🎉 Azure Logic Apps: Ushering in the Era of Multi-Agentic Business Process Automation
We've reached another exciting milestone in our automation journey, after introducing Agent loop at Build this year. We're excited to announce enhancements that make Azure Logic Apps the multiagentic business process automation platform that empowers you to build intelligent, collaborative automation solutions. This isn't just about automating tasks—it's about creating an ecosystem where agents, workflows, and humans work together seamlessly to drive exceptional business outcomes. The key highlights of this release includes support for Agent loop in any workflow - new or existing, Python code interpreter, support for Foundry Agent Service in Agent loop, rich Conversational capabilities in our agentic workflows, and multiagent patterns in workflows. Along with these new features, we are also introducing Azure Logic Apps Labs, your hub for AI labs, workshops, and tutorials. Customer Momentum & Use Cases Agent Loop has been received with tremendous excitement since its introduction. Customers across industries—Healthcare, Retail, Energy, Financial Services, and more—are embracing it to reimagine how agents collaborate with humans, tools, and workflows. Today, thousands of customers—from startups to large enterprises—are building agentic workflows using Logic Apps platform that power both everyday tasks and mission-critical business processes. Customers are building agentic workflows that drive impact across a wide range of scenarios: Developer Productivity: Write code, generate unit tests, create workflows, mapping data between systems, automate source control,deployment and release pipelines. IT Operations: Incident Management, Ticket and Issues Handling, Policy review and enfocement, Triage, Resource management, cost optimization, issue remediation and more Business Process Automation: Empower sales specialists, retail assistants, order processing/approval flows, and healthcare assistants for intake and scheduling. Customer & Stakeholder Support: Project planning and estimation, Generating content, Automate communication, and streamline customer service workflows. Agent Loop is also powering the Logic Apps team’s own operations, demonstrating its versatility and real-world impact: Release & Deployment Agent: To streamline deployment and release management for the Logic Apps platform. Incident Management Agent: An extension of our SRE Agent, leveraging Agent Loop to accelerate incident response and remediation. Analyst Agent: Assists teams in exploring product usage and health data, generating insights directly from analytics. Evolving Automation: Extending workflows with intelligent Agents Workflows remain the backbone of reliable business automation—essential for governed, regulated, and strictly defined processes where consistency and auditability are paramount. Yet in today’s fast-moving environment, not every process fits into rigid rules. Some must adapt in real time, apply reasoning, and collaborate across multiple participants to achieve outcomes. That’s where our new agentic workflow capabilities come in—not to replace traditional workflows, but to complement them. Workflows deliver structure and reliability for repeatable processes, while agentic workflows powered by Agent loop add adaptability, reasoning, and collaboration for dynamic scenarios. With Logic Apps, you can orchestrate workflows, agents and human experts—preserving compliance where needed and enabling intelligence where it matters most. Every workflow is now Agentic Every workflow in Logic Apps is now an agentic workflow. This means you can seamlessly add AI intelligence to any existing business process with our Agent loop capability. Whether it's a simple approval workflow or a complex multi-step process, you can now infuse AI-powered decision-making and adaptability without rebuilding from scratch. Agent loop backed by Foundry Agent Service Azure Logic Apps now supports creating Agent loop backed by Foundry Agent service, giving you access to the full spectrum of models in Microsoft's Foundry - including third-party options—plus powerful built-in tools like Code Interpreter. You get the best of Microsoft's AI stack: use Logic Apps to build and orchestrate your agentic workflows, while Foundry serves as your centralized catalog for agents, models, and built-in tools. Conversational Agents in workflows built on A2A standards We’re excited to announce that Logic Apps now supports Conversational Agents in workflows—a major expansion beyond Autonomous Agents. Our conversational agents are built on the A2A (Agent-to-Agent) standard, making them fully interoperable within the broader A2A ecosystem of agents and applications. This standards-based approach ensures your Logic Apps agents can seamlessly participate in multi-vendor agent networks while maintaining enterprise-grade security., making