Hosted MCP Servers in Connector Namespace (Preview)
Imagine you've built an agent and you want to give it access to tools via MCP servers. Local servers won't work because your agent can't connect to them in production. Wouldn't it be great if you could quickly stand up secure, enterprise-ready remote MCP servers that your agent can use? This is what Connector Namespace enables. Among other capabilities, the namespace provides a feature called hosted MCP servers that lets you deploy remote MCP servers in minutes. Pick a server from the catalog, deploy it and your agents can discover and call its tools immediately, with infrastructure, deployment, scaling, observability, authentication, and more handled by the platform. Why hosted MCP? Self-hosting MCP servers comes with real operational cost: infrastructure, authentication, monitoring, scaling, availability, and debugging are all on you. For servers that expose standard capabilities like database access or browser automation, that's undifferentiated work that slows you down. Hosted MCP servers shift that burden to the platform, offering a fully managed experience so you can just pick a server and let the platform handle everything else: Hosted MCP server Self-hosted MCP server Setup Deploy from catalog in minutes Build/find server, deploy to your own infra, wire up networking Scaling Platform-managed, scales automatically You configure and manage scaling (VMs, containers, load balancers) Auth Inbound and outbound auth handled by the platform You configure OAuth, managed identity, or OBO end-to-end Observability One-click App Insights integration You set up logging, metrics, and alerting yourself Cold starts Platform manages server lifecycle You manage warm-up, health checks, and process restarts Availability Platform-managed uptime, health monitoring, and automatic recovery You own high availability, e.g. failover and redundancy How it works When you deploy a hosted MCP server, the namespace: Pulls the pre-built server image from the catalog. Provisions the runtime environment with your configuration. Exposes a secure MCP endpoint that agents and MCP clients can connect to. Handles scaling, health monitoring, and authentication. Public preview feature highlight Supported servers During public preview, a curated set of hosted MCP servers is available. The catalog expands over time based on demand. Server What it does Playwright Browser automation tools for web navigation, screenshots, and interaction Azure SQL Exposes SQL operations as MCP tools through Data API builder, enabling AI agents to interact with SQL databases through a controlled, secure contract with entity abstraction, RBAC, and caching. If there's a server you'd like to see in the catalog, file an issue at aka.ms/hosted-mcp-github. Support for publishing custom-built MCP servers to the catalog is planned for the future. Authentication Hosted MCP servers involve two authentication boundaries: Inbound (client → server): OAuth-based authentication with Microsoft Entra ID. Connections from GitHub Copilot in VS Code work out of the box. Outbound (server → downstream service) The server authenticates to the downstream service using either managed identity or on-behalf-of (OBO) flow. You choose the approach during deployment, and the platform handles the rest, including credential management and token exchange. Observability Hosted MCP servers integrate with Azure Application Insights], so you can monitor server health without setting up your own logging infrastructure. After deployment, you can enable monitoring by providing your Application Insights connection string. Once configured, logs and metrics from the server flow directly into your Application Insights resource, where you can search, filter, and analyze them. Get started Quickstart: Create a hosted MCP server in Connector Namespace Hosted MCP overview: Hosted MCP servers in Connector Namespace Connector Namespace overview: What is Connector Namespace? Try it out and let us know what you think! File feedback and feature requests at aka.ms/hosted-mcp-github. What's next Hosted MCP servers are in public preview and the team is actively working to improve the experience. We're looking for your feedback to help shape what comes next. Some areas we're prioritizing: Expanding the server catalog: adding more servers based on demand and community requests Region availability: expanding regional coverage beyond the current preview regions VNet support: deploying Hosted MCP servers inside virtual networks with private endpoints Custom server images: support for bringing your own MCP server images to the catalog Tool-level access control: fine-grained permissions and throttling at the individual tool levelAzure Connector Namespaces: managed integration for any Azure compute
The integration tax nobody budgets for It is always a simple task on paper: connect apps to the systems the business actually runs on — SharePoint, Salesforce, SAP, Outlook — and get back to building features. What gets in the way is rarely the business logic. It's the plumbing. You write a custom API client for each service. You wire up OAuth flows and then babysit token refresh. You add retry policies, handle throttling, page through results, and stand up webhook subscriptions you now have to keep alive. None of that is the feature. All of it is on you. Historically, if you wanted that work done for you, the answer was a workflow engine. That's great when you want a workflow — but a lot of apps just want to call an action or react to an event from code they already have, running on the compute they already use. That's the gap Azure Connector Namespace fills. What is Azure Connector Namespace? Azure Connector Namespace is a fully managed integration service that hosts a catalog of prebuilt, reusable connectors and MCP servers that your apps consume through a consistent programming model. Instead of writing and operating a client for each system, you create a connection once and call typed operations from your code. The namespace handles authentication, credential rotation, polling, webhook delivery, retries, throttling, and error handling on your behalf. Worth saying clearly, because people ask: a connector namespace is independent of Azure Logic Apps. It doesn't require, use, or change anything in Logic Apps, and the Logic Apps connectors gallery keeps working separately for workflows. Connector Namespace is the integration path for compute that doesn't run on a workflow engine — your Functions, Container Apps, App Service, and self-hosted services. Each connector exposes three kinds of surface through one shared connection model: Triggers — event subscriptions your app registers (a new email arrives, a record updates, a file lands in a folder). Actions — operations your app calls (send a message, read a row, upload a file). AI agent tools — the same operations, exposed to agents and Copilot through MCP servers. You call all of it from strongly typed SDKs for C# (Azure.Connectors.Sdk), Node.js (@azure/connectors), and Python (azure-connectors) — or over plain HTTP if a typed SDK isn't a fit. The building blocks Five concepts and you have the whole model: Concept What it is Connector namespace The Azure resource that hosts the connector runtime — loads and runs operations, maintains connection state and credentials, polls source systems, dispatches webhook events, and applies retry and diagnostic policies. Create it from the Azure portal, ARM/Bicep, or the CLI. Connector A prebuilt component for one service (SharePoint, Salesforce, SAP, Outlook). It abstracts the underlying API, auth protocol, pagination, and retry behavior so your code stays on business logic. Connection An authenticated, configured binding to an account or tenant. Connections are reusable — multiple apps and connectors can share one. Auth types: OAuth, API key, and Basic. MCP server A first-class resource that exposes tools to AI agents over the Model Context Protocol. Comes in managed and hosted flavors (more below). Connector SDKs Strongly typed clients for C#, Node.js, and Python that share the same catalog, connection model, telemetry, and retry semantics. Or call connectors over HTTP. What you can actually do with it The point of all this is the scenarios it unlocks. A few that show the range: Scenario What it looks like Process documents and content An Azure Function uses SharePoint connector operations to detect new or updated files, processes them, and writes results back to SharePoint. Monitor events from external services An Azure Container App uses a Salesforce trigger to receive events about new leads as they're created. Automate productivity A Node.js app uses Outlook operations to read and send email — reusing a connection another app already owns. Ground AI and agentic workloads A Python service calls connector actions to enrich model output with data from business systems. Reuse existing app code ASP.NET, Node.js, and Python services use managed integrations with no workflow engine in the call path. Publish connectors to agents Turn any connector into an MCP server in one step so Copilot and other agents can call it as a tool. Connections: authenticate once, reuse everywhere Connections are where the Logic Apps connector ecosystem pays off. You get the same broad catalog of first-party Azure services and popular SaaS apps — built on years of connector investment — without bringing a workflow engine along for the ride. You create a connection to a service, authenticate it once, and then any number of apps and connectors reuse it. Creating one is deliberately simple, which is the point: In the Connector Namespaces portal (connectors.azure.com), open your namespace and select Connections > Create connection. Find and select the connector — say, Office 365 Outlook. Give the connection a clear, specific name so it's easy to pick later. Sign in to authorize, and complete any extra steps the service requires. Confirm the connection shows as healthy on the Overview page — it's now ready for your apps to use. Supported authentication types today are OAuth, API key, and Basic. And because the namespace stores and rotates the credentials, your app never touches a raw secret. Triggers: deliver events to the compute you already run A trigger is an event subscription your app registers on a connector — new email, updated record, new file. When the source system raises that event, the namespace delivers the payload to your compute. And it does the hard part for you: it manages polling schedules and webhook registration based on what the underlying service supports, so you don't stand up or maintain subscription infrastructure. Your app can receive those events running on: Azure Container Apps Sandboxes Azure Functions Direct HTTP — App Services or self-hosted ASP.NET, Node.js, or Python on AKS or VMs, through the same connector namespace. Two details that matter in practice: a trigger is defined independently of any specific app, and multiple apps can subscribe to the same trigger event over the same connection. Actions, for contrast, run synchronously when your app calls them; trigger delivery uses webhooks or pull-based subscriptions depending on the connector and source service. You can learn more about how to use the Connectors SDK to inject connectors on