities. On the build side, it's fast to get started. Login and start building workflows; stay on a single canvas throughout the experience: use AI assisted workflow development, use visual workflows when they’re the right fit, and drop into code the moment you need additional control. No switching tools, no handoffs, no separate infrastructure to manage. On the production side, organizations get the capabilities they expect from an enterprise platform, on day-0: isolated compute, virtual network integration and private endpoints, identity, role-based access, audit logging, and governance policies. For many automation tools, becoming "enterprise-ready" is something that happens later. With Logic Apps Automation , production-readiness is part of the foundation. Built for how teams actually work Making automation easier for builders shouldn't create additional complexity for administrators. Organizations already have established governance boundaries, ownership models, and operational processes. Logic Apps Automation is designed to align with those realities through a simple two-level hierarchy of Projects and Applications. Project sits at the top and act as your security and governance boundary; inside each project you run one or more Applications. Admins and project owners set networking policies, connector policies, sandbox configuration, and approved AI models once, at the project scope and every application inherits them. Builders get a wide-open space to create. Admins get a firm line around it. Nobody has to choose between the two. Flexible permission management for individuals and teams The permission model is also designed to match how teams collaborate: A private space for an individual. To give a single user a place to run their own automations with a privacy boundary around personal resources such as their email account - create an application that only that individual can access. A shared space for a team. To support an automation that several people co-develop and operate together, add multiple users to the application so they can build, run, and maintain it collectively. The same model accommodates both access patterns, giving builders clear control over the scope of each application and who can work within it. AI-native, not AI-retrofitted Logic Apps Automation is designed for a new generation of business processes that combine workflows, AI agents, enterprise systems, and human decision-making. It starts with how you build. A built-in AI Assistant turns plain language into working automation. You describe what you want and it drafts the workflow, configures actions, writes expressions, and generates inline code, then helps you edit the same way. You can author at the level of a single step or an entire end-to-end flow. This is the thing that opens the platform to *every* developer: the person closest to the problem can describe it and get something real, while pros stay in control and drop to code whenever they want. "With the power of AI, automations just got on steroids! Simply tell it what you need, explain the intent, et voilà! Love it.", Sonny Gillissen, Integration Architect at Rubicon Cloud Advisor [LA Automation Early Adopter] Agents are first-class Agents are first-class, and we meet you where you are with three ways to integrate them: Agent-loop orchestration. If you're already using Logic Apps actions as tools inside an agent loop, that pattern carries forward. Your actions are callable tools the agent can invoke, so you keep orchestrating the way you always have. Foundry agents. Connect to an existing Microsoft Foundry Hosted or Prompt Agent or create a new one right from the canvas. The platform handles the wiring, and your workflow calls the agent, gets results back, and keeps moving. Managed sandbox for agent harnesses. Bring a well-known agent harness, like GitHub Copilot and run it in a managed, isolated sandbox. We take care of the compute, the isolation, native shell access, and your GitHub repos as first-class context; you just define the business logic. Then orchestrate all of these inside a larger workflow, right next to traditional rule-based actions, on a single canvas. Deterministic and agentic, in one place. A few capabilities that make this especially powerful: Sandboxed agent harnesses. Run agent harnesses such as GitHub Copilot in a managed, isolated sandbox with shell execution, skills, and GitHub repos as first-class context, without operating any of that infrastructure yourself. Tools and MCP. Turn any of the 1400+ connectors into a tool or expose any workflow as an MCP server that any compatible agent can call. No code required. Knowledge as a Service. Drop in your documents and the platform handles ingestion, chunking, embeddings, and retrieval. No RAG pipeline to build, no vector store to operate; just grounded answers. Any model, anywhere. Plug in whatever fits the job: frontier, open-source, fine-tuned, or local. You're never locked in. "Azure Automation closes the gap between integration and intelligence with agents as first-class workflow actions, grounded in your own data, executing in isolated sandboxes, all within the same canvas where your triggers and connectors live. Excited to see the evolution.", Sagar Sharma, Enterprise Solution Architect at i8c NL [LA Automation Early Adopter] What's new in this release Logic Apps Automation introduces several new capabilities designed to help teams build, deploy, and govern AI-powered automations: Zero-friction onboarding. Get from Sign-in to first workflow in minutes, with managed infrastructure and enterprise capabilities available from the start. A new designer. Modern designer with single pane experience to build and monitor workflows, draft-mode for workflows for easy iterations, instant code-to-workflow synchronization when you want to work in code-view, run history you can stream live, and so much more Natural language authoring. Describe workflows in plain language to create and edit them, with AI assistance in the designer. More powerful agents. Three ways to bring agents into a workflow; agent-loop orchestration, Foundry Hosted Agents, and well-known harnesses like GitHub Copilot running in a managed, isolated sandbox with shell access and GitHub repos as context. Knowledge as a Service. A managed knowledge layer that turns your documents into a ready-to-use knowledge base; no RAG pipeline required. JavaScript expressions. Write inline JavaScript to transform data and express logic without leaving the designer; no domain-specific language to learn. Projects and applications. A two-level governance hierarchy that gives admins a clear boundary and builders room to create. A permission management model that accommodates different level of access patterns, giving builders clear control over the scope of each application and who can work within it. Elastic scale, including to zero. Workflows scale up automatically when load arrives and scale all the way down to zero when there's no work to do. You pay only for the vCPU-seconds you actually use. Built to scale Logic Apps Automation scales automatically with demand, from idle workloads to business-critical processes. Customers pay only for the resources they use, without per-seat licensing requirements or infrastructure management overhead. When workflows aren't running, you're not paying for compute. When demand increases, the platform scales with you. Pricing Logic Apps Automation uses a consumption-based pricing model, so you pay only for what you use. Pricing is based on a small managed-environment fee, workflow execution, and optional services such as AI model usage, knowledge, sandboxes, connector calls. There is no annual commitment, no per-seat license, no quota cliff. When your workflows sit idle, you pay nothing for compute. More details to follow soon. What's available, and what's next Logic Apps Automation is available today in public preview, with an intial set of regions today, with more rolling out over the coming weeks. Here is the list of regions its available today: East Asia Sweden Central Australia East North Central US UK South Southeast Asia West US Coming Soon We're continuing to expand the platform with additional AI and enterprise capabilities, including: Foundry Hosted Agents. Create or Invoke Foundry Hosted Agents directly inside your workflows. Foundry Prompt Agents. Create/Invoke Foundry prompt directly inside your workflows. Hosted Models. Managed model endpoints provided for you; no keys or infrastructure to bring. Inline Python. Write inline Python alongside JavaScript when you need it. Bring your own container image. Run your own code in sandboxes; for example, orchestrate a Python ETL job from within a Logic Apps workflow. VNet support and private endpoints. Custom connectors and more Automation templates. Build custom connectors, start from a growing library of templates, and set project-level policies on connectors and more. Get started Whether you're automating a business process, orchestrating AI agents, integrating enterprise systems, or building entirely new AI-powered experiences, Logic Apps Automation provides a simpler path from idea to production. Start building today at https://auto.azure.com Read the docs at http://auto.azure.com/docs Watch the announcement session at Microsoft Build 2026. See it live at the Integrate conference, June 8–9.