them fully interoperable within the broader A2A ecosystem of agents and applications. This standards-based approach ensures your Logic Apps agents can seamlessly participate in multi-vendor agent networks while maintaining enterprise-grade security. Chat experience The out-of-box A2A chat client delivers a rich conversational experience with: Real-time streaming for responsive, natural interactions Multiturn conversations that maintain context across complex interactions Multiple session management allowing users to maintain separate conversation threads Designer integration for testing and development directly within Logic Apps Open-source external client option that organizations can fully customize and brand to match their specific requirements Per-user chat session that supports in-chat consent flow. Full security and isolation across user chats and sessions The chat client is open source so you can customize for your organizational needs Enterprise security by default Security isn't an afterthought—it's built into the foundation. Our conversational agents leverage Azure App Service's built-in Easy Auth capabilities - an out-of-the-box authentication layer that supports federated identity providers like Microsoft Entra. With no SDKs or code changes required, the platform automatically handles token validation, session management, and user identity injection—making your agents secure by default. Agents act On Behalf of Users Our agents operate with full user-aware context through per-user connections using the On-Behalf-Of (OBO) authentication flow. This important capability means that when an agent needs to call a tool, access data, or take an action, it does so using the specific user's identity and permissions—not a shared service account. This ensures that the access rights, permissions, and security policies applied to the user are consistently enforced across all downstream services, preventing unauthorized access. This user-aware approach using OBO transforms agents from simple chatbots into true collaborative partners that can take meaningful action while maintaining the security and governance standards your organization requires. Advanced multiagent orchestration Logic Apps now serves as a powerful workflow orchestration engine that enables sophisticated collaboration between multiple AI agents. Built on proven patterns used in production systems worldwide, our multiagent capabilities let you build automation workflows from simple agent handoffs to complex hierarchical systems where agents coordinate, delegate tasks, and work together to solve problems that would not be feasible for any single agent to handle alone. State Machine powered handoffs: Logic Apps now functions as a sophisticated state machine, enabling you to define precise handoff conditions between agents. This creates dynamic, powerful applications that can tackle complex problems by seamlessly transferring context and control between specialized agents. Nested Agent architecture: Build sophisticated patterns like supervisor-agent hierarchies where agents can utilize other agents as tools. This enables powerful architectural patterns that can break down complex challenges into manageable, specialized tasks. Python Code Interpreter: Extensible Agent tools With Python Code Interpreter support, agents can now think computationally—processing complex problems through code execution. Developers gain unlimited extensibility by bringing custom Python code as agent tools, either writing the code themselves or letting the agent generate it dynamically. This empowers agents to tackle large datasets, perform complex calculations, and execute custom business logic, giving developers the freedom to build specialized tools that go far beyond standard capabilities. Comprehensive Observability and Transparency Logic Apps provides complete run history for full transparency and auditability. We're now introducing task timeline visualization in run history that makes it easy to follow agent and task execution through an intuitive timeline view. The newly added task timeline captures the entire A2A communication flow—showing how tasks are initiated, delegated between agents, and completed, tools used, along with all messages exchanged throughout the process. This gives you full visibility into your multiagent workflows, letting you track task handoffs, monitor agent interactions, and understand the complete execution path for debugging and compliance needs. The platform that grows with your ambitions Logic Apps as a multiagent business process automation platform isn't just about today's needs—it's about future-proofing your automation strategy. As your business evolves and new AI capabilities emerge, your agents and workflows can evolve too, without requiring complete system overhauls. The beauty of this approach lies in its accessibility. Developers familiar with Logic Apps