Azure Functions here. MCP servers: turn connectors into agent tools This is the part I'm most excited about. An MCP server in your namespace exposes tools that AI agents — Copilot, custom agents, any MCP-aware client — can discover and call, using the same connection model as everything else. That's how you put your line-of-business systems directly in front of an agent without writing tool wrappers or standing up hosting. There are two ways to get one. Managed MCP servers Take any connector in your namespace and publish it as an MCP server in a single step. The namespace builds and configures the server — tool definitions, lifecycle, runtime — and the only thing you do is authenticate the underlying connection. If you can create a connection, you can give an agent a tool. Hosted MCP servers Sometimes you want a ready-made server rather than one projected from a connector. Hosted MCP servers are pre-built images from a curated catalog that the namespace runs in dedicated compute it provisions for you. You own the configuration; the platform handles hosting, scaling, networking, lifecycle, dependencies, health monitoring, and credentials. When you deploy one, the namespace pulls the image, provisions the runtime with your config, and exposes a secure MCP endpoint agents can connect to. The curated catalog during preview includes: Playwright — browser automation tools for navigation, screenshots, and page interaction. Azure SQL — SQL operations exposed as MCP tools through Data API builder, with entity abstraction, RBAC, and caching so agents work through a controlled, secure contract. It's a deliberately curated set today, and it expands over time based on demand. You can learn more about Hosted MCP Servers here. How agents authenticate Hosted MCP servers have two auth boundaries: Inbound (client to server) — OAuth with Microsoft Entra ID. Connections from GitHub Copilot in VS Code work out of the box; other MCP clients need a little extra config. Outbound (server to downstream system) — either a managed identity assigned to the namespace, or on-behalf-of (OBO) using the calling user's identity for delegated access. How it fits together End to end, the flow for connectors is short: Create a connector namespace resource in your subscription. Create one or more connections to the services you want — say, an OAuth connection to Microsoft 365. Your app — in Functions, Container Apps, App Service, or self-hosted compute — references the namespace and connection through a Connector SDK, then subscribes to triggers or calls actions. The namespace handles authentication, request signing, polling, webhook subscription, and retries. Your app gets back typed responses and event payloads. For MCP servers, it's the same shape: create the namespace, add a managed or hosted server from the catalog, authenticate the underlying connection, and agents can find the server, read its tool catalog, and invoke tools. Where you can run it Azure compute — App Service, Container Apps, and Functions can all consume connector operations. Self-hosted — any self-hosted service works too: ASP.NET, Node.js, or Python on AKS or Azure VMs. Agents, directly — Copilot extensions and MCP-aware clients call tools on MCP servers in your namespace without going through a separate compute layer; the namespace provides the compute that runs the servers. Security and governance, by default Credentials stay with the namespace — it stores, manages, and rotates them; your app never handles raw secrets. Network isolation — restrict access with virtual network integration and private endpoints. RBAC — control who can create connections, register triggers, and invoke actions. Observability — diagnostic logs and correlation IDs flow to Azure Monitor for end-to-end tracing across the namespace and your compute. Before you build: preview realities I'd rather you go in clear-eyed. While Connector Namespace is in preview, keep these in mind: Consideration What to expect No SLA Not recommended for production workloads during preview. Region availability Limited regions today; the list expands over time. Connector coverage High-usage and standard connectors first; enterprise connectors like SAP, IBM MQ, and Oracle Database follow in later waves. Identity API key and OAuth connections now; managed identity for connections comes later (and arrives earlier for select MCP servers). Versioning SDK and namespace runtime versions are paired during preview — expect breaking changes between milestones. Pricing The pricing model isn't finalized; the metering shape may change before GA. Try it, and tell us your feedback If you've ever shipped an integration and then spent the next quarter maintaining its plumbing, this is for you. The preview is open: create a namespace from the Azure portal, wire up a connection at connectors.azure.com, and call your first action or publish your first MCP server. It is easy to start here: Learn more: What is Azure Connector Namespace? Quickstart: Create and manage connector namespaces Create reusable connections in connector namespaces Hosted MCP servers in Azure Connector Namespace Related Blog Posts: Azure Functions - Connectors SDK Hosted MCP Server announcement Samples repositories: Using connectors SDK with Azure Functions This is a preview, which means your feedback genuinely shapes where it goes — which connectors come next, which MCP servers land in the catalog, where the rough edges are. Bring issues, feature requests and feedback to our GitHub page. I read it. Let's build the integration layer you actually want to use.
Ingesting Logs through Azure Private Link
Hi, We are currently using Azure Private Link within our environment and we are attempting to ingest logs into Log Analytics. When I reached out to Microsoft Support, it appears that the CCF connectors will not work using Private Link and the Azure Functions connectors are becoming depricated. Has anyone else run into this issue and what is the solution for getting logs into Sentinel through the Private Link, specifically API log sources? Did this require a custom app for each of these log sources or some sort of custom script that lives on an AMA host within the Private Link to ingest the logs? Any advice here would be greatly appeciated. Thank you,Logic Apps Newsletter - May 2026
In this issue: Ace Aviator of the Month News from our product group News from our community Ace Aviator of the Month May 2026's Ace Aviator: Yahya Ajwad What's your role and title? What are your responsibilities? I work as Chief Architect and AI Lead at Epical. My role is centered around technical leadership, architecture, and advisory mostly within cloud adoption and transformation programs. I help customers design and implement integration platforms. I also support customers in navigating the AI landscape, with a focus on how integration platforms impact AI readiness and how AI can be used to create value in the platforms we build. Internally at Epical, I lead our Microsoft and Azure cloud integration offering, as well as our CoreAI team and AI initiatives. Can you give us some insights into your day-to-day activities? One moment it is about how we can utilize AI to deliver things cheaper and faster without compromising security, governance and quality. Next, a customer contacts us with an undocumented BizTalk environment asking us what to do next (that's where the fun begins). Usually followed up by the question about how much does their future integration platform in Azure will cost down to the last cent (good luck answering that 😜 hint: the right answer is always: "less than BizTalk"). And hey, public cloud might not be good enough for their security, so thank God for private connectivity. I also spend time helping customers identify the best cloud integration strategies and patterns and for their business needs, choosing the right platforms and components for specific use cases, and ensuring that the platforms we design are as secure, scalable, maintainable, and cost-efficient as possible. Increasingly, this also includes sprinkling AI so bosses are happy (joking I'm a true AI believer myself). Internally, I support Epical with technical business development and help ensure that we stay relevant. What motivates and inspires you to be an active member of the Aviators/Microsoft community? I honestly believe that IT in general would never have been the same without communities and the willingness to share knowledge and support one another. The Logic Apps and Azure Integration community is especially unique because it is both practical and open. People share real experiences which makes it incredibly valuable when trying to solve real-world challenges. What motivates me is the opportunity to learn by sharing, receive feedback, and be part of a community where everyone contributes to each other’s growth. Between us, I'm here for the stickers ;) Looking back, what advice do you wish you had been given earlier? Be kind, stay curious and be helpful. Share what you know, even if it feels small or irrelevant. Those small insights often help others more than you think and that will definitely help you grow. Also, focus on learning the fundamentals. Tools and platforms change quickly, and what is popular today might not matter in a few weeks. Strong basics will always stay relevant. Keep learning and try new things all the time. What has helped you grow professionally? Being around kind, skilled, and generous people both in the community and at work who are willing to teach, challenge my thinking, and share their knowledge. Also, finding mentors who are open to mentor me, listen to all my questions even the silly ones, and who are willing to guide me and correct me when I’m hallucinating. If you had a magic wand that could create a feature in Logic Apps, what would it be? I don’t need to wish for one, you guys (Shout-out to Harold, Dyvia and the team) have already created it: Logic Apps Migration Assistant Agents. That stuff is definitely magic. News from our product group Network Connectivity Check APIs for Logic App Standard This post introduces built-in network troubleshooting APIs for Logic App Standard when integrated with a virtual network. Three endpoints—connectivityCheck, dnsCheck, and tcpPingCheck—let you validate end‑to‑end connectivity to services such as Azure SQL, Key Vault, Storage, Service Bus, and Event Hubs, perform DNS resolution, and test TCP reachability from the actual worker hosting your workflows. It covers supported provider types, credential options including Managed Identity and app settings, example payloads, and known limitations (e.g., SMB port 445 cannot be tested). Step-by-step guidance shows how to call the APIs via Azure API Playground or CLI to quickly isolate network issues. Service Bus SBMP Retirement: What BizTalk Server 2020 Customers Need to Know Azure Service Bus will retire the Service Bus Messaging Protocol (SBMP) on September 30, 2026, impacting BizTalk Server 2020 customers using the SB‑Messaging adapter. Microsoft has released a hotfix that adds Advanced Message Queuing Protocol (AMQP) support to the adapter for CU6 and CU7. With the hotfix, AMQP becomes the default transport while SBMP remains an opt‑in fallback; an updated hotfix based on the new Service Bus SDK is expected in June. Guidance includes