Logic Apps Aviators Newsletter - December 2025
In this issue: Ace Aviator of the Month News from our product group Community Playbook News from our community Ace Aviator of the Month December’s Ace Aviator: Daniel Jonathan What's your role and title? What are your responsibilities? I’m an Azure Integration Architect at Cnext, helping organizations modernize and migrate their integrations to Azure Integration Services. I design and build solutions using Logic Apps, Azure Functions, Service Bus, and API Management. I also work on AI solutions using Semantic Kernel and LangChain to bring intelligence into business processes. 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 begins by attending customer requests and handling recent deployments. Most of my time goes into designing integration patterns, building Logic Apps, mentoring the team, and helping customers with technical solutions. Lately, I’ve also been integrating AI capabilities into workflows. What motivates and inspires you to be an active member of the Aviators/Microsoft community? The community is open, friendly, and full of knowledge. I enjoy sharing ideas, writing posts, and helping others solve real-world challenges. It’s great to learn and grow together. 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? Start small and stay consistent. Learn the basics well—like messaging, retries, and error handling—before diving into complex tools. Keep learning and share what you know. What has helped you grow professionally? Hands-on experience, teamwork, and continuous learning. Working across different projects taught me how to design reliable and scalable systems. Exploring AI with Semantic Kernel and LangChain has also helped me think beyond traditional integrations. If you had a magic wand that could create a feature in Logic Apps, what would it be and why? I’d add an “Overview Page” in Logic Apps containing the HTTP URLs for each workflow, so developers can quickly access to test from one place. It would save time and make working with multiple workflows much easier. News from our product group Logic Apps Community Day 2025 Playlist Did you miss or want to catch up on individual sessions from Logic Apps Community Day 2025? Here is the full playlist – choose your favorite sessions and have fun! The future of integration is here and it's agentic Missed Kent Weare and Divya Swarnkar session at Ignite? It is here for you to watch on demand. Enterprise integration is being reimagined. It’s no longer just about connecting systems, but about enabling adaptive, agentic workflows that unify apps, data, and systems. In this session, discover how to modernize integration, migrate from BizTalk, and adopt AI-driven patterns that deliver agility and intelligence. Through customer stories and live demos, see how to bring these workflows to life with Agent Loop in Azure Logic Apps. Public Preview: Azure Logic Apps Connectors as MCP Tools in Microsoft Foundry Unlock secure enterprise connectivity with Azure Logic Apps connectors as MCP tools in Microsoft Foundry. Agents can now use hundreds of connectors natively—no custom code required. Learn how to configure and register MCP servers for seamless integration. Announcing AI Foundry Agent Service Connector v2 (Preview) AI Foundry Agent Service Connector v2 (Preview) is here! Azure Logic Apps can now securely invoke Foundry agents, enabling low-code AI integration, multi-agent workflows, and faster time-to-value. Explore new operations for orchestration and monitoring. Announcing the General Availability of the XML Parse and Compose Actions in Azure Logic Apps XML Parse and Compose Actions are now GA in Azure Logic Apps! Easily handle XML with XSD schemas, streamline workflows, and support internationalization. Learn best practices for arrays, encoding, and safe transport of content. Clone a Consumption Logic App to a Standard Workflow Clone your Consumption Logic Apps into Standard workflows with ease! This new feature accelerates migration, preserves design, and unlocks advanced capabilities for modern integration solutions. Announcing the HL7 connector for Azure Logic Apps Standard and Hybrid (Public Preview) Connect healthcare systems effortlessly! The new HL7 connector for Azure Logic Apps (Standard & Hybrid) enables secure, standardized data exchange and automation using HL7 protocols—now in Public Preview. Announcing Foundry Control Plane support for Logic Apps Agent Loop (Preview) Foundry Control Plane now supports Logic Apps Agent Loop (Preview)! Manage, govern, and observe agents at scale with built-in integration—no extra steps required. Ensure trust, compliance, and scalability in the agentic era. Announcing General Availability of Agent Loop in Azure Logic Apps Agent Loop transforms Logic Apps into a multi-agent automation platform, enabling AI agents to collaborate with workflows and humans. Build secure, enterprise-ready agentic solutions for business automation at scale. Agent Loop Ignite Update - New Set of AI Features Arrive in Public Preview We are releasing a broad set of Agent Loop new and powerful AI-first capabilities in Public Preview that dramatically expand what developers can build: run agents in the Consumption SKU ,bring your own models through APIM AI Gateway, call any tool through MCP, deploy agents directly into Teams, secure RAG with document-level permissions, onboard with Okta, and build in a completely redesigned workflow designer. Announcing MCP Server Support for Logic Apps Agent Loop Agent Loop in Azure Logic Apps now supports Model Context Protocol (MCP), enabling secure, standardized tool integration. Bring your own MCP connector, use Azure-managed servers, or build custom connectors for enterprise workflows. Enabling API Key Authentication for Logic Apps MCP Servers Logic Apps MCP Servers now support API Key authentication alongside OAuth2 and Anonymous options. Configure keys via host.json or Azure APIs, retrieve and regenerate keys easily, and connect MCP clients securely for agentic workflows. Announcing Public Preview of Agent Loop in Azure Logic Apps Consumption Agent Loop now brings AI-powered automation to Logic Apps Consumption with a frictionless, pay-as-you-go model. Build autonomous and conversational agents using 1,400+ connectors—no dedicated infrastructure required. Ideal for rapid prototyping and enterprise workflows. Moving the Logic Apps Designer Forward Major redesign of Azure Logic Apps designer enters Public Preview for Standard workflows. Phase I focuses on faster onboarding, unified views, draft mode with auto-save, improved search, and enhanced debugging. Feedback will shape future phases for a seamless development experience. Announcing the General Availability of the RabbitMQ Connector RabbitMQ Connector for Azure Logic Apps is now generally available, enabling reliable message exchange for Standard and Hybrid workflows. It supports triggers, publishing, and advanced routing, with global rollout underway for robust, scalable integration scenarios. Duplicate Detection in Logic App Trigger Prevent duplicate processing in Logic Apps triggers with a REST API-based solution. It checks recent runs using clientTrackingId to avoid reprocessing items caused by edits or webhook updates. Works with Logic App Standard and adaptable for Consumption or Power Automate. Announcing the BizTalk Server 2020 Cumulative Update 7 BizTalk Server 2020 Cumulative Update 7 is out, adding