can immediately begin building agentic applications using familiar tools and patterns, while gradually exploring more sophisticated multiagent architectures as their needs grow. The future is built on collaboration! The future of automation is about creating intelligent systems where AI agents, automated processes, and human expertise work together seamlessly. Logic Apps now provides the platform to make this vision a reality. Built with security, isolation, scale, and governance, Logic Apps runs anywhere—giving you everything you need for production-ready applications. Welcome to the era of collaborative intelligence. Welcome to Azure Logic Apps as your Intelligent automation platform! Explore Logic Apps Labs The best way to learn is by building. Ready to get started? Introducing Azure Logic Apps Labs —your hub for AI labs, workshops, and tutorials. Whether you’re exploring agent basics, building autonomous or conversational agents, or designing advanced multi-agent patterns for building agentic workflows, this is the perfect place to begin. We’re continuously expanding these capabilities and welcome your feedback at or https://aka.ms/AgentLoopFeedbackLogic Apps - MCP Demos
We recently announced the ability to create MCP servers using Logic Apps connectors. In this post we are going to share some demo videos that will help you get started and provide you with some ideas on how you can build MCP servers to address your Agent connectivity needs. API Center + Logic Apps MCP Server Demos Getting Started - Salesforce Sales MCP Server In this video, we will leverage Azure API Center to create an MCP Server using Logic Apps connectors. Our solution will allow an end user to manage their Salesforce Contacts, Accounts and Opportunities. Building a Dataverse MCP Server In this video, we will leverage Azure API Center to create a Dataverse MCP Server using Logic Apps connectors. Our solution will allow an end user to gain insights on product returns and log an action for a quality control manager. Logic Apps MCP Server Demos Getting Started - ServiceNow Incident MCP Server In this video, we will take an existing Logic App (Standard) instance and enable it as an MCP server. Our MCP server will expose tools that help users assign IT Incident tickets to ServiceNow. Resources Looking for more resources? Check out our product documentation: API Center and Logic Apps MCP server Logic Apps as an MCP server📢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.Logic Apps Aviators Newsletter - August 25
In this issue: Ace Aviator of the Month News from our product group News from our community Ace Aviator of the Month August Ace Aviator: Jenny Anderson What's your role and title? What are your responsibilities? I’m an Integration Architect at Tietoevry Tech Services, where I work with large enterprise customers to develop integration solutions. For the past two years my main focus has been on cloud and hybrid integrations. I design integration architectures, advise on best practices including security and the chosen architecture, and collaborate closely with development teams to implement and maintain these solutions. Can you give us some insights into your day-to-day activities and what a typical day in your role looks like? My days usually start with scrum meetings across ongoing projects, which help me stay updated on progress, align with teams and prioritize my tasks for the day. After that, I often have customer meetings where I advise on integration strategies, provide architectural guidance or work on pre-sales engagements to scope out potential solutions. Recently, a big focus has been on BizTalk migrations, helping customers modernize their integration platforms by moving to Azure-based solutions. I try to dedicate my afternoons to hands-on technical work, which I really enjoy. Lately, that’s involved working with the new hybrid capabilities in Logic Apps. It’s a great mix of strategic consulting and deep technical implementation, which keeps the role dynamic and rewarding. What motivates and inspires you to be an active member of the Aviators/Microsoft community? I’ve always received a lot of support from the community especially when I was starting out in my career and I still benefit from it today. That generosity and openness made a big impact on me, so I feel it’s important to give back whenever I can. For me it’s a way to pay it forward and stay connected to a network that has helped me grow both technically and professionally. 