Azure API Management Your Auth Gateway For MCP Servers
The Model Context Protocol (MCP) is quickly becoming the standard for integrating Tools š ļø with Agents š¤ and Azure API Management is at the fore-front, ready to support this open-source protocol š. 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. š šš²š ššµš®š¹š¹š²š»š“š²š 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. š” š¢ššæ š¦š¼š¹ššš¶š¼š»: To address these challenges, we recommend using ššššæš² šš£š š š®š»š®š“š²šŗš²š»š as your authorization gateway for remote MCP servers. šFor 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 ā 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-demoBuild. 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? š¤ 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" ā 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 š” Your remote MCP server can be discoverable on API Centerās 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 Centerās MCP Discovery page. Early Partners: Atlassian ā Connect to Jira and Confluence for issue tracking and documentation Box ā Use Box to securely store, manage and share your photos, videos, and documents in the cloud Neon ā Manage and query Neon Postgres databases with natural language Pipedream ā Add 1000s of APIs with built-in authentication and 10,000+ tools to your AI assistant or agent - coming soon - Stripe ā 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? š 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, weāre 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: āMy API Management is giving me 503 errorsā āWe updated our policies yesterday, and now the backend is timing out.ā āCan you help me figure out why requests to our billing API are failing?ā āShow me recent changes to our APIM instance.ā āWhatās the failure rate on our orders operation this week?ā 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: āWhich backend is responsible for the spike in errors on my /checkout API?ā āAre there any timeouts happening from APIM to service X?ā 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 whatās happening. For example: āYour 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.ā 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 doesnāt 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 Weāre excited to bring this level of intelligence and automation to APIM, and weāre 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 HubIntroducing Agent in a Day
Looking for a jumpstart on how to build Agents? Confused by the plethora of options when building Agents? You have come to the right place. In May 2025, the Logic Apps team introduced Agent Loop which provides the ability to build Autonomous or Conversational agents in Logic Apps. This gives customers an easy-to-use agent building design surface, the ability to deploy your agent to a Azure and integrate with Azure AI Foundry. Azure represents an enterprise-ready platform that addresses your organizational requirements including VNET integration, Private Endpoint support, Managed Identity and gives you several scaling options. Sounds great? It does, but how can I get started? This is where Logic Apps Agent in a Day comes in. We have recently published a step-by-step lab guide that will help you build an IT Support Agent that uses ServiceNow as the IT Service Management tool. The guide is available here: https://aka.ms/la-agent-in-a-day and we have included an Instructor Slide Deck in Module 1. Agent in a Day represents a fantastic opportunity for customers to participate in hackathon-style contests where attendees learn how to build agents and then can apply them to their unique business use cases. For Partners, Agent in a Day represents a great way to engage your customers by building agents with them and uncovering new use cases. Have any feedback or ideas on how to make this better? Feel free to send me a DM and we can discuss further.
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, weāre 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 toolsāsuch as APIsāvia a standardized, JSON-RPC-based interface. With this release, Azure empowers you to operationalize your APIs for AI integrationāsecurely, 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 ātools,ā 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 serversāwithout 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 toolsāoffering 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 APIMās 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 APIās 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: ā¤ 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 capabilityāserving 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 serversāensuring 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. Whatās next These new previews are just the beginning. We're already working on: Azure API Management (APIM) Passthrough MCP server support Weāre enabling APIM to act as a transparent proxy between your APIs and AI agentsāno 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. Weāre 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 ā The Azure API Management & API Center Teamsš¢Announcing agent loop: Build AI Agents in Azure Logic Apps š¤
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 ā 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 ā itās a new action type that brings together your AI model of choice, domain-specific tools, and enterprise knowledge sources. Whether youāre 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 ā 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 ā 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 agentās 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 ā 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 tasksāespecially 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 ā 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 isnāt 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 ā for instance, pausing to get a managerās approval or to ask for clarification ā leveraging Logic Appsā 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 ā whether thatās gathering more information, calling a specific connector, or formulating an answer. This step is essentially the AI āplanningā its next action based on the goal youāve 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 Appsā extensive connector library, the agent has a rich toolbox at its disposal. Each action is executed as a Logic Apps step, meaning itās 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 ā 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 ā 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 ā 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 agentās decisions and actions. Every run is logged in the workflow history, with data stored within the customerās own network and storage boundaries. The Agent Chat view provides insights into the agentās 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 Azureās 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 ā 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 canāt wait to see what you will build with Agent Loop! This is just the beginning. Weāre actively investing in new capabilities that are planned for release soon Multi-agent Hand-off Support ā 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 ā A2A is a communication standard that defines how autonomous agents exchange messages, share context, and coordinate actions in a secure and structured way. Itās 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 userās behalf. Contact Us Have feedback or questions about Agent Loop? Weād 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.Azure API Management Turns 10: Celebrating a Decade of Customer-Driven Innovation and Success
This September marks a truly special occasion: Azure API Management turns 10! Since our launch in 2014, we've been on an incredible journey, transforming how businesses connect, scale and secure their digital ecosystems. As the first cloud provider to integrate API management into its platform, Azure has led the way in helping organizations seamlessly navigate the evolving digital landscape.š 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 tasksāit'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 industriesāHealthcare, Retail, Energy, Financial Services, and moreāare embracing it to reimagine how agents collaborate with humans, tools, and workflows. Today, thousands of customersāfrom startups to large enterprisesāare 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 teamās 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 automationāessential for governed, regulated, and strictly defined processes where consistency and auditability are paramount. Yet in todayās 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. Thatās where our new agentic workflow capabilities come inānot 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 expertsāpreserving 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 optionsāplus 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 Weāre excited to announce that Logic Apps now supports Conversational Agents in workflowsāa 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 afterthoughtāit'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 injectionāmaking 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 permissionsānot 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 computationallyāprocessing 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 flowāshowing 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 needsāit'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 anywhereāgiving 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 āyour hub for AI labs, workshops, and tutorials. Whether youāre 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. Weāre continuously expanding these capabilities and welcome your feedback at or https://aka.ms/AgentLoopFeedback