support for Visual Studio 2022, Windows Server 2022, SQL Server 2022, and Windows 11. Includes all prior fixes. Upgrade from older versions or consider migrating to Azure Logic Apps for modernization. News from our community Logic Apps Local Development Series Post by Daniel Jonathan Last month I shared an article from Daniel about debugging XSLT in VS Code. This month, I bumped into not one, but five articles in a series about Build, Test and Run Logic Apps Standard locally – definitely worth the read! Working with sessions in Agentic Workflows Post by Simon Stender Build AI-powered chat experiences with session-based agentic workflows in Azure Logic Apps. Learn how they enable dynamic, stateful interactions, integrate with APIs and apps, and avoid common pitfalls like workflows stuck in “running” forever. Integration Love Story with Mimmi Gullberg Video by Ahmed Bayoumy and Robin Wilde Meet Mimmi Gullberg, Green Cargo’s new integration architect driving smarter, sustainable rail logistics. With experience from BizTalk to Azure, she blends tech and business insight to create real value. Her mantra: understand the problem first, then choose the right tools—Logic Apps, Functions, or AI. Integration Love Story with Jenny Andersson Video by Ahmed Bayoumy and Robin Wilde Discover Jenny Andesson’s inspiring journey from skepticism to creativity in tech. In this episode, she shares insights on life as an integration architect, tackling system challenges, listening to customers, and how AI is shaping the future of integration. You Can Get an XPath value in Logic Apps without returning an array Post by Luis Rigueira Working with XML in Azure Logic Apps? The xpath() function always returns an array—even for a single node. Or does it? Found how to return just the values you want on this Friday Fact from Luis Rigueira. Set up Azure Standard Logic App Connectors as MCP Server Video by Srikanth Gunnala Expose your Azure Logic Apps integrations as secure tools for AI assistants. Learn how to turn connectors like SAP, SQL, and Jira into MCP tools, protect them with Entra ID/OAuth, and test in GitHub Copilot Chat for safe, action-ready AI workflows. Making Logic Apps Speak Business Post by Al Ghoniem Stop forcing Logic Apps to look like business diagrams. With Business Process Tracking, you can keep workflows technically sound while giving business users clear, stage-based visibility into processes—decoupled, visual, and KPI-driven.
Announcing the General Availability (GA) of the Premium v2 tier of Azure API Management
Superior capacity, highest entity limits, unlimited included calls, and the most comprehensive set of features set the Premium v2 tier apart from other API Management tiers. Customers rely on the Premium v2 tier for running enterprise-wide API programs at scale, with high availability, and performance. The Premium v2 tier has a new architecture that eliminates management traffic from the customer VNet, making private networking much more secure and easier to setup. During the creation of a Premium v2 instance, you can choose between VNet injection or VNet integration (introduced in the Standard v2 tier) options. In addition, today we are also adding three new features to Premium v2: Inbound Private Link: You can now enable private endpoint connectivity to restrict inbound access to your Premium v2 instance. It can be enabled along with VNet injection or VNet integration or without a VNet. Availability zone support: Premium v2 now supports availability zones (zone redundancy) to enhance the reliability and resilience of your API gateway. Custom CA certificates: Azure API management v2 gateway can now validate TLS connections with the backend service using custom CA certificates. New and improved VNet injection Using VNet injection in Premium v2 no longer requires configuring routes or service endpoints. Customers can secure their API workloads without impacting API Management dependencies, while Microsoft can secure the infrastructure without interfering with customer API workloads. In short, the new VNet injection implementation enables both parties to manage network security and configuration settings independently and without affecting each other. You can now configure your APIs with complete networking flexibility: force tunnel all outbound traffic to on-premises, send all outbound traffic through an NVA, or add a WAF device to monitor all inbound traffic to your API Management Premium v2—all without constraints. Inbound Private Link Customers can now configure an inbound private endpoint for their API Management Premium v2 instance to allow your API consumers securely access the API Management gateway over Azure Private Link. The private endpoint uses an IP address from an Azure virtual network in which it's hosted. Network traffic between a client on your private network and API Management traverses over the virtual network and a Private Link on the Microsoft backbone network, eliminating exposure from the public internet. Further, you can configure custom DNS settings or an Azure DNS private zone to map the API Management hostname to the endpoint's private IP address. With a private endpoint and Private Link, you can: Create multiple Private Link connections to an API Management instance. Use the private endpoint to send inbound traffic on a secure connection. Apply different API Management policies based on whether traffic comes from the private endpoint. Limit incoming traffic only to private endpoints, preventing data exfiltration. Combine with inbound virtual network injection or outbound virtual network integration to provide end-to-end network isolation of your API Management clients and backend services. More details can be found here Today, only the API Management instance’s Gateway endpoint supports inbound private link connections. Each API management instance can support at most 100 Private Link connections. Availability zones Azure API Management Premium v2 now supports Availability Zones (AZ) redundancy to enhance the reliability and resilience of your API gateway. When deploying an API Management instance in an AZ-enabled region, users can choose to enable zone redundancy. This distributes the service's units, including Gateway, management plane, and developer portal, across multiple, physically separate AZs within that region. Learn how to enable AZs here. CA certificates If the API Management Gateway needs to connect to the backends secured with TLS certificates issued by private certificate authorities (CA), you need to configure custom CA certificates in the API Management instance. Custom CA certificates can be added and managed as Authorization Credentials in the Backend entities. The Backend entity has been extended with new properties allowing customers to specify a list of certificate thumbprints or subject name + issuer thumbprint pairs that Gateway should trust when establishing TLS connection with associated backend endpoint. More details can be found here. Region availability The Premium v2 tier is now generally available in six public regions (Australia East, East US2, Germany West Central, Korea Central, Norway East and UK South) with additional regions coming soon. For pricing information and regional availability, please visit the API Management pricing page. Learn more API Management v2 tiers FAQ API Management v2 tiers documentation API Management overview documentation🤖 Agent Loop Demos 🤖