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? Don’t overthink it, just start doing! In the beginning of my career, I assumed that everyone else knew everything, and that I couldn’t contribute or be part of certain areas because I didn’t know enough. But the truth is, no one knows everything, and that’s completely okay. The best way to learn is by doing and taking on challenges, making mistakes and growing from experience. I believe confidence comes from action, not from waiting until you feel “ready.” What has helped you grow professionally? One thing that has really helped me grow is surrounding myself with people who have different experiences or areas of expertise, whether at work, in communities, or through networking. I’ve learned a lot simply by asking questions, even the ones that might seem obvious. I also try to say yes to new opportunities, especially when they push me outside my comfort zone. Being an overthinker, I’ve developed a personal mantra: “Think 40%, do 60%.” It reminds me not to get stuck in planning or doubt, but to take action and learn along the way. That mindset has really helped me move forward. If you had a magic wand that could create a feature in Logic Apps, what would it be and why? If I could use a bit of magic in Logic Apps, I’d want AI to automagically handle all the data mappings. It’s honestly my least favorite part of integration work. It takes forever, it’s a bit dull and yet it’s always important. So, if AI could just step in and quietly take care of it, I wouldn’t complain. I’ve also heard a few customers ask for a disconnected control plane that can be hosted on-premises. That would be a big win for scenarios where cloud access is limited or compliance rules are extra strict. News from our product group Logic Apps Live July 2025 Missed Logic Apps Live in July? You can watch it here. We had a sneak peek into Logic Apps MCP Servers and Python support for Agent loop. Excinting topics and worth a watch! Troubleshoot Az Module within Logic App Standard Learn how to resolve Az Module installation failures in Logic Apps due to network restrictions or storage limits. Quick tests and fixes included to keep your workflows running smoothly. Introducing API Management Support in the Azure SRE Agent Azure’s SRE Agent now supports API Management, offering real-time diagnostics, backend health visualization, and intelligent remediation to keep your APIs reliable and scalable. Launch Your Private MCP Registry with Azure API Center. Discover how to create a secure, governed, and enterprise-ready MCP registry using Azure API Center—empowering AI innovation while maintaining control and visibility. Perform video analysis by using Azure Machine Learning and Computer Vision Replace manual video review with a scalable, AI-powered pipeline using Azure Machine Learning, Logic Apps, and Computer Vision. Boost accuracy and efficiency across industries like agriculture, traffic control, and manufacturing. Bringing Azure Logic Apps to on-prem, private, or public cloud with new Hybrid model | Azure Friday In this video Scott Hanselman and Harold Campos discuss the new Logic Apps Hybrid deployment model that allows customers to run their integration workloads in their own Kubernetes environments. This is ideal for customers initiating their journey to the cloud and hosting multiple on-premises workloads, who need to meet industry regulations, who wants to reuse their own Kubernetes infrastructure, or to avoid the natural latency introduced in hybrid configurations. News from our community Exposing Logic Apps as MCP Server in Azure API Management Video by Kent Weare On top of his PM work, Kent also finds time to keep his personal YouTube channel quite active. This time, he shows a walkthrough of creating an MCP Server using Logic Apps and API Management. The initial explanation of MCP and the various protocols alone make this video a great watch! Integration Love Story - Divya Swarnkar Video by Ahmed Bayoumy and Robin Wilde In this short episode of Integration Love Story, Ahmed and Robin chat with our own Divya Swarnkar, Product Manager at Microsoft who's been on an incredible journey from using Logic Apps as a customer to now helping build the product with the team behind the scenes. From BizTalk to Azure: A Guide for the Slightly Terrified Post by Sandro Pereira Explore the risks, timelines, and migration strategies as BizTalk nears end-of-life. Sandro shares the webinar recording – another tool to help you decide whether to stay or move to Azure Integration Services—without losing sleep. Azure Logic Apps Naming Conventions whitepaper Post by Sandro Pereira Boost clarity, scalability, and collaboration in Azure Logic Apps with this whitepaper. Learn best practices for naming triggers, actions, variables, and more - essential for automation, CI/CD, and long-term maintainability. You can create and use your own personal templates in Azure Logic Apps Post by Sandro Pereira It is not a newsletter, without at least a Friday Fact from Sandro! In this post, you can learn how to build, manage, and share reusable templates for consistent, efficient integration across projects. Speed up automation and standardize workflows you’re your own personal templates in Logic Apps.Build. Secure. Launch Your Private MCP Registry with Azure API Center.