migrating configurations to AMQP, installing and validating the hotfix in non‑production, and testing large message/file scenarios. The hotfix can be requested via support (KB5091375). Migrate Data Ingestion from Data Collector to Log Ingestion With the HTTP Data Collector API for Log Analytics deprecated and heading out of support in September 2026, this guide shows Logic Apps users how to move to the Log Ingestion API. It explains impacts—such as 403 errors for new Data Collector connections and missing data in newly created custom log tables—and provides a migration path using the HTTP action. Steps include creating a Data Collection Endpoint (DCE) and Data Collection Rule (DCR), deriving the full ingestion URL, mapping sample data to define schema, assigning the Monitoring Metrics Publisher role to the Logic App’s managed identity, and verifying success (HTTP 204). Introducing AI Skill Assessment in Azure API Center Azure API Center now includes automated AI skill assessment, providing governance and quality scoring for skills at scale using an “LLM‑as‑a‑judge” approach. The system evaluates outputs against defined criteria and ships with four default dimensions—Documentation Clarity, Help Completeness, Discoverability, and Safe Usage—each scored 1–5 with configurable thresholds. Developers get detailed reports showing pass/fail, per‑dimension scores, structural checks, and schema validation, helping them decide which skills are production‑ready. Platform administrators can extend or customize criteria to match organizational standards. The feature centralizes oversight and reduces manual review effort, improving confidence when adopting AI skills across integration solutions. Introducing the plugin marketplace for Azure API Center Azure API Center adds a plugin marketplace endpoint (public preview) that exposes a version‑controlled catalog of organizational AI plugins—such as MCP servers and skills—directly from your API Center data plane. Developers can discover and install plugins from familiar tools like Claude Code and GitHub Copilot CLI using simple marketplace commands. The endpoint inherits the API Center portal’s authentication model, ensuring governance and security while platform teams control publication. The article explains the problem it addresses, the marketplace.git URL format, quick start commands, and documentation to enable the feature—streamlining how teams curate, manage, and consume AI plugins in enterprise environments. News from our community Control the Initial State of Logic Apps Standard Workflows Post by Sandro Pereira This tip explains how to prevent Logic Apps Standard workflows from auto-starting after deployment, a risky default in production. Unlike Consumption, Standard doesn’t expose a state property in ARM. Instead, each workflow’s initial state is controlled via App Service application settings using the pattern “Workflows.<WorkflowName>.FlowState=Disabled.” The post shows how to define these keys in local.settings.json (or pipelines/Bicep), deploy with workflows disabled, and enable them when ready. It also notes acceptable values (Disabled/disabled) and clarifies that omitting the keys leaves workflows enabled by default—reducing unwanted executions and noisy alerts. 10 Azure Function App Limitations for Enterprise Integration Post by Tarun Garg The post outlines ten reasons Azure Function Apps may be a poor fit for enterprise integration workloads. Issues include cold-start latency, limited orchestration and state management, operational complexity at scale, and the need to hand-roll observability. It also highlights security and network isolation challenges, cost variability under heavy throughput, strong dependence on custom code, risks around versioning and breaking changes, and insufficient governance controls for integration use cases. The takeaway: Function Apps excel at granular compute, but integration programs often benefit from managed orchestration layers and patterns better aligned to enterprise requirements. Logic Apps Standard: how accidentally blocking access from Edge results in CORS errors Post by Şahin Özdemir Şahin Özdemir explains a troubleshooting case where Logic Apps Standard action inputs/outputs stopped loading in the Azure portal, appearing like a CORS issue. The root cause was a blocked “Local Network Access” permission in Microsoft Edge, not misconfigured CORS. The article advises checking Edge’s site permissions and re-enabling local network access before diving into CORS diagnostics. By validating browser settings first, engineers can avoid unnecessary changes to integration apps and resolve portal rendering failures quickly—saving time and reducing confusion when workflow views suddenly fail to load. Logic apps – Handling AND OR conditions Post by Anitha Eswaran Anitha Eswaran explains how to correctly implement combined AND/OR logic in Azure Logic Apps when the designer view becomes confusing. Using a real example—validating item numbers—she shows how to check for empty values or specific suffixes (W/WN) and when to terminate processing. The article demonstrates building expressions to explicitly control evaluation order and outcomes, avoiding unintended behavior from default AND logic. Practical screenshots and expression snippets help readers structure conditions, handle arrays from trigger data, and create maintainable workflows that reflect real business rules. Why Enterprises Are Migrating from Logic Apps (Consumption) to Logic Apps (Standard): Practical Insights from the Field Post by Kunal Saha Kunal Saha outlines why organizations reach an inflection point where Logic Apps Standard becomes a better fit than Consumption. Drawing from field experience, he highlights drivers like consolidated app-level management, richer local development workflows, environment isolation, and cost predictability for sustained workloads. The piece discusses when per-execution billing ceases to be efficient, how Standard’s hosting model supports enterprise governance, and migration considerations around triggers, connectors, and stateful patterns. The article encourages teams to evaluate workload characteristics and operational needs to determine the right time to modernize to Standard. Event Debouncing with Logic Apps and Azure Table Storage Post by Daniel Jonathan This article presents an event debouncing pattern for webhook-heavy systems using three Logic Apps and a single Azure Table Storage table. Incoming events are buffered by upserting rows keyed by entity ID, ensuring only the latest state is retained. A timer-driven workflow processes pending rows after a cooldown window, fetches fresh state from the source, and calls downstream APIs, deleting entries on success or resetting on failure. Benefits include implicit deduplication, reduced downstream load, and operational transparency in Storage Explorer. The pattern suits moderate scale without Service Bus, with caveats for strict ordering or very high throughput scenarios. XML to JSON in Logic Apps: Fixing the “Object vs Array” Trap Post by Prashant Singh Prashant Singh explains a common pitfall when converting XML to JSON in Logic Apps: json(xml(...)) yields an array when multiple nodes exist, but a single object when only one node is present—breaking For each loops. He outlines three remedies: debatch directly with xpath() to always return a node set; wrap the target node with array() to normalize object/array differences; or use coalesce() with array() to safely handle missing nodes. With clear examples and expressions, the post helps engineers avoid brittle assumptions and build resilient workflows that handle single, multiple, or absent records cleanly. DevUP Talks 02 - 2026 Q1 Reflections with Ahmed Bayoumy, Sebastian Meyer and Andrew Wilson Video by Mattias Lögdberg This 12‑minute panel discussion surveys how AI and automation are changing day‑to‑day engineering work. Mattias Lögdberg hosts Ahmed Bayoumy, Sebastian Meyer, and Andrew Wilson to share early field perspectives: the shift from experimentation to production, emerging testing responsibilities around AI‑assisted code, and how integration teams are adapting operating models and skills. The conversation favors practical observations over hype, offering a snapshot of what practitioners are seeing at the start of 2026. It’s a compact watch for leaders tracking real impacts rather than theoretical roadmaps. How low can you code? From ‘drag-and-drop’ dreams to try–catch reality Post by Sonny Gillissen Sonny Gillissen argues that early low‑code promised simplicity but often obscured complexity with visual designers and limited tooling. He suggests AI can shift low‑code from diagramming boxes to describing intent—letting teams express business behavior in natural terms, with systems generating implementations. The piece calls for focusing on domain clarity, robust data/APIs, and guardrails over chasing more drag‑and‑drop. For integration engineers, it frames a path where Logic Apps and related platforms become orchestration shells around AI‑assisted specifications, improving maintainability without hiding the hard parts. Legacy Integration to Azure: 40% Cost Savings and Faster Delivery Post by Adaptiv (post by Simon Clendon) This piece outlines lessons from migrating legacy integration platforms to Microsoft Azure. It details the discovery work needed to untangle historical integrations, the diplomacy required with stakeholders, and the engineering patterns that de-risk cutovers. Highlights include modernizing HR workflows, establishing clear migration decision trees, and treating AI as a force multiplier rather than a silver bullet. The article emphasizes measurable outcomes—around 40% cost savings and faster delivery—while cautioning against underestimating analysis, testing, and organizational change, and recommending experienced partners to accelerate the journey. Using the Right Tool Is Not Over‑Engineering Post by Marcelo Gomes Marcelo Gomes argues that many integration failures stem from tool misalignment rather than flawed logic. Using a market‑stall analogy, he outlines when to rely on API Management for exposure and control, where Logic Apps should orchestrate rather than absorb all work, and why Azure Storage underpins durable, production‑ready designs. The piece encourages architects to map responsibilities explicitly—governance at the edge, orchestration in workflows, compute where it belongs—so systems scale cleanly without masking complexity under a single service. Choosing fit‑for‑purpose components, he suggests, is discipline—not over‑engineering. Using Event Grid to detect deleted files and trigger Logic App Post by Sandro Pereira (author: Luis Rigueira) This walkthrough shows how to capture Azure Storage blob deletion events with Event Grid and invoke a Logic App for downstream actions like audit, recovery, or notifications. It explains why native Blob triggers don’t fire on delete, then sets up a System Topic on the storage account, configures a Logic App with the “When a resource event occurs” trigger for Microsoft.Storage.BlobDeleted, and parses the event payload for container, file name, content‑type, and timestamp. The post provides expressions and screenshots to build the flow end‑to‑end, enabling reliable reactions to file deletions without custom functions.