We announced the public preview of agent loop at Build 2025. Agent Loop is a new feature in Logic Apps to build AI Agents for use cases that span across industry domains and patterns. Here are some resources to learn more about them Logic Apps Labs - https://aka.ms/lalabs Agent in a day workshop - https://aka.ms/la-agent-in-a-day In this article, share with you use cases implemented in Logic Apps using agent loop and other features. This video shows a conversational agent that answers questions about Health Plans and their coverage. The demo features document ingestion of policy documents in AI Search and then retrieving them in Agent loop using natural language This video shows an autonomous agent that generates sales report. The demo features Python Code Interpreter which analyzed excel data and aggregates it for the LLM to reason on it This video shows a conversational agent that helps recruiters with candidate screening and interview scheduling. The demo features OBO (On-Behalf-Of) where agent uses tools in the security context of user. This video shows a conversational agent for a Utility company. The demo features multi agent orchestration using handoff, and a native chat client that supports multi turn conversations and streaming, and is secured via Entra for user authentication This video shows an autonomous Loan Approval Agent specifically that handles auto loans for a bank. The demo features an AI Agent that uses an Azure Open AI model, company's policies, and several tools to process loan application. For edge cases, huma in involved via Teams connector. This video shows an autonomous Product Return Agent for Fourth Coffee company. The returns are processed by agent based on company policy, and other criterions. In this case also, a human is involved when decisions are outside the agent's boundaries This video shows a commercial agent that grants credits for purchases of groceries and other products, for Northwind Stores. The Agent extracts financial information from an IBM Mainframe and an IBM i system to assess each requestor and updates the internal Northwind systems with the approved customers information. Multi-Agent scenario including both a codeful and declarative method of implementation. Note: This is pre-release functionality and is subject to change. If you are interested in further discussing Logic Apps codeful Agents, please fill out the following feedback form. Operations Agent (part 1): In this conversational agent, we will perform Logic Apps operations such as repair and resubmit to ensure our integration platform is healthy and processing transactions. To ensure of compliance we will ensure all operational activities are logged in ServiceNow. Operations Agent (part 2): In this autonomous agent, we will perform Logic Apps operations such as repair and resubmit to ensure our integration platform is healthy and processing transactions. To ensure of compliance we will ensure all operational activities are logged in ServiceNow.Logic Apps Aviators Newsletter - November 2025
In this issue: Ace Aviator of the Month News from our product group News from our community Ace Aviator of the Month Novembers’s Ace Aviator: Al Ghoniem What's your role and title? What are your responsibilities? As a Senior Integration Consultant, I design and deliver enterprise-grade integration on Microsoft Azure, primarily using Logic Apps Standard, API Management, Service Bus, Event Grid and Azure Functions. My remit covers reference architectures, “golden” templates, governance and FinOps guardrails, CI/CD automation (Bicep and YAML), and production-ready patterns for reliability, observability and cost efficiency. Alongside my technical work, I lead teams of consultants and engineers, helping them adopt standardised delivery models, mentor through code reviews and architectural walkthroughs, and ensure we deliver consistent, high-quality outcomes across projects. I also help teams apply decisioning patterns (embedded versus external rules) and integrate AI responsibly within enterprise workflows. Can you give us some insights into your day-to-day activities and what a typical day in your role looks like? Architecture and patterns: refining solution designs, sequence diagrams and rules models for new and existing integrations. Build and automation: evolving reusable Logic App Standard templates, Bicep modules and pipelines, embedding monitoring, alerts and identity-first security. Problem-solving: addressing performance tuning, transient fault handling, poison/DLQ flows and “design for reprocessing.” Leadership and enablement: mentoring consultants, facilitating technical discussions, and ensuring knowledge is shared across teams. Community and writing: publishing articles and examples to demystify real-world integration trade-offs. What motivates and inspires you to be an active member of the Aviators/Microsoft community? The community continuously turns hard-won lessons into reusable practices. Sharing patterns (and anti-patterns) saves others time and incidents, while learning from peers strengthens my own work. Microsoft’s product teams also listen closely, and seeing customer feedback directly shape the platform is genuinely rewarding. 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? Optimise for learning speed, not titles. Choose problems that stretch you and deliver in small, measurable increments. Master the fundamentals. Naming, idempotency, retries and observability are not glamorous but make systems dependable. Document everything. Diagrams, runbooks and ADRs multiply your impact. Understand trade-offs. Every decision buys something and costs something; acknowledge both sides clearly. Value collaboration over heroics. Ask questions, share knowledge and give credit freely. What has helped you grow professionally? Reusable scaffolding: creating golden templates and reference repositories that capture best practice once and reuse it everywhere. Feedback loops: leveraging telemetry, post-incident reviews and peer critique to improve. Teaching and mentoring: explaining concepts to others brings clarity and strengthens leadership. Cross-disciplinary curiosity: combining architecture, DevOps, FinOps and AI to address problems holistically. If you had a magic wand that could create a feature in Logic Apps, what would it be and why? "Stateful Sessions and Decisions” as a first-class capability: Built-in session state across multiple workflows, durable correlation and resumable orchestrations without external storage. A native decisioning activity with versioned decision tables and rule auditing (“why this rule fired”). A local-first developer experience with fast testing and contract validation for confident iteration. This would simplify complex, human-in-the-loop and event-driven scenarios, reduce custom plumbing, and make advanced orchestration patterns accessible to a wider audience. News from our product group Logic Apps Community Day 2025 Did you miss or want to catch up again on your favorite Logic Apps Community Day videos – jump back into action on this four hours long learning session, with 10 sessions from our Community Experts. And stay tuned for individual sessions being shared throughout the week. Announcing Parse & Chunk with Metadata in Logic Apps: Build Context-Aware RAG Agents New Parse & Chunk actions add metadata like page numbers and sentence completeness—perfect for context-aware