We are thrilled to embrace a new era in the world of MCP registries. As organizations increasingly build and consume MCP servers, the need for a secure, governed, robust and easily discoverable tools catalog has become critical. Today, we are excited to show you how to do just that with MCP Center, a live example demonstrating how Azure API Center (APIC) can serve as a private and enterprise-ready MCP registry. The registry puts your MCPs just one click away for developers, ensuring no setup fuss and a direct path to coding brilliance. Why a private registry? 🤔 Public OSS registries have been instrumental in driving growth and innovation across the MCP ecosystem. But as adoption scales, so does the need for tighter security, governance, and control, this is where private MCP registries step in. This is where Azure API Center steps in. Azure API Center offers a powerful and centralized approach to MCP discovery and governance across diverse teams and services within an organization. Let's delve into the key benefits of leveraging a private MCP registry with Azure API Center. Security and Trust: The Foundation of AI Adoption Review and Verification: Public registries, by their open nature, accept submissions from a wide range of developers. This can introduce risks from tools with limited security practices or even malicious intent. A private registry empowers your organization to thoroughly review and verify every MCP server before it becomes accessible to internal developers or AI agents (like Copilot Studio and AI Foundry). This eliminates the risk of introducing random, potentially vulnerable first or third-party tools into your ecosystem. Reduced Attack Surface: By controlling which MCP servers are accessible, organizations significantly shrink their potential attack surface. When your AI agents interact solely with known and secure internal tools, the likelihood of external attackers exploiting vulnerabilities in unvetted solutions is drastically reduced. Enterprise-Grade Authentication and Authorization: Private registries enable the enforcement of your existing robust enterprise authentication and authorization mechanisms (e.g., OAuth 2) across all MCP servers. Public registries, in contrast, may have varying or less stringent authentication requirements. Enforced AI Gateway Control (Azure API Management): Beyond vetting, a private registry enables organizations to route all MCP server traffic through an AI gateway such as Azure API Management. This ensures that every interaction, whether internal or external, adheres to strict security policies, including centralized authentication, authorization, rate limiting, and threat protection, creating a secure front for your AI services. Governance and Control: Navigating the AI Landscape with Confidence Centralized Oversight and "Single Source of Truth": A private registry provides a centralized "single source of truth" for all AI-related tools and data connections within your organization. This empowers comprehensive oversight of AI initiatives, clearly identifying ownership and accountability for each MCP server. Preventing "Shadow AI": Without a formal registry, individual teams might independently develop or integrate AI tools, leading to "shadow AI" – unmanaged and unmonitored AI deployments that can pose significant risks. A private registry encourages a standardized approach, bringing all AI tools under central governance and visibility. Tailored Tool Development: Organizations can develop and host MCP servers specifically tailored to their unique needs and requirements. This means optimized efficiency and utility, providing specialized tools you won't typically find in broader public registries. Simplified Integration and Accelerated Development: A well-managed private registry simplifies the discovery and integration of internal tools for your AI developers. This significantly accelerates the development and deployment of AI-powered applications, fostering innovation. Good news! Azure API Center can be created for free in any Azure subscription. You can find a detailed guide to help you get started: Inventory and Discover MCP Servers in Your API Center - Azure API Center Get involved 💡 Your remote MCP server can be discoverable on API Center’s MCP Discovery page today! Bring your MCP server and reach Azure customers! These Microsoft partners are shaping the future of the MCP ecosystem by making their remote MCP Servers discoverable via API Center’s MCP Discovery page. Early Partners: Atlassian – Connect to Jira and Confluence for issue tracking and documentation Box – Use Box to securely store, manage and share your photos, videos, and documents in the cloud Neon – Manage and query Neon Postgres databases with natural language Pipedream – Add 1000s of APIs with built-in authentication and 10,000+ tools to your AI assistant or agent - coming soon - Stripe – Payment processing and financial infrastructure tools If partners would like their remote MCP servers to be featured in our Discover Panel, reach out to us here: GitHub/mcp-center and comment under the following GitHub issue: MCP Server Onboarding Request Ready to Get Started? 🚀 Modernize your AI strategy and empower your teams with enhanced discovery, security, and governance of agentic tools. Now's the time to explore creating your own private enterprise MCP registry. Check out MCP Center, a public showcase demonstrating how you can build your own enterprise MCP registry - MCP Center - Build Your Own Enterprise MCP Registry - or go ahead and create your Azure API Center today!Introducing API Management Support in the Azure SRE Agent