Migrate Data Ingestion from Data Collector to Log Ingestion
HTTP Data Collector API in Log Analytics workspaces is being deprecated, and will be totally out of support in September 2026. Data Collector actions in logic app using already created API connections (which uses workspace Id & Key) would still work against old custom log tables, however, newly created table will not be able to ingest data, although the connector would still succeed in logic app, but no data will be populated in newly created custom logs. In case new API connection is created for Data Collector action (using workspace Id & Key); these will fail with 403 - Forbidden action. Users should start using the Log Ingestion API to send data to custom tables, and this document will guide users on how to use Log Ingestion API in logic apps. Note: Azure portal currently is update so it doesn't show the Workspace keys in Log Analytics workspace page, however, Az CLI will still get the keys, but as stated, actions will fail with 403 when using them in Data Collector Action. Creating DCE & DCRs: To utilize the Log Ingestion API, Data Collection Endpoint & Data Collection Rule should be created first. DCE Creation is simple, from azure portal search for DCE, and then create a new one: For DCR creation, it can be either created from the DCR page in Azure Portal, or upon creating the custom log in Log Analytics workspace. DCR Popup You need to upload sample data file, so the custom log table has a schema, it needs to be JSON array. In case the sample log doesn't have a TimeGenerated field, you can easily add it using the mapping function as below: Add the below code in the Transformation box, then click run. Once you complete the DCR creation, we need to get the DCE full endpoint. Getting DCE Log Ingestion Full URL To get the full endpoint URL, please follow the below: 1. Get the DCE Log Ingestion URL from the DCE overview page: 2. On the DCR Page, get the immutable id for the DCR., then click on the JSON view of the DCR resource: 3. From the JSON view, get the stream name from the streamDeclarations field Now the full Log Ingestion URL is: DCE_URL/dataCollectionRules/{immutable_id}/streams/{streamName}?api-version=2023-01-01 It would be similar to: https://mshbou****.westeurope-1.ingest.monitor.azure.com/dataCollectionRules/dcr-7*****4e988bef2995cd52ae/streams/Custom-mshboulLogAPI_CL?api-version=2023-01-01 Granting Logic App MI needed IAM Roles: To call the ingestion endpoint using Logic Apps MI, we need to grant logic apps MI the role "Monitoring Metrics Publisher" over the DCR resource. To do this, open the DCR, from the blade choose Access Control (IAM), and then grant the logic app MI the role "Monitoring Metrics Publisher" Calling Log Ingestion Endpoint from logic apps: To call the ingestion endpoint from logic apps, we need to use the HTTP action, as below, the URI is the full DCE Endpoint we created before. Add the content-type headers, and the json body that contains the log data you want to send. For the authentication, it will be as below: Once executed, it should succeed, with status code 204. For more details on the Log Ingestion API, and the migration, please see our documentation: Migrate from the HTTP Data Collector API to the Log Ingestion API - Azure Monitor | Microsoft Learn Logs Ingestion API in Azure Monitor - Azure Monitor | Microsoft Learn Thanks.Logic Apps Aviators Newsletter - April 2026
In this issue: Ace Aviator of the Month News from our product group News from our community Ace Aviator of the Month April 2026's Ace Aviator: Marcelo Gomes What’s your role and title? What are your responsibilities? I’m an Integration Team Leader (Azure Integrations) at COFCO International, working within the Enterprise Integration Platform. My core responsibility is to design, architect, and operate integration solutions that connect multiple enterprise systems in a scalable, secure, and resilient way. I sit at the intersection of business, architecture, and engineering, ensuring that business requirements are correctly translated into technical workflows and integration patterns. From a practical standpoint, my responsibilities include: - Defining integration architecture standards and patterns across the organization - Designing end‑to‑end integration solutions using Azure Integration Services - Owning and evolving the API landscape (via Azure API Management) - Leading, mentoring, and supporting the integration team - Driving PoCs, experiments, and technical explorations to validate new approaches - Acting as a bridge between systems, teams, and business domains, ensuring alignment and clarity In short, my role is to make sure integrations are not just working — but are well‑designed, maintainable, and aligned with business goals. Can you give us some insights into your day‑to‑day activities and what a typical day looks like? My day‑to‑day work is a balance between technical leadership, architecture, and execution. A typical day usually involves: - Working closely with Business Analysts and Product Owners to understand integration requirements, constraints, and expected outcomes - Translating those requirements into integration flows, APIs, and orchestration logic - Defining or validating the architecture of integrations, including patterns, error handling, resiliency, and observability - Guiding developers during implementation, reviewing approaches, and helping them make architectural or design decisions - Managing and governing APIs through Azure API Management, ensuring consistency, security, and reusability - Unblocking team members by resolving technical issues, dependencies, or architectural doubts - Performing estimations, supporting planning, and aligning delivery expectations I’m also hands‑on. I actively build integrations myself — not just to help deliver, but to stay close to the platform, understand real challenges, and continuously improve our standards and practices. I strongly believe technical leadership requires staying connected to the actual implementation. What motivates and inspires you to be an active member of the Aviators / Microsoft community? What motivates me is knowledge sharing. A big part of what I know today comes from content shared by others — blog posts, samples, talks, community discussions, and real‑world experiences. Most of my learning followed a simple loop: someone shared → I tried it → I broke it → I fixed it → I learned. For me, learning only really completes its cycle when we share back. Explaining what worked (and what didn’t) helps others avoid the same mistakes and accelerates collective growth. Communities like Aviators and the Microsoft ecosystem create a space where learning is practical, honest, and experience‑driven — and that’s exactly the type of environment I want to contribute to. Looking back, what advice would you give to people getting into STEM or technology? My main advice is: start by doing. Don’t wait until you feel ready or confident — you won’t. When you start doing, you will fail. And that failure is not a problem; it’s part of the learning process. Each failure builds experience, confidence, and technical maturity. Another important point: ask questions. There is no such thing as a stupid question. Asking questions opens perspectives, challenges assumptions, and often triggers better solutions. Sometimes, a simple question from a fresh point of view can completely change how a problem is solved. Progress in technology comes from curiosity, iteration, and collaboration — not perfection. What has helped you grow professionally? Curiosity has been the biggest driver of my professional growth. I like to understand how things work under the hood, not just how to use them. When I’m curious about something, I try it myself, test different approaches, and build my own experience around it. That hands‑on curiosity helps me: - Develop stronger technical intuition - Understand trade‑offs instead of just following patterns blindly - Make better architectural decisions - Communicate more clearly with both technical and non‑technical stakeholders Having personal experience with successes and failures gives me clarity about what I’m really looking for in a solution — and that has been key to my growth. If you had a magic wand to create a new feature in Logic Apps, what would it be and why? I’d add real‑time debugging with execution control. Specifically, the ability to: - Pause a running Logic App execution - Inspect intermediate states, variables, and payloads in real time - Step through actions one by one, similar to a traditional debugger This would dramatically improve troubleshooting, learning, and optimization, especially in complex orchestrations. Today, we rely heavily on post‑execution inspection, which works — but real‑time visibility would be a huge leap forward in productivity and understanding. For integration engineers, that kind of feature would be a true game‑changer. News from our product group How to revoke connection OAuth programmatically in Logic Apps The post shows how to revoke an API connection’s OAuth tokens programmatically in Logic Apps, without using the portal. It covers two approaches: invoking the Revoke Connection Keys REST API directly from a Logic App using the 'Invoke an HTTP request' action, and using an Azure AD app registration to acquire a bearer token that authorizes the revoke call from Logic Apps or tools like Postman. Step-by-step guidance includes building the request URL, obtaining tokens with client credentials, parsing the token response, and setting the Authorization header. It also documents required permissions and a least-privilege custom RBAC role. Introducing Skills in Azure API Center This article introduces Skills in Azure API Center—registered, reusable capabilities that AI agents can discover and use alongside APIs, models, agents, and MCP servers. A skill describes what it does, its source repository, ownership, and which tools it is allowed to access, providing explicit governance. Teams can register skills manually in the Azure portal or automatically sync them from a Git repository, supporting GitOps workflows at scale. The portal offers discovery, filtering, and lifecycle visibility. Benefits include a single inventory for AI