document Q&A using Azure AI Search and Agent Loop. Introducing the RabbitMQ Connector (Public Preview) The new connector (Public Preview) lets you send and receive messages with RabbitMQ in Logic Apps Standard and Hybrid—ideal for scalable, reliable messaging across industries. News from our community EventGrid And Entra Auth In Logic Apps Standard Post by Riccardo Viglianisi Learn how to use Entra Auth for webhook authentication, ditch SAS tokens, and configure private endpoints with public access rules—perfect for secure, scalable integrations. Debugging XSLT Made Easy in VS Code: .NET-Based Debugging for Logic Apps Post by Daniel Jonathan A new .NET-based extension brings real debugging to XSLT for Logic Apps. Set breakpoints, step through transformations, and inspect variables—making XSLT development clear and productive. This is the 3 rd post in a 5 part series, so worth checking out the other posts too. Modifying the Logic App Azure Workbook: Custom Views for Multi Workflow Monitoring Post by Jeff Wessling Learn how to tailor dashboards with KQL, multi-workflow views, and context panes—boosting visibility, troubleshooting speed, and operational efficiency across your integrations. Azure AI Agents in Logic Apps: A Guide to Automate Decisions Post by Imashi Kinigama Discover how GPT-powered agents, created using Logic Apps Agent Loop, automate decisions, extract data, and adapt in real time. Build intelligent workflows with minimal effort—no hardcoding, just instructions and tools. How to Turn Logic App Connectors into MCP Servers (Step-by-Step Guide) Post by Stephen W. Thomas Learn how to expose connectors like Google Drive or Salesforce as MCP endpoints using Azure API Center—giving AI agents secure, real-time access to 1,400+ services directly from VS Code. Custom SAP MCP Server with Logic Apps Post by Sebastian Meyer Learn how to turn Logic Apps into AI-accessible tools using MCP. From workflow descriptions to Easy Auth setup and VS Code integration—this guide unlocks SAP automation with Copilot. How Azure Logic Apps as MCP Servers Accelerate AI Agent Development Post by Monisha S Turn 1,400+ connectors into AI tools with Logic Apps Standard. Build agents fast, integrate with legacy systems, and scale intelligent workflows across your organization. Designing Business Rules in Azure Logic Apps: When to Go Embedded vs External Post by Al Ghoniem Learn when to use Logic Apps' native Rules Engine or offload to Azure Functions with NRules or JSON RulesEngine. Discover hybrid patterns for scalable, testable decision automation. Syncing SharePoint with Azure Blob Storage using Logic Apps & Azure Functions for Azure AI Search Post by Daniel Jonathan Solve folder delete issues by tagging blobs with SharePoint metadata. Use Logic Apps and a custom Azure Function to clean up orphaned files and keep Azure AI Search in sync. Step-by-Step Guide: Building a Conversational Agent in Azure Logic Apps Post by Stephen W. Thomas Use Azure AI Foundry and Logic Apps Standard to create chatbots that shuffle cards, answer questions, and embed into websites—no code required, just smart workflows and EasyAuth. You can hide sensitive data from the Logic App run history Post by Francisco Leal Learn how to protect sensitive data like authentication tokens, credentials, and personal information in Logic App, so this data don’t appear in the run history, which could pose security and privacy risks.📢Announcement! Python Code Interpreter in Logic Apps is now in Public Preview
As AI agents evolve, they increasingly need to do more than just respond to text—they must analyze structured data, reason over complex patterns, and perform custom computations on demand. This is especially true in real-world scenarios where users upload large CSV files and expect agents to perform tasks like exploratory data analysis or generating insights—all from natural language prompts. Why This Matters The above image captures why this matters - behind this need lies a real challenge that many businesses face today. Data is diverse, fragmented, and large. It often comes in the form of CSV files, Excel spreadsheets, or JSON—containing thousands or even millions of rows. But this raw data is rarely useful on its own. It typically requires: Cleaning and transformation Custom logic to extract insights Visualizations or summaries that make the data actionable These steps are often manual, error-prone, and time-consuming—especially for users without data science or engineering expertise. Introducing Python Code Interpreter in Logic Apps Agent Loop We’re excited to announce support for Python code execution, powered by Azure Container Apps (ACA) session pool. This capability enables Logic Apps developers to use Python Code Interpreter in their workflows and also as a tool in Agent loop. You can author the code or use LLM to write code for you. As a code interpreter tool, it Accept natural language instructions Automatically generate Python code Execute that code securely on uploaded datasets (like CSV or JSON) Return insights, visualizations, or next-step data back to the user This brings the power of a code interpreter—similar to ChatGPT’s advanced data analysis tool—right into the Logic Apps runtime. Instead of writing code or manually manipulating spreadsheets, users can now describe their intent in natural language—for example: “Find the top 5 products by revenue” “Forecast demand by region for the next quarter” “Highlight customer segments based on purchase patterns” Under the hood, Logic Apps now enables this flow by interpreting the instruction, generating Python code, executing it securely in an isolated environment, and returning usable results— summaries, forecasts, or data transformations—within the same workflow. Real-World Use Cases This opens up a wide range of possibilities for businesses looking to embed intelligence into their automation: Sales & Marketing: Upload raw sales data and get on-the-fly summaries, forecasts, or regional comparisons. Finance: Analyze expense reports, detect anomalies, or generate quarterly breakdowns from Excel exports. Operations: Clean large log files, surface exceptions, and generate insights to improve reliability. Data Exploration: Let business users ask questions like “Which region had the highest YoY growth?” without writing a single line of code. How It Works The action to execute Python code is powered by Azure Container Apps (ACA)session pool. Azure Container Apps dynamic sessions provides fast and scalable access to a code interpreter. Each code interpreter session is fully isolated by a Hyper-V boundary and is designed to run untrusted code. By enabling network isolation on ACA, your data never leaves the defined network boundaries In Logic Apps, choose the action to execute Python code. You need to create a connection to the ACA session before you use the action. The code to execute can be authored by the developer or generated by the agent Optionally, upload file to the ACA session which can then be referenced as a data source in the Python code Run the workflow to get insights/results from the action execution Getting Started We can’t wait to see developers use this feature to build powerful agents! You can find all the details about the feature and step by step guidance to use this capability in our MS Learn document. If you have any questions, comments or feedback, please reach out to us via this form: http://aka.ms/la/feedbackIntroducing Logic Apps MCP servers (Public Preview)