In May, the Azure SRE Agent was introduced - an AI-powered Site Reliability Engineering (SRE) assistant built to help customers identify, diagnose, and resolve issues across their Azure environments faster and with less manual effort. Today, we’re excited to highlight how the SRE Agent now extends these capabilities to Azure API Management (APIM) , delivering deep operational visibility, guided troubleshooting, and intelligent remediation for customers running critical APIs at scale. API Management sits at the center of API application architectures, acting as a unified entry point for services, enforcing security, transforming requests, and routing traffic to backends. Ensuring the reliability of this layer is crucial - but as systems grow more distributed, it becomes harder to isolate failures, detect misconfigurations, or trace degraded performance to its root cause. The SRE Agent helps APIM users stay ahead of these challenges by providing both diagnostics and remediation tailored for API Management environments. You can ask the SRE agent direct API Management questions or concerns such as: “My API Management is giving me 503 errors” “We updated our policies yesterday, and now the backend is timing out.” “Can you help me figure out why requests to our billing API are failing?” “Show me recent changes to our APIM instance.” “What’s the failure rate on our orders operation this week?” Proactively Monitor API Management App Health The SRE Agent continuously monitors the overall health of your API Management service. It tracks key metrics such as CPU utilization, latency, error rates, and availability over time, surfacing any abnormal patterns and offering insight into capacity. This helps teams anticipate issues before they impact users and plan for scaling with confidence. Visualize Backend Connections and Health One of the most valuable APIM capabilities introduced with the agent is backend mapping. The agent can identify which backend services each API operation routes to, and visualize the health of those backends. This makes it much easier to answer operational questions like: “Which backend is responsible for the spike in errors on my /checkout API?” “Are there any timeouts happening from APIM to service X?” Drill into Backend App Issues If the root cause lies in a backend application - whether it's a service hosted in Azure Container Apps, Azure Functions Apps App Service, or another compute platform - the SRE Agent can go further. It analyzes backend-specific metrics such as memory and CPU usage, response time distribution, recent deployments, and any logged exceptions. The agent correlates this backend behavior with the observed degradation at the API Management layer to provide a full stack view of what’s happening. For example: “Your backend container app failed 37% of requests in the last hour due to out-of-memory errors. This correlated with a 5xx spike at the /stock/check API operation.” Detect and Fix Configuration Issues The SRE Agent also helps uncover common configuration issues that lead to downtime or silent failures, including: Malformed API policies Missing or misapplied network rules (NSGs, VNet) Incorrect scaling configuration or quota enforcement But it doesn’t stop at diagnostics. Where safe and possible, the agent can also perform remediation with your approval - for example, by adjusting NSG rules, scaling your API Management, etc. Built for Teams that Depend on APIM If API Management is critical to your infrastructure, the SRE Agent gives you an extra layer of confidence - offering the clarity and tooling needed to maintain uptime, reduce operational overhead, and catch issues before they escalate. The APIM-specific capabilities of SRE Agent are now available, and can be used in any SRE Agent resource (currently in preview). Signup for preview access We’re excited to bring this level of intelligence and automation to APIM, and we’re looking forward to your feedback as we continue to evolve the experience. Additional resources Azure SRE Agent overview (preview) | Microsoft Learn Introducing Azure SRE Agent | Microsoft Community HubAzure API Management Your Auth Gateway For MCP Servers
The Model Context Protocol (MCP) is quickly becoming the standard for integrating Tools 🛠️ with Agents 🤖 and Azure API Management is at the fore-front, ready to support this open-source protocol 🚀. You may have already encountered discussions about MCP, so let's clarify some key concepts: Model Context Protocol (MCP) is a standardized way, (a protocol), for AI models to interact with external tools, (and either read data or perform actions) and to enrich context for ANY language models. AI Agents/Assistants are autonomous LLM-powered applications with the ability to use tools to connect to external services required to accomplish tasks on behalf of users. Tools are components made available to Agents allowing them to interact with external systems, perform computation, and take actions to achieve specific goals. Azure API Management: As a platform-as-a-service, API Management supports the complete API lifecycle, enabling organizations to create, publish, secure, and analyze APIs with built-in governance, security, analytics, and scalability. New Cool Kid in Town - MCP AI Agents are becoming widely adopted due to enhanced Large Language Model (LLM) capabilities. However, even the most advanced models face limitations due to their isolation from external data. Each new data source requires custom implementations to extract, prepare, and make data accessible for any model(s). - A lot of heavy lifting. Anthropic developed an open-source standard - the Model Context Protocol (MCP), to connect your agents to external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet through e.g. APIs). MCP Hosts: LLM applications