assets, better reuse, and controlled access via Allowed tools. Skills are available in preview with documentation links. Reliable blob processing using Azure Logic Apps: Recommended architecture The post explains limitations of the in‑app Azure Blob trigger in Logic Apps, which relies on polling and best‑effort storage logs that can miss events under load. For mission‑critical scenarios, it recommends a queue‑based pattern: have the source system emit a message to Azure Storage Queues after each blob upload, then trigger the Logic App from the queue and fetch the blob by metadata. Benefits include guaranteed triggering, decoupling, retries, and observability. As an alternative, it outlines using Event Grid with single‑tenant Logic App endpoints, plus caveats for private endpoints and subscription validation requirements. Implementing / Migrating the BizTalk Server Aggregator Pattern to Azure Logic Apps Standard This article shows how to implement or migrate the classic BizTalk Server Aggregator pattern to Azure Logic Apps Standard using a production-ready template available in the Azure portal. It maps BizTalk orchestration concepts (correlation sets, pipelines, MessageBox) to cloud-native equivalents: a stateful workflow, Azure Service Bus as the messaging backbone, CorrelationId-based grouping, and FlatFileDecoding for reusing existing BizTalk XSD schemas with zero refactoring. Step-by-step guidance covers triggering with the Service Bus connector, grouping messages by CorrelationId, decoding flat files, composing aggregated results, and delivering them via HTTP. A side‑by‑side comparison highlights architectural differences and migration considerations, aligned with BizTalk Server end‑of‑life timelines. News from our community Resilience for Azure IPaaS services Post by Stéphane Eyskens Stéphane Eyskens examines resilience patterns for Azure iPaaS workloads and how to design multi‑region architectures spanning stateless and stateful services. The article maps strategies across Service Bus, Event Hubs, Event Grid, Durable Functions, Logic Apps, and API Management, highlighting failover models, idempotency, partitioning, and retry considerations. It discusses trade‑offs between active‑active and active‑passive, the role of a governed API front door, and the importance of consistent telemetry for recovery and diagnostics. The piece offers pragmatic guidance for integration teams building high‑availability, fault‑tolerant solutions on Azure. From APIs to Agents: Rethinking Integration in the Agentic Era Post by Al Ghoniem, MBA This article frames AI agents as a new layer in enterprise integration rather than a replacement for existing platforms. It contrasts deterministic orchestration with agent‑mediated behavior, then proposes an Azure‑aligned architecture: Azure AI Agent Service as runtime, API Management as the governed tool gateway, Service Bus/Event Grid for events, Logic Apps for deterministic workflows, API Center as registry, and Entra for identity and control. It also outlines patterns—tool‑mediated access, hybrid orchestration, event+agent systems, and policy‑enforced interaction—plus anti‑patterns to avoid. DevUP Talks 01 - 2026 Q1 trends with Kent Weare Video by Mattias Lögdberg Mattias Lögdberg hosts Kent Weare for a concise discussion on early‑2026 trends affecting integration and cloud development. The conversation explores how AI is reshaping solution design, where new opportunities are emerging, and how teams can adapt practices for reliability, scalability, and speed. It emphasizes practical implications for developers and architects working with Azure services and modern integration stacks. The episode serves as a quick way to track directional changes and focus on skills that matter as agentic automation and platform capabilities evolve. Azure Logic Apps as MCP Servers: A Step-by-Step Guide Post by Stephen W Thomas Stephen W Thomas shows how to expose Azure Logic Apps (Standard) as MCP servers so AI agents can safely reuse existing enterprise workflows. The guide explains why this matters—reusing logic, tapping 1,400+ connectors, and applying key-based auth—and walks through creating an HTTP workflow, defining JSON schemas, connecting to SQL Server, and generating API keys from the MCP Servers blade. It closes with testing in VS Code, demonstrating how agents invoke Logic Apps tools to query live data with governance intact, without rewriting integration code. BizTalk to Azure Migration Roadmap: Integration Journey Post by Sandro Pereira This roadmap-style article distills lessons from BizTalk-to-Azure migrations into a structured journey. It outlines motivations for moving, capability mapping from BizTalk to Azure Integration Services, and phased strategies that reduce risk while modernizing. Readers get guidance on assessing dependencies, choosing target Azure services, designing hybrid or cloud‑native architectures, and sequencing workloads. The post emphasises that migration is not a lift‑and‑shift but a program of work aligned to business priorities, platform governance, and operational readiness. BizTalk Adapters to Azure Logic Apps Connectors Post by Michael Stephenson Michael Stephenson discusses how organizations migrating from BizTalk must rethink integration patterns when moving to Azure Logic Apps connectors. The post considers what maps well, where gaps and edge cases appear, and how real-world implementations often require re‑architecting around AIS capabilities rather than a one‑to‑one adapter swap. It highlights community perspectives and practical considerations for planning, governance, and operationalizing new designs beyond pure connector parity. Pro-Code Enterprise AI-Agents using MCP for Low-Code Integration Video by Sebastian Meyer This short video demonstrates bridging pro‑code and low‑code by using the Model Context Protocol (MCP) to let autonomous AI agents interact with enterprise systems via Logic Apps. It walks through the high‑level setup—agent, MCP server, and Logic Apps workflows—and shows how to connect to platforms like ServiceNow and SAP. The focus is on practical tool choice and architecture so teams can extend existing integration assets to agent‑driven use cases without rebuilding from scratch. Friday Fact: The Hidden Retry Behavior That Makes Logic Apps Feel Stuck Post by João Ferreira This Friday Fact explains why a Logic App can appear “stuck” when calling unstable APIs: hidden retry policies, exponential backoff, and looped actions can accumulate retries and slow runs dramatically. It lists default behaviors many miss, common causes like throttling, and mitigation steps such as setting explicit retry policies, using Configure run after for failure paths, and introducing circuit breakers for flaky backends. The takeaway: the workflow may not be broken—just retrying too aggressively—so design explicit limits and recovery paths. Your Logic App Is NOT Your Business Process (Here’s Why) Video by Al Ghoniem, MBA This short explainer argues that mapping Logic Apps directly to a business process produces brittle workflows. Real systems require retries, enrichment, and exception paths, so the design quickly diverges from a clean process diagram. The video proposes separating technical orchestration from business visibility using Business Process Tracking. That split yields clearer stakeholder views and more maintainable solutions, while keeping deterministic execution inside Logic Apps. It’s a practical reminder to design for operational reality rather than mirroring a whiteboard flow. BizTalk Server Migration to Azure Integration Services Architecture Guidance Post by Sandro Pereira A brief overview of Microsoft’s architecture guidance for migrating BizTalk Server to Azure Integration Services. The post explains the intent of the guidance, links to sections on reasons to migrate, AIS capabilities, BizTalk vs. AIS comparisons, and service selection. It highlights planning topics such as migration approaches, best practices, and a roadmap, helping teams frame decisions for hybrid or cloud‑native architectures as they modernize BizTalk workloads. Logic Apps & Power Automate Action Name to Code Translator Post by Sandro Pereira This post introduces a lightweight utility that converts Logic Apps and Power Automate action names into their code identifiers—useful when writing expressions or searching in Code View. It explains the difference between designer-friendly labels and underlying names (spaces become underscores and certain symbols are disallowed), why this causes friction, and how the tool streamlines the translation. It includes screenshots, usage notes, and the download link to the open-source project, making it a practical time-saver for developers moving between designer and code-based workflows. Logic Apps Consumption CI/CD from Zero to Hero Whitepaper Post by Sandro Pereira This whitepaper provides an end‑to‑end path to automate CI/CD for Logic Apps Consumption using Azure DevOps. It covers solution structure, parameterization, and environment promotion, then shows how to build reliable pipelines for packaging, deploying, and validating Logic Apps. The guidance targets teams standardizing delivery with repeatable patterns and governance. With templates and practical advice, it helps reduce manual steps, improve quality, and accelerate releases for Logic Apps workloads. Logic App Best practices, Tips and Tricks: #2 Actions Naming Convention Post by Sandro Pereira This best‑practices post focuses on action naming in Logic Apps. It explains why consistent, descriptive names improve readability, collaboration, and long‑term maintainability, then outlines rules and constraints on allowed characters. It shows common pitfalls—default names, uneditable trigger/branch labels—and practical tips for renaming while avoiding broken references. The guidance helps teams treat names as living documentation so workflows remain understandable without drilling into each action’s configuration. How to Expose and Protect Logic App Using Azure API Management (Whitepaper) Post by Sandro Pereira This whitepaper explains how to front Logic Apps with Azure API Management for governance and security. It covers publishing Logic Apps as APIs, restricting access, enforcing IP