Using Logic Apps (Standard) as MCP servers transforms the way organizations build and manage agents by turning connectors into modular, reusable MCP tools. This approach allows each connector—whether it's for data access, messaging, or workflow orchestration—to act as a specialized capability within the MCP framework. By dynamically composing these tools into Logic Apps, developers can rapidly construct agents that are both scalable and adaptable to complex enterprise scenarios. The benefits include reduced development overhead, enhanced reusability, and a streamlined path to integrating diverse systems—all while maintaining the flexibility and power of the Logic Apps platform. Starting today, we now support creating Logic Apps MCP Servers in the following ways: Registering Logic Apps connectors as MCP servers using Azure API Center Using this approach provides a streamlined experience when building MCP servers based upon Azure Logic Apps connectors. This new experience includes selecting a managed connector and one or more of its actions to create an MCP server and its related tools. This experience also automates the creation of Logic Apps workflows and wires up Easy Auth authentication for you in a matter of minutes. Beyond the streamlined experience that we provide, customers also benefit from any MCP server created using this experience to be registered within their API Center enterprise catalogue. For admins this means they can manage their MCP servers across the enterprise. For developers, it offers a centralized catalog where MCP servers can be discovered and quickly onboarded in Agent solutions. To get started, please refer to our product documentation or our demo videos. Enabling Logic Apps as remote MCP server For customers who have existing Logic Apps (Standard) investments or who want additional control over how their MCP tools are created we are also offering the ability to enable a Logic App as an MCP server. For a Logic App to be eligible to become an MCP server, it must have the following characteristics: One or more workflows that have an HTTP Request trigger and a corresponding HTTP Response action It is recommended that your trigger has a description and your request payload has schema that includes meaningful descriptions Your host.json file has been configured to enable MCP capabilities You have created an App registration in Microsoft Entra and have configured Easy Auth in your Logic App To get started, please refer to our product documentation or our demo videos. Feedback Both of these capabilities are now available, in public preview, worldwide. If you have any questions or feedback on these MCP capabilities, we would love to hear from you. Please fill out the following form and I will follow-up with you.Expose REST APIs as MCP servers with Azure API Management and API Center (now in preview)
As AI-powered agents and large language models (LLMs) become central to modern application experiences, developers and enterprises need seamless, secure ways to connect these models to real-world data and capabilities. Today, we’re excited to introduce two powerful preview capabilities in the Azure API Management Platform: Expose REST APIs in Azure API Management as remote Model Context Protocol (MCP) servers Discover and manage MCP servers using API Center as a centralized enterprise registry Together, these updates help customers securely operationalize APIs for AI workloads and improve how APIs are managed and shared across organizations. Unlocking the value of AI through secure API integration While LLMs are incredibly capable, they are stateless and isolated unless connected to external tools and systems. Model Context Protocol (MCP) is an open standard designed to bridge this gap by allowing agents to invoke tools—such as APIs—via a standardized, JSON-RPC-based interface. With this release, Azure empowers you to operationalize your APIs for AI integration—securely, observably, and at scale. 1. Expose REST APIs as MCP servers with Azure API Management An MCP server exposes selected API operations to AI clients over JSON-RPC via HTTP or Server-Sent Events (SSE). These operations, referred to as “tools,” can be invoked by AI agents through natural language prompts. With this new capability, you can expose your existing REST APIs in Azure API Management as MCP servers—without rebuilding or rehosting them. Addressing common challenges Before this capability, customers faced several challenges when implementing MCP support: Duplicating development efforts: Building MCP servers from scratch often led to unnecessary work when existing REST APIs already provided much of the needed functionality. Security concerns: Server trust: Malicious servers could impersonate trusted ones. Credential management: Self-hosted MCP implementations often had to manage sensitive credentials like OAuth tokens. Registry and discovery: Without a centralized registry, discovering and managing MCP tools was manual and fragmented, making it hard to scale securely across teams. API Management now addresses these concerns by serving as a managed, policy-enforced hosting surface for MCP tools—offering centralized control, observability, and security. Benefits of using Azure API Management with MCP By exposing MCP servers through Azure API Management, customers gain: Centralized governance for API access, authentication, and usage policies Secure connectivity using OAuth 2.0 and subscription keys Granular control over which API operations are exposed to AI agents as tools Built-in observability through APIM’s monitoring and diagnostics features How it works MCP servers: In your API Management instance navigate to MCP servers Choose an API: + Create a new MCP Server and select the REST API you wish to expose. Configure the MCP Server: Select the API operations you want to expose as tools. These can be all or a subset of your API’s methods. Test and Integrate: Use tools like MCP Inspector or Visual Studio Code (in agent mode) to connect, test, and invoke the tools from your AI host. Getting started and availability This feature is now in public preview and being gradually rolled out to early access customers. To use the MCP server capability in Azure API Management: Prerequisites Your APIM instance must be on a SKUv1 tier: Premium, Standard, or Basic Your service must be enrolled in the AI Gateway early update group (activation may take up to 2 hours) Use the Azure Portal with feature flag: ➤ Append ?Microsoft_Azure_ApiManagement=mcp to your portal URL to access the MCP server configuration experience Note: Support for SKUv2 and broader availability will follow in upcoming updates. Full setup instructions and test guidance can be found via aka.ms/apimdocs/exportmcp. 