such as chat apps or AI assistant in your IDEs (like GitHub Copilot in VS Code) that need to access external capabilities MCP Clients: Protocol clients that maintain 1:1 connections with servers, inside the host application MCP Servers: Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients MCP Protocol: Transport layer in the middle At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it is going to connect to the MCP server. The MCP server will then connect to for example a database or an API. MCP hosts and servers will connect with each other through the MCP protocol. You can create your own custom MCP Servers that connect to your or organizational data sources. For a quick start, please visit our GitHub repository to learn how to build a remote MCP server using Azure Functions without authentication: https://aka.ms/mcp-remote Remote vs. Local MCP Servers The MCP standard supports two modes of operation: Remote MCP servers: MCP clients connect to MCP servers over the Internet, establishing a connection using HTTP and Server-Sent Events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth. Local MCP servers: MCP clients connect to MCP servers on the same machine, using stdio as a local transport method. Azure API Management as the AI Auth Gateway Now that we have learned that MCP servers can connect to remote services through an API. The question now rises, how can we expose our remote MCP servers in a secure and scalable way? This is where Azure API Management comes in. A way that we can securely and safely expose tools as MCP servers. Azure API Management provides: Security: AI agents often need to access sensitive data. API Management as a remote MCP proxy safeguards organizational data through authentication and authorization. Scalability: As the number of LLM interactions and external tool integrations grows, API Management ensures the system can handle the load. Security remains to be a critical piece of building MCP servers, as agents will need to securely connect to protected endpoints (tools) to perform certain actions or read protected data. When building remote MCP servers, you need a way to allow users to login (Authenticate) and allow them to grant the MCP client access to resources on their account (Authorization). MCP - Current Authorization Challenges State: 4/10/2025 Recent changes in MCP authorization have sparked significant debate within the community. 🔍 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 with the Authorization Changes: The MCP server is now treated as both a resource server AND an authorization server. This dual role has fundamental implications for MCP server developers and runtime operations. 💡 𝗢𝘂𝗿 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: To address these challenges, we recommend using 𝗔𝘇𝘂𝗿𝗲 𝗔𝗣𝗜 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 as your authorization gateway for remote MCP servers. 🔗For an enterprise-ready solution, please check out our azd up sample repo to learn how to build a remote MCP server using Azure API Management as your authentication gateway: https://aka.ms/mcp-remote-apim-auth The Authorization Flow The workflow involves three core components: the MCP client, the APIM Gateway, and the MCP server, with Microsoft Entra managing authentication (AuthN) and authorization (AuthZ). Using the OAuth protocol, the client starts by calling the APIM Gateway, which redirects the user to Entra for login and consent. Once authenticated, Entra provides an access token to the Gateway, which then exchanges a code with the client to generate an MCP server token. This token allows the client to communicate securely with the server via the Gateway, ensuring user validation and scope verification. Finally, the MCP server establishes a session key for ongoing communication through a dedicated message endpoint. Diagram source: https://aka.ms/mcp-remote-apim-auth-diagram Conclusion Azure API Management (APIM) is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). In this blog, we've emphasized the simplicity of connecting AI agents to various data sources through MCP, streamlining previously complex implementations. Given the critical role of secure access to platforms and services for AI agents, APIM offers robust solutions for managing OAuth tokens and ensuring secure access to protected endpoints, making it an invaluable asset for enterprises, despite the challenges of authentication. API Management: An Enterprise Solution for Securing MCP Servers Azure API Management is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). It is designed to help you to securely expose your remote MCP servers. MCP servers are still very new, and as the technology evolves, API Management provides an enterprise-ready solution that will evolve with the latest technology. Stay tuned for further feature announcements soon! Acknowledgments This post and work was made possible thanks to the hard work and dedication of our incredible team. Special thanks to Pranami Jhawar, Julia Kasper, Julia Muiruri, Annaji Sharma Ganti Jack Pa, Chaoyi Yuan and Alex Vieira for their invaluable contributions. Additional Resources MCP Client Server integration with APIM as AI gateway Blog Post: https://aka.ms/remote-mcp-apim-auth-blog Sequence Diagram: https://aka.ms/mcp-remote-apim-auth-diagram APIM lab: https://aka.ms/ai-gateway-lab-mcp-client-auth Python: https://aka.ms/mcp-remote-apim-auth .NET: https://aka.ms/mcp-remote-apim-auth-dotnet On-Behalf-Of Authorization: https://aka.ms/mcp-obo-sample 3rd Party APIs – Backend Auth via Credential Manager: Blog Post: https://aka.ms/remote-mcp-apim-lab-blog APIM lab: https://aka.ms/ai-gateway-lab-mcp YouTube Video: https://aka.ms/ai-gateway-lab-demo