filtering, preventing direct calls to Logic Apps, and documenting operations. It also discusses combining multiple Logic Apps under a single API, naming conventions, and how to remove exposed operations safely. The paper provides step‑by‑step guidance and a download link to help teams standardize exposure and protection patterns. Logic apps – Check the empty result in SQL connector Post by Anitha Eswaran This post shows a practical pattern for handling empty SQL results in Logic Apps. Using the SQL connector’s output, it adds a Parse JSON step to normalize the result and then evaluates length() to short‑circuit execution when no rows are returned. Screenshots illustrate building the schema, wiring the content, and introducing a conditional branch that terminates the run when the array is empty. The approach avoids unnecessary downstream actions and reduces failures, providing a reusable, lightweight guard for query‑driven workflows. Azure Logic Apps Is Basically Power Automate on Steroids (And You Shouldn’t Be Afraid of It) Post by Kim Brian Kim Brian explains why Logic Apps feels familiar to Power Automate builders while removing ceilings that appear at scale. The article contrasts common limits in cloud flows with Standard/Consumption capabilities, highlights the designer vs. code‑view model, and calls out built‑in Azure management features such as versioning, monitoring, and CI/CD. It positions Logic Apps as the “bigger sibling” for enterprise‑grade integrations and data throughput, offering more control without abandoning the visual authoring experience. Logic Apps CSV Alphabetic Sorting Explained Post by Sandro Pereira Sandro Pereira describes why CSV headers and columns can appear in alphabetical order after deploying Logic Apps via ARM templates. He explains how JSON serialization and array ordering influence CSV generation, what triggers the sorting behavior, and practical workarounds to preserve intended column order. The article helps teams avoid subtle defects in data exports by aligning workflow design and deployment practices with how Logic Apps materializes CSV content at runtime. Azure Logic Apps Translation vs Transformation – Actions, Examples, and Schema Mapping Explained Post by Maheshkumar Tiwari Maheshkumar Tiwari clarifies the difference between translation (format change) and transformation (business logic) in Logic Apps, then maps each to concrete Azure capabilities. Using a purchase‑order scenario, he shows how to decode/encode flat files and EDI, convert XML↔JSON, and apply Liquid/XSLT, Select, Compose, and Filter Array for schema mapping and enrichment. A quick reference table ties common tasks to the right action, helping architects separate concerns so format changes don’t break business rules and workflow design remains maintainable.How to revoke connection OAuth programmatically in Logic Apps
There are multiple ways to revoke the OAuth of an API Connection other than clicking on the Revoke button in the portal: For using the "Invoke an HTTP request": Get the connection name: Create a Logic App (Consumption), use a trigger of your liking, then add the “Invoke an HTTP request” Action. Create a connection on the same Tenant that has the connection, then add the below URL to the action, and test it: https://management.azure.com/subscriptions/[SUBSCRIPTION_ID]/resourceGroups/[RESOURCE_GROUP]/providers/Microsoft.Web/connections/[NAME_OF_CONNECTION]/revokeConnectionKeys?api-version=2018-07-01-preview Your test should be successful. For using an App Registration to fetch the Token (which is how you can do this with Postman or similar as well): App Registration should include this permission: For your Logic App, or Postman, get the Bearer Token by calling this URL: https://login.microsoftonline.com/[TENANT_ID]/oauth2/v2.0/token With this in the Body: Client_Id=[CLIENT_ID_OF_THE_APP_REG]&Client_Secret=[CLIENT_SECRET_FROM_APP_REG]&grant_type=client_credentials&scope=https://management.azure.com/.default For the Header use: Content-Type = application/x-www-form-urlencoded If you’ll use a Logic App for this; Add a Parse JSON action, use the Body of the Get Bearer Token HTTP Action as an input to the Parse JSON Action, then use the below as the Schema: { "properties": { "access_token": { "type": "string" }, "expires_in": { "type": "integer" }, "ext_expires_in": { "type": "integer" }, "token_type": { "type": "string" } }, "type": "object" } Finally, add another HTTP Action (or call this in Postman or similar) to call the Revoke API. In the Header add “Authorization”key with a value of “Bearer” followed by a space then add the bearer token from the output of the Parse JSON Action. https://management.azure.com/subscriptions/[SUBSCRIPTION_ID]/resourceGroups/[RESOURCE_GROUP]/providers/Microsoft.Web/connections/[NAME_OF_CONNECTION]/revokeConnectionKeys?api-version=2018-07-01-preview If you want to use CURL: Request the Token Use the OAuth 2.0 client credentials flow to get the token: curl -X POST \ -H "Content-Type: application/x-www-form-urlencoded" \ -d "client_id=[CLIENT_ID_OF_APP_REG]" \ -d "scope= https://management.azure.com/.default" \ -d "client_secret=[CLIENT_SECRET_FROM_APP_REG]" \ -d "grant_type=client_credentials" \ https://login.microsoftonline.com/[TENANT_ID]/oauth2/v2.0/token The access_token in the response is your Bearer token. Call the Revoke API curl -X POST "https://management.azure.com/subscriptions/[SUBSCRIPTION_ID]/resourceGroups/[RESOURCE_GROUP]/providers/Microsoft.Web/connections/[NAME_OF_CONNECTION]/revokeConnectionKeys?api-version=2018-07-01-preview" \ -H "Authorization: Bearer <ACCESS_TOKEN>" \ -H "Content-Type: application/json" \ -d '{"key":"value"}' If you face the below error, you will need to grant Contributor Role to the App Registration on the Resource Group that contains the API Connection. (If you want “Least privilege” skip to below ) { "error": { "code": "AuthorizationFailed", "message": "The client '[App_reg_client_id]' with object id '[App_reg_object_id]' does not have authorization to perform action 'Microsoft.Web/connections/revokeConnectionKeys/action' over scope '/subscriptions/[subscription_id]/resourceGroups/[resource_group_id]/providers/Microsoft.Web/connections/[connection_name]' or the scope is invalid. If access was recently granted, please refresh your credentials." } } For “Least privilege” solution, create a Custom RBAC Role with the below Roles, and assign it to the App Registration Object ID same as above: { "actions": [ "Microsoft.Web/connections/read", "Microsoft.Web/connections/write", "Microsoft.Web/connections/delete", "Microsoft.Web/connections/revokeConnectionKeys/action" ] }
How to Access a Shared OneDrive Folder in Azure Logic Apps
What is the problem? A common enterprise automation scenario involves copying files from a OneDrive folder shared by a colleague into another storage service such as SharePoint or Azure Blob Storage using Azure Logic Apps. However, when you configure the OneDrive for Business – “List files in folder” action in a Logic App, you quickly run into a limitation: The folder picker only shows: Root directory Subfolders of the authenticated user’s OneDrive Shared folders do not appear at all, even though you can access them in the OneDrive UI This makes it seem like Logic Apps cannot work with shared OneDrive folders—but that’s not entirely true. Why this happens The OneDrive for Business connector is user‑context scoped. It only enumerates folders that belong to the signed-in user’s drive and does not automatically surface folders that are shared with the user. Even though shared folders are visible under “Shared with me” in the OneDrive UI, they: Live in a different drive Have a different driveId Require explicit identification before Logic Apps can access them How to access a shared OneDrive folder There are two supported ways to access a shared OneDrive directory from Logic Apps. Option 1: Use Microsoft Graph APIs (Delegated permissions) You can invoke Microsoft Graph APIs directly using: HTTP with Microsoft Entra ID (preauthorized) Delegated permissions on behalf of the signed‑in user This requires: Admin consent or delegated consent workflows Additional Entra ID configuration 📘 Reference: HTTP with Microsoft Entra ID (preauthorized) - Connectors | Microsoft Learn While powerful, this approach adds setup complexity. Option 2: Use Graph Explorer to configure the OneDrive connector Instead of calling Graph from Logic Apps directly, you can: Use Graph Explorer to discover the shared folder metadata Manually configure the OneDrive action using that metadata Step-by-step: Using Graph Explorer to access a shared folder Scenario A colleague has shared a OneDrive folder named “Test” with me, and I need to process files inside it using a Logic App. Step 1: List shared folders using Microsoft Graph In Graph Explorer, run the following request: GET https://graph.microsoft.com/v1.0/users/{OneDrive shared folder owner username}/drive/root/children 📘Reference: List the contents of a folder - Microsoft Graph v1.0 | Microsoft Learn ✅This returns all root-level folders visible to the signed-in user, including folders shared with you. From the response, locate the shared folder. You only need two values: parentReference.driveId id (folder ID) Graph explorer snippet showing the request sent to the API to list the files & folders shared by a specific user on the root drive Step 2: Configure Logic App “List files in folder” action In your Logic App: Add OneDrive for Business → List files in folder Do not use the folder picker Manually enter the folder value using this format: {driveId}.{folderId} Once saved, the action successfully lists files from the shared OneDrive folder. Step 3: Build the rest of your workflow After the folder is resolved correctly: You can loop through files Copy them to SharePoint Upload them to Azure Blob Storage Apply filters, conditions, or transformations All standard OneDrive actions now work as expected. Troubleshooting: When Graph Explorer doesn’t help If you’re unable to find the driveId or folderId via Graph Explorer, there’s a reliable fallback. Use browser network tracing Open the shared folder in OneDrive (web) Open Browser Developer Tools → Network Look for requests like: & folderId In the response payload, extract: CurrentFolderUniqueId → folder ID drives/{driveId} from the CurrentFolderSpItemUrl This method is very effective when Graph results are incomplete or filtered.Logic Apps Agentic Workflows with SAP - Part 1: Infrastructure