2. Centralized MCP registry and discovery with Azure API Center As enterprises adopt MCP servers at scale, the need for a centralized, governed registry becomes critical. Azure API Center now provides this capability—serving as a single, enterprise-grade system of record for managing MCP endpoints. With API Center, teams can: Maintain a comprehensive inventory of MCP servers. Track version history, ownership, and metadata. Enforce governance policies across environments. Simplify compliance and reduce operational overhead. API Center also addresses enterprise-grade security by allowing administrators to define who can discover, access, and consume specific MCP servers—ensuring only authorized users can interact with sensitive tools. To support developer adoption, API Center includes: Semantic search and a modern discovery UI. Easy filtering based on capabilities, metadata, and usage context. Tight integration with Copilot Studio and GitHub Copilot, enabling developers to use MCP tools directly within their coding workflows. These capabilities reduce duplication, streamline workflows, and help teams securely scale MCP usage across the organization. Getting started This feature is now in preview and accessible to customers: https://aka.ms/apicenter/docs/mcp AI Gateway Lab | MCP Registry 3. What’s next These new previews are just the beginning. We're already working on: Azure API Management (APIM) Passthrough MCP server support We’re enabling APIM to act as a transparent proxy between your APIs and AI agents—no custom server logic needed. This will simplify onboarding and reduce operational overhead. Azure API Center (APIC) Deeper integration with Copilot Studio and VS Code Today, developers must perform manual steps to surface API Center data in Copilot workflows. We’re working to make this experience more visual and seamless, allowing developers to discover and consume MCP servers directly from familiar tools like VS Code and Copilot Studio. For questions or feedback, reach out to your Microsoft account team or visit: Azure API Management documentation Azure API Center documentation — The Azure API Management & API Center Teams🚀 New in Azure API Management: MCP in v2 SKUs + external MCP-compliant server support
Your APIs are becoming tools. Your users are becoming agents. Your platform needs to adapt. Azure API Management is becoming the secure, scalable control plane for connecting agents, tools, and APIs — with governance built in. -------------------------------------------------------------------------------------------------------------------------------------------------------------------- Today, we’re announcing two major updates to bring the power of the Model Context Protocol (MCP) in Azure API Management to more environments and scenarios: MCP support in v2 SKUs — now in public preview Expose existing MCP-compliant servers through API Management These features make it easier than ever to connect APIs and agents with enterprise-grade control—without rewriting your backends. Why MCP? MCP is an open protocol that enables AI agents—like GitHub Copilot, ChatGPT, and Azure OpenAI—to discover and invoke APIs as tools. It turns traditional REST APIs into structured, secure tools that agents can call during execution — powering real-time, context-aware workflows. Why API Management for MCP? Azure API Management is the single, secure control plane for exposing and governing MCP capabilities — whether from your REST APIs, Azure-hosted services, or external MCP-compliant runtimes. With built-in support for: Security using OAuth 2.1, Microsoft Entra ID, API keys, IP filtering, and rate limiting. Outbound token injection via Credential Manager with policy-based routing. Monitoring and diagnostics using Azure Monitor, Logs, and Application Insights. Discovery and reuse with Azure API Center integration. Comprehensive policy engine for request/response transformation, caching, validation, header manipulation, throttling, and more. …you get end-to-end governance for both inbound and outbound agent interactions — with no new infrastructure or code rewrites. ✅ What’s New? 1. MCP support in v2 SKUs Previously available only in classic tiers (Basic, Standard, Premium), MCP support is now in public preview for v2 SKUs — Basic v2, Standard v2, and Premium v2 — with no pre-requisites or manual enablement required. You can now: Expose any REST API as an MCP server in v2 SKUs Protect it with Microsoft Entra ID, keys or tokens Register tools in Azure API Center 2. Expose existing MCP-compliant servers (pass-through scenario) Already using tools hosted in Logic Apps, Azure Functions, LangChain or custom runtimes? Now you can govern those external tool servers by exposing them through API Management. Use API Management to: Secure external MCP servers with OAuth, rate limits, and Credential Manager Monitor and log usage with Azure Monitor and Application Insights Unify discovery with internal tools via Azure API Center 🔗 You bring the tools. API Management brings the governance. 🧭 What’s Next We’re actively expanding MCP capabilities in API Management: Tool-level access policies for granular governance Support for MCP resources and prompts to expand beyond tools 📚 Get Started 📘 Expose APIs as MCP servers 🌐 Connect external MCP servers 🔐 Secure access to MCP servers 🔎 Discover tools in API Center Summary Azure API Management is your single control plane for agents, tools and APIs — whether you're building internal copilots or connecting external toolchains. This preview unlocks more flexibility, less friction, and a secure foundation for the next wave of agent-powered applications. No new infrastructure. Secure by default. Built for the future.📢Announcing agent loop: Build AI Agents in Azure Logic Apps 🤖
This post is written in collaboration with Kent Weare and Rohitha Hewawasam The era of intelligent business processes has arrived! Today, we are excited to announce agent loop, a groundbreaking new capability in Azure Logic Apps to build AI agents into your enterprise workflows. With agent loop, you can embed advanced AI decision-making directly into your processes – enabling your apps and automation to not just follow predefined steps, but to reason, adapt, and act autonomously towards goals. Agent loop becomes central to AI Agent development — it’s a new action type that brings together your AI model of choice, domain-specific tools, and enterprise knowledge sources. Whether you’re building an autonomous agent to process loan approvals, a conversational agent to support customers, or a multi-agent system that coordinates tasks such as Sales Report generation across agents, Agent Loop enables your workflows to go beyond static steps — making decisions, adapting to context, and delivering outcomes. Agent loop is implemented using kernel object in the Semantic Kernel. The kernel object, along with an LLM, creates the plan for what needs to be done, while Logic Apps runtime handles execution of that plan. Agent Loop is highly configurable, enabling you to build agents with diverse capabilities: Conversational or Autonomous Agents With Logic Apps' extensive gallery of connectors, you can build fully autonomous agents that respond to real-time events — like new records in a database, files added to a share, or messages in a queue. Agent Loop also supports conversational agents via Channels, allowing agents to interact with users through the Azure portal or custom chat clients. Bring your own Model Associate your AI agent with any Azure OpenAI model of your choice. As new models become available, you can easily switch or upgrade without re-architecting the solution. Define Agent Goals and Guardrails Specify your agent’s objective and behavioral boundaries through system prompts and user instructions. Using connectors like Outlook or Teams, you can easily introduce human-in-the-loop interactions for approvals or overrides — enabling safe, controlled autonomy. Tools and Knowledge, Built In Leverage hundreds of out-of-the-box connectors to equip agents with access to enterprise systems, APIs, and business data. Enrich their reasoning with knowledge from vector stores, structured databases, or unstructured files, and empower them to take meaningful actions across your environment. AI Agents in Action Here are some examples of AI Agents in Action that highlight the value and efficiencies of these agents across different domains and solution areas. A product return agent verifies order details, return eligibility, and refund rules, then processes the return or requests additional info from the customer. A loan approval agent evaluates credit score, income, and risk profile, applies business rules, and auto-approves or routes applications for review. A recruiting agent screens resumes, summarizes qualifications, and drafts personalized outreach to top candidates, streamlining early hiring stages. A sales report generation workflow uses a writer agent to draft content, a reviewer agent to verify accuracy, and a publisher agent to format and distribute the report. An IT operations agent triages alerts, checks recent changes, and either resolves common issues or escalates to on-call engineers when needed. A multi-agent retail supply chain solution combines inventory and logistics agents to ensure timely restocks and optimize fulfillment routes. Why agent loop matters Modern businesses thrive on agility and intelligence. Traditional workflows remain essential for deterministic tasks—especially those involving structured data or high-risk decisions. But when processes involve unstructured data, changing context, or require adaptive decision-making, AI agents excel. They can reason, act in real time, and dynamically sequence steps to meet goals. Agent Loop exactly serves this purpose. What makes Agent Loop especially powerful is its deep integration with the Logic Apps ecosystem. Logic Apps comes with over 1,400+ connectors for Microsoft and third-party services – from databases and ERP systems to SaaS applications and custom APIs. They can also invoke custom code and scripts, making it easy to tap into homegrown capabilities. The agent isn’t limited to information in its prompt; it can actively retrieve knowledge, perform transactions, and effect change in the real world via these connectors. Logic Apps is uniquely positioned to enable customers to leverage their API and connector ecosystem cohesively across their workflows and AI Agents to build agentic applications. Equally important, Agent Loop is designed for flexibility. You can orchestrate single-agent workflows or coordinate multiple agents working in tandem towards a common goal. Agent Loop can even involve humans in the loop when needed – for instance, pausing to get a manager’s approval or to ask for clarification – leveraging Logic Apps’ human workflow capabilities. All of this is handled within the familiar, visual Logic Apps designer, so you get a high-level view of the entire orchestration. How agent loop works At a high level, Agent Loop works by pairing the reasoning capabilities of large-scale AI models with the robust action framework of Logic Apps. Built on top of Semantic Kernel, the Agent loop operates in iterative cycles, allowing the agent to think, act, and learn from each step: Reasoning (Think): The agent (powered by an LLM like Azure OpenAI Service under the hood) and on Semantic Kernel, examines its goal and the current context. It decides what needs to be done next – whether that’s gathering more information, calling a specific connector, or formulating an answer. This step is essentially the AI “planning” its next action based on the goal you’ve provided and the data it has so far. Action (Act): The agent then carries out the decided action by invoking a tool or connector through Logic Apps. This could be anything from querying a database, calling a REST API, sending an email, to running a calculation. Thanks to Logic Apps’ extensive connector library, the agent has a rich toolbox at its disposal. Each action is executed as a Logic Apps step, meaning it’s secure, managed, and logged like any other workflow action. Reflection (Learn): After the action, the agent receives the results (e.g. data retrieved, outcome of the API call, user input, etc.). It then evaluates: Did this bring it closer to the goal? Does the plan need adjusting? The agent updates its understanding based on new information. This reflection is what lets the agent handle complex, open-ended tasks – it can correct course if needed, try alternative approaches, or conclude if the goal has been satisfied. These steps repeat in a loop. The Agent Loop action manages this cycle automatically – calling the AI model to reason, executing the chosen connector operations, feeding results back, and iterating. Why Build AI Agents in Logic Apps? Building AI agents is an emerging frontier in automation but doing it from the ground up can be daunting especially when organizations build them in large numbers. Agent Loop in Logic Apps makes this dramatically easier and more scalable for several reasons: Declarative Orchestration: Logic Apps provides a visual workflow canvas and a serverless runtime. The Agent Loop action plugs into this and the platform handles the sequence of steps and iterations, so you can focus on defining the goal and selecting the connectors (tools) the agent can use. Code extensibility: Logic Apps supports both declarative and code-first approaches to building agents. You can combine the two — using visual designer for orchestration and injecting code where needed through extensibility points. Write custom logic in C#, PowerShell, JavaScript, or use inline scripts for lightweight processing. Python support is coming soon, enabling even more flexibility. 1400+ Integrated Tools: With the rich connector ecosystem at its disposal, your agent can seamlessly tap into your enterprise systems and SaaS applications. Your entire ecosystem of connectors, APIs, custom code and agents can be used by deterministic workflows and agents to solve business problems Observability: Logic Apps offers full traceability into each agent’s decisions and actions. Every run is logged in the workflow history, with data stored within the customer’s own network and storage boundaries. The Agent Chat view provides insights into the agent’s reasoning, tool invocations, and goal progress. Developers can easily revisit these logs for debugging, auditing, or analysis. Enterprise-Grade Governance: Because it runs on Azure Logic Apps, agent loop inherits all the robust monitoring, logging, security and compliance capabilities of the platform You can secure connections with managed identities and leverage built-in rate limiting, retries, and exception handling. Your AI agents run with the same enterprise-ready guardrails as any mission-critical workflow. Human-in-the-Loop & Multi-Agent Coordination: Logic Apps makes it straightforward to involve people at key decision points or to coordinate multiple agents. You can chain Agent Loop actions or have agents invoke other workflows, enabling collaborative problem-solving that would be difficult to implement from scratch. The result is a system where AI and humans can smoothly interact and complement each other. Faster Time to Value: By eliminating the boilerplate work of building an agent architecture (managing memory, planning logic, connecting to services, etc.), Agent Loop lets developers and architects concentrate on high-value