When you integrate Azure Logic Apps with SAP, the “hello world” part is usually easy. The part that bites you later is data quality. In SAP-heavy flows, validation isn’t a nice-to-have — it’s what makes the downstream results meaningful. If invalid data slips through, it can get expensive fast: you may create incorrect business documents, trigger follow-up processes, and end up in a cleanup path that’s harder (and more manual) than building validation upfront. And in “all-or-nothing” transactional patterns, things get even more interesting: one bad record can force a rollback strategy, compensating actions, or a whole replay/reconciliation story you didn’t want to own. See for instance Handling Errors in SAP BAPI Transactions | Microsoft Community Hub to get an idea of the complexity in a BizTalk context. That’s the motivation for this post: a practical starter pattern that you can adapt to many data shapes and domains for validating data in a Logic Apps + SAP integration. Note: For the full set of assets used here, see the companion GitHub repository (workflows, schemas, SAP ABAP code, and sample files). 1. Introduction Scenario overview The scenario is intentionally simple, but it mirrors what shows up in real systems: A Logic App workflow sends CSV documents to an SAP endpoint. SAP forwards the payload to a second Logic App workflow that performs: rule-based validation (based on pre-defined rules) analysis/enrichment (market trends, predictions, recommendations) The workflow either: returns validated results (or validation errors) to the initiating workflow, or persists outputs for later use For illustration, I’m using fictitious retail data. The content is made up, but the mechanics are generic: the same approach works for orders, inventory, pricing, master data feeds, or any “file in → decision out” integration. You’ll see sample inputs and outputs below to keep the transformations concrete. What this post covers This walkthrough focuses on the integration building blocks that tend to matter in production: Calling SAP RFCs from Logic App workflows, and invoking Logic App workflows from SAP function modules Using the Logic Apps SAP built-in trigger Receiving and processing IDocs Returning responses and exceptions back to SAP in a structured, actionable way Data manipulation patterns in Logic Apps, including: parsing and formatting inline scripts XPath (where it fits, and where it becomes painful). Overall Implementation A high-level view of the implementation is shown below. The source workflow handles end-to-end ingestion—file intake, transformation, SAP integration, error handling, and notifications—using Azure Logic Apps. The destination workflows focus on validation and downstream processing, including AI-assisted analysis and reporting, with robust exception handling across multiple technologies. I’ll cover the AI portion in a follow-up post. Note on AI-assisted development Most of the workflow “glue” in this post—XPath, JavaScript snippets, and Logic Apps expressions—was built with help from Copilot and the AI assistant in the designer (see Get AI-assisted help for Standard workflows - Azure Logic Apps | Microsoft Learn). In my experience, this is exactly where AI assistance pays off: generating correct scaffolding quickly, then iterating based on runtime behavior. I’ve also included SAP ABAP snippets for the SAP-side counterpart. You don’t need advanced ABAP knowledge to follow along; the snippets are deliberately narrow and integration-focused. I include them because it’s hard to design robust integrations if you only understand one side of the contract. When you understand how SAP expects to receive data, how it signals errors, and where transactional boundaries actually are, you end up with cleaner workflows and fewer surprises. 2. Source Workflow This workflow is a small, end‑to‑end “sender” pipeline: it reads a CSV file from Azure Blob Storage, converts the rows into the SAP table‑of‑lines XML shape expected by an RFC, calls Z_GET_ORDERS_ANALYSIS via the SAP connector, then extracts analysis or error details from the RFC response and emails a single consolidated result. The name of the data file is a parameter of the logic app. At a high level: Input: an HTTP request (used to kick off the run) + a blob name. Processing: CSV → array of rows → XML (…) → RFC call Output: one email containing either: the analysis (success path), or a composed error summary (failure path). The diagram below summarizes the sender pipeline: HTTP trigger → Blob CSV (header included) → rows → SAP RFC → parse response → email. Two design choices are doing most of the work here. First, the workflow keeps the CSV transport contract stable by sending the file as a verbatim list of lines—including the header—wrapped into … elements under IT_CSV . Second, it treats the RFC response as the source of truth: EXCEPTIONMSG and RETURN/MESSAGE drive a single Has errors gate, which determines whether the email contains the analysis or a consolidated failure summary. Step-by-step description Phase 0 — Trigger Trigger — When_an_HTTP_request_is_received The workflow is invoked via an HTTP request trigger (stateful workflow). Phase 1 — Load and split the CSV Read file — Read_CSV_orders_from_blob Reads the CSV from container onlinestoreorders using the blob name from @parameters('DataFileName'). The name of the data file is a parameter of the logic app. Split into rows — Extract_rows Splits the blob content on \r\n, producing an array of CSV lines. Design note: Keeping the header row is useful when downstream validation or analysis wants column names, and it avoids implicit assumptions in the sender workflow. Phase 2 — Shape the RFC payload Convert CSV rows to SAP XML — Transform_CSV_to_XML Uses JavaScript to wrap each CSV row (including the header line) into the SAP line structure and XML‑escape special characters. The output is an XML fragment representing a table of ZTY_CSV_LINE rows. Phase 3 — Call SAP and extract response fields Call the RFC — [RFC]_ Z_GET_ORDERS_ANALYSIS Invokes Z_GET_ORDERS_ANALYSIS with an XML body containing … built from the transformed rows. Note that a <DEST>...</DEST> element can also be provided to override the default value in the function module definition. Extract error/status — Save_EXCEPTION_message and Save_RETURN_message Uses XPath to pull: EXCEPTIONMSG from the RFC response, and the structured RETURN / MESSAGE field. Phase 4 — Decide success vs failure and notify Initialize output buffer — Initialize_email_body Creates the EmailBody variable used by both success and failure cases. Gate — Has_errors Determines whether to treat the run as failed based on: EXCEPTIONMSG being different from "ok", or RETURN / MESSAGE being non‑empty. Send result — Send_an_email_(V2) Emails either: the extracted ANALYSIS (success), or a concatenated error summary including RETURN / MESSAGE plus message details ( MESSAGE_V1 … MESSAGE_V4 ) and EXCEPTIONMSG . Note: Because the header row is included in IT_CSV , the SAP-side parsing/validation treats the first line as column titles (or simply ignores it). The sender workflow stays “schema-agnostic” by design. Useful snippets Snippet 1 — Split the CSV into rows split(string(body('Read_CSV_orders_from_blob')?['content']), '\r\n') Tip: If your CSV has a header row you don’t want to send to SAP, switch back to: @skip(split(string(body('Read_CSV_orders_from_blob')?['content']), '\r\n'), 1) Snippet 2 — JavaScript transform: “rows → SAP table‑of‑lines XML” const lines = workflowContext.actions.Extract_rows.outputs; function xmlEscape(value) { return String(value) .replace(/&/g, "&") .replace(//g, ">") .replace(/"/g, """) .replace(/'/g, "'"); } // NOTE: we don't want to keep empty lines (which can be produced by reading the blobs) // the reason being that if the recipient uses a schema to validate the xml, // it may reject it if it does not allow empty nodes. const xml = lines .filter(line => line && line.trim() !== '') // keep only non-empty lines .map(line => `<zty_csv_line><line>${xmlEscape(line)}</line></zty_csv_line>`) .join(''); return { xml }; Snippet 3 — XPath extraction of response fields (namespace-robust) EXCEPTIONMSG: @xpath(body('[RFC]_Call_Z_GET_ORDERS_ANALYSIS')?['content'], 'string( /*[local-name()="Z_GET_ORDERS_ANALYSISResponse"] /*[local-name()="EXCEPTIONMSG"])') RETURN/MESSAGE: @xpath(body('[RFC]_Call_Z_GET_ORDERS_ANALYSIS')?['content'], 'string( /*[local-name()="Z_GET_ORDERS_ANALYSISResponse"] /*[local-name()="RETURN"] /*[local-name()="MESSAGE"])') Snippet 4 — Failure email body composition concat( 'Error message: ', outputs('Save_RETURN_message'), ', details: ', xpath(body('[RFC]_Call_Z_GET_ORDERS_ANALYSIS')?['content'], 'string(//*[local-name()=\"MESSAGE_V1\"])'), xpath(body('[RFC]_Call_Z_GET_ORDERS_ANALYSIS')?['content'], 'string(//*[local-name()=\"MESSAGE_V2\"])'), xpath(body('[RFC]_Call_Z_GET_ORDERS_ANALYSIS')?['content'], 'string(//*[local-name()=\"MESSAGE_V3\"])'), xpath(body('[RFC]_Call_Z_GET_ORDERS_ANALYSIS')?['content'], 'string(//*[local-name()=\"MESSAGE_V4\"])'), '; ', 'Exception message: ', outputs('Save_EXCEPTION_message'), '.') 