logic and business goals, accelerating how you bring AI-driven improvements to your business processes. In short, agent loop combines the brains of generative AI with the brawn of Azure’s integration platform. It offers a turnkey way to build sophisticated AI-driven automation without reinventing the wheel. Companies no longer have to choose between the flexibility of custom AI solutions and the convenience of a managed workflow service – with Logic Apps and Agent Loop, you get both. Getting Started Agent Loop is available in Logic Apps Standard starting today! Here are some resources to help you begin: Documentation: Explore the agent loop concepts and detailed guide with step-by-step instructions on how to configure and use Agent Loop. Samples & Demos: Watch pre-recorded demos showcasing both conversational and autonomous agent scenarios built with Agent Loop. You'll also get a preview of exciting features coming soon. Looking Ahead Agent Loop opens up a new realm of possibilities for what you can achieve with Azure Logic Apps. It blurs the line between application integration and AI, allowing workflows to evolve from static sequences into adaptive, self-directed processes. We can’t wait to see what you will build with Agent Loop! This is just the beginning. We’re actively investing in new capabilities that are planned for release soon Multi-agent Hand-off Support – A multi-agent application with hand-off capabilities enables different agent-loops to collaborate by transferring tasks between one another based on expertise or context, which is crucial for building agentic applications that can dynamically adapt to complex, evolving goals and user needs. A2A (Agent-to-Agent) protocol support – A2A is a communication standard that defines how autonomous agents exchange messages, share context, and coordinate actions in a secure and structured way. It’s especially important in building agentic applications because it ensures interoperability, enables seamless hand-offs between agents, and maintains context integrity across different agents working toward a shared goal. This will allow Logic Apps agents to seamlessly integrate with other agentic platforms. OBO Auth for Logic Apps Agents: On Behalf Of Auth support for logic Apps agents would allow Logic Apps agents to use logged-in users identity for authentication when invoking Logic Apps connectors as part of agent-loop execution. This will enable building conversational applications to dynamically perform OAuth flows for fetching consent from log-in users to invoke Logic Apps connectors on logged-in user’s behalf. Contact Us Have feedback or questions about Agent Loop? We’d love to hear from you. Reply directly to this blog post or reach out to us through this form. Your input helps shape the future of Logic Apps and agentic automation.📢Announcement: Power your Agents in Azure AI Foundry Agent Service with Azure Logic Apps
We’re excited to announce the Public Preview of two major integrations that bring the power of Azure Logic Apps to AI Agents in Foundry: Logic Apps as tools: You can now use Logic Apps workflows—and their 1400+ connectors—as tools within the Azure Foundry AI Agent Service. This unlocks seamless integration between AI agents and enterprise-grade automation—enabling agents to reason and act through Logic Apps. AI Agent Service connector: A new Logic Apps connector for the AI Agent Service is now available, allowing you to build workflows that can trigger agents based on events across hundreds of applications. This enables your agents to respond proactively and continuously—bringing event-driven autonomy to your AI solutions. Checkout the blogpost for these announcements from Foundry as well. Logic Apps as tool for Agents in Foundry Logic Apps now powers the tool layer for AI Agents in Foundry Agent Service —bringing together the strengths of business process automation and intelligent reasoning. AI agents need more than powerful models to be effective—they need the ability to act and the context to act appropriately. Tools play a critical role in this: they don’t just let agents perform actions—they provide the inputs, signals, and structure that anchor the agent’s reasoning and guide consistent behavior. Well-designed tools help ensure that agents make decisions based on reliable, real-world data and aligned business rules. With over 1400+ connectors, Logic Apps lets agents tap into real-world enterprise systems—such as reading records from a SQL database, retrieving order data from an ERP system like SAP, managing support tickets in ServiceNow, or triggering actions in CRM platforms like Dynamics or Salesforce. This integration transforms agents from passive responders into intelligent actors that can take meaningful, context-aware action across your organization. Requirements for using Logic Apps as Tools To use a Logic App as a tool within the AI Agent Service, your workflow must meet the following criteria: Consumption SKU: Currently, only Logic Apps in Consumption plan are supported. Request Trigger: The workflow must begin with a Request trigger so that it can be invoked by the agent via a REST call. Tool Description: Each workflow should include a clear, concise description to help the agent understand its purpose and appropriate usage. Getting Started with Logic Apps in AI Foundry There are two ways to bring Logic Apps into your agents’ toolset. You can find step by step instructions in the docs here. To summarize, Use prebuilt Microsoft authored templates Select from a library of curated Logic Apps templates designed for agent scenarios. After selecting a template: Configure the tool’s name and description Authenticate any services used in the workflow Set required parameters Once configured, the workflow will be deployed to your selected subscription and resource group, ready to be used by your agent. Import existing workflows If you already have Logic Apps powering key operations in your business, you can import them directly: Go to the Your Actions tab Select your existing workflow Provide a name and description for agent usage This makes it easy to extend your existing APIs and business logic to AI agents—no need to start from scratch. Tool Calling Demo In this demo video we build an AI Agent that can respond to any questions about GitHub issues and send an email report about them. The opportunities to unlock scenarios are endless and we can’t wait to hear from you. Logic Apps as a trigger for Agents in Foundry We’re excited to launch the AI Agent Service connector in Logic Apps—making it easier than ever to bring autonomy to your business processes. With this connector, you can now use any Logic Apps trigger—from HTTP requests to Service Bus messages, file drops, or scheduled events—to kick off a workflow that invokes an AI agent. This means your agents can now respond to real-world events in near real time, making decisions and taking actions based on dynamic context. Whether it’s processing a new order, reviewing a document, or triaging support tickets, Logic Apps + AI Agent Service gives you the power to build truly autonomous, intelligent workflows. Start Building Ready to try it out? Check out the documentation for step-by-step guidance on using Logic Apps as tools in the AI Agent Service. We’d love to hear what you build! Try the feature and share your feedback—your input helps shape the future of AI-powered automation in Azure.