3. SAP Support To make the SAP/Logic Apps boundary simple, I model the incoming CSV as a table of “raw lines” on the SAP side. The function module Z_GET_ORDERS_ANALYSIS exposes a single table parameter, IT_CSV , typed using a custom line structure. Figure: IT_CSV is a table of CSV lines ( ZTY_CSV_LINE ), with a single LINE field ( CHAR2048 ). IT_CSV uses the custom structure ZTY_CSV_LINE , which contains a single component LINE ( CHAR2048 ). This keeps the SAP interface stable: the workflow can send CSV lines without SAP having to know the schema up front, and the parsing/validation logic can evolve independently. The diagram below shows the plumbing that connects SAP to Azure Logic Apps in two common patterns: SAP sending IDocs to a workflow and SAP calling a remote-enabled endpoint via an RFC destination. I’m showing all three pieces together—the ABAP call site, the SM59 RFC destination, and the Logic Apps SAP built-in trigger—because most “it doesn’t work” problems come down to a small set of mismatched configuration values rather than workflow logic. The key takeaway is that both patterns hinge on the same contract: Program ID plus the SAP Gateway host/service. In SAP, those live in SM59 (TCP/IP destination, registered server program). In Logic Apps, the SAP built-in trigger listens using the same Program ID and gateway settings, while the trigger configuration (for example, IDoc format and degree of parallelism) controls how messages are interpreted and processed. Once these values line up, the rest of the implementation becomes “normal workflow engineering”: validation, predictable error propagation, and response shaping. Before diving into workflow internals, I make the SAP-side contract explicit. The function module interface below shows the integration boundary: CSV lines come in as IT_CSV , results come back as ANALYSIS , and status/error information is surfaced both as a human-readable EXCEPTIONMSG and as a structured RETURN ( BAPIRET2 ). I also use a dedicated exception ( SENDEXCEPTIONTOSAPSERVER ) to signal workflow-raised failures cleanly. Contract (what goes over RFC): Input: IT_CSV (CSV lines) Outputs: ANALYSIS (analysis payload), EXCEPTIONMSG (human-readable status) Return structure: RETURN ( BAPIRET2 ) for structured SAP-style success/error Custom exception: SENDEXCEPTIONTOSAPSERVER for workflow-raised failures Here is the ABAP wrapper that calls the remote implementation and normalizes the result. FUNCTION z_get_orders_analysis. *"---------------------------------------------------------------------- *" This module acts as a caller wrapper. *" Important: the remote execution is determined by DESTINATION. *" Even though the function name is the same, this is not recursion: *" the call runs in the remote RFC server registered under DESTINATION "DEST". *"---------------------------------------------------------------------- *" Contract: *" TABLES it_csv "CSV lines *" IMPORTING analysis "Result payload *" EXPORTING exceptionmsg "Human-readable status / error *" CHANGING return "BAPIRET2 return structure *" EXCEPTIONS sendexceptiontosapserver *"---------------------------------------------------------------------- CALL FUNCTION 'Z_GET_ORDERS_ANALYSIS' DESTINATION dest IMPORTING analysis = analysis TABLES it_csv = it_csv CHANGING return = return EXCEPTIONS sendexceptiontosapserver = 1 system_failure = 2 MESSAGE exceptionmsg communication_failure = 3 MESSAGE exceptionmsg OTHERS = 4. CASE sy-subrc. WHEN 0. exceptionmsg = 'ok'. "Optional: normalize success into RETURN for callers that ignore EXCEPTIONMSG IF return-type IS INITIAL. return-type = 'S'. return-message = 'OK'. ENDIF. WHEN 1. exceptionmsg = |Exception from workflow: SENDEXCEPTIONTOSAPSERVER { sy-msgv1 }{ sy-msgv2 }{ sy-msgv3 }{ sy-msgv4 }|. return-type = 'E'. return-message = exceptionmsg. WHEN 2 OR 3. "system_failure / communication_failure usually already populate exceptionmsg IF exceptionmsg IS INITIAL. exceptionmsg = |RFC system/communication failure.|. ENDIF. return-type = 'E'. return-message = exceptionmsg. WHEN OTHERS. exceptionmsg = |Error in workflow: { sy-msgv1 }{ sy-msgv2 }{ sy-msgv3 }{ sy-msgv4 }|. return-type = 'E'. return-message = exceptionmsg. ENDCASE. ENDFUNCTION. The wrapper is intentionally small: it forwards the payload to the remote implementation via the RFC destination and then normalizes the outcome into a predictable shape. The point isn’t fancy ABAP — it’s reliability. With a stable contract ( IT_CSV, ANALYSIS, RETURN, EXCEPTIONMSG ) the Logic Apps side can evolve independently while SAP callers still get consistent success/error semantics. Important: in 'CALL FUNCTION 'Z_GET_ORDERS_ANALYSIS' DESTINATION dest' the name of the called function should be the same as the name of the ABAP wrapper function module, the reason being that the SAP built-in trigger in the logic app uses the function module signature as the contract (i.e. metadata). Also note that in the provided companion artifacts, a default value was assigned to 'dest' in the function module definition. But it can also be specified in the request (DEST element). Z_GET_ORDERS_ANALYSIS . To sum up, the integration is intentionally shaped around three outputs: the raw input table ( IT_CSV ), a standardized SAP return structure ( RETURN / BAPIRET2 ), and a readable status string ( EXCEPTIONMSG ). The custom exception ( SENDEXCEPTIONTOSAPSERVER ) gives me a clean way to surface workflow failures back into SAP without burying them inside connector-specific error payloads. This is depicted in the figure below. 4. Destination Workflow The diagram below shows the destination workflow at a high level. I designed it as a staged pipeline: guard early, normalize input, validate, and then split the workload into two paths—operational handling of invalid records (notifications and optional IDoc remediation) and analysis of the validated dataset. Importantly, the SAP response is intentionally narrow: SAP receives only the final analysis (or a structured error), while validation details are delivered out-of-band via email. How to read this diagram Guardrail: Validate requested action ensures the workflow only handles the expected request. Normalize: Create CSV payload converts the inbound content into a consistent CSV representation. Validate: Data Validation Agent identifies invalid records (and produces a summary). Operational handling (invalid data): invalid rows are reported by email and may optionally be turned into IDocs (right-hand block). Analyze (valid data): Analyze data runs only on the validated dataset (invalid IDs excluded). Outputs: users receive Email analysis, while SAP receives only the analysis (or a structured error) via Respond to SAP server. Figure: Destination workflow with staged validation, optional IDoc remediation, and an SAP response . Reading the workflow top-to-bottom, the main design choice is separation of concerns. Validation is used to filter and operationalize bad records (notify humans, optionally create IDocs), while the SAP-facing response stays clean and predictable: SAP receives the final analysis for the validated dataset, or an error if the run can’t complete. This keeps the SAP contract stable even as validation rules and reporting details evolve. Step‑by‑step walkthrough Phase 0 — Entry and routing Trigger — When a message is received The workflow starts when an inbound SAP message is delivered to the Logic Apps SAP built‑in trigger. Guardrail — Validate requested action (2 cases) The workflow immediately checks whether the inbound request is the operation it expects (for example, the function/action name equals Z_GET_ORDERS_ANALYSIS ). If the action does not match: the workflow sends an exception back to SAP describing the unexpected action and terminates early (fail fast). If the action matches: processing continues. Phase 1 — Normalize input into a workflow‑friendly payload Prepare input — Create CSV payload The workflow extracts CSV lines from the inbound (XML) SAP payload and normalizes them into a consistent CSV text payload that downstream steps can process reliably. Initialize validation state — Initialize array of invalid order ids The workflow creates an empty array variable to capture order IDs that fail validation. This becomes the “validation output channel” used later for reporting, filtering, and optional remediation. Phase 2 — Validate the dataset (AI agent loop) Validate — Data Validation Agent (3 cases) This stage performs rule‑based validation using an agent pattern (backed by Azure OpenAI). Conceptually, it does three things (as shown in the diagram’s expanded block): Get validation rules: retrieves business rules from a SharePoint‑hosted validation document. The location of the validation rules file is a parameter of the logic app. Get CSV payload: loads the normalized CSV created earlier. Summarize CSV payload review: evaluates the CSV against the rules and produces structured validation outputs. Outputs produced by validation: A list of invalid order IDs The corresponding invalid CSV rows A human‑readable validation summary Note: The detailed AI prompt/agent mechanics are covered in Part 2. In Part 1, the focus is on the integration flow and how data moves. Phase 3 — Operational handling of invalid records (email + optional SAP remediation) After validation, the workflow treats invalid records as an operational concern: they are reported to humans and can optionally be routed into an SAP remediation path. This is shown in the right‑hand “Create IDocs” block. Notify — Send verification summary The workflow sends an email report (Office 365) to configured recipients containing: the validation summary the invalid order IDs the invalid CSV payload (or the subset of invalid rows) Transform — Transform CSV to XML The workflow converts the invalid CSV lines into an XML format that is suitable for SAP processing. Optional remediation — [RFC] Create all IDocs (conditional) If the workflow parameter (for example, CreateIDocs) is enabled, the workflow calls an SAP RFC (e.g., Z_CREATE_ONLINEORDER_IDOC ) to create IDocs from the transformed invalid data. Why this matters: Validation results are made visible (email) and optionally actionable (IDocs), without polluting the primary analysis response that SAP receives. Phase 4 — Analyze only the validated dataset (AI analysis) The workflow runs AI analysis on the validated dataset, explicitly excluding invalid order IDs discovered during the validation phase. The analysis prompt instructs the model to produce outputs such as trends, predictions, and recommendations. Note: The AI analysis prompt design and output shaping are covered in Part 2. Phase 5 — Post‑process the AI response and publish outputs Package results — Process analysis results (Scope) The workflow converts the AI response into a format suitable for email and for SAP consumption: Parse the OpenAI JSON response Extract the analysis content Convert markdown → HTML using custom JavaScript formatting Outputs Email analysis: sends the formatted analysis to recipients. Respond to SAP server: returns only the analysis (and errors) to SAP. Key design choice: SAP receives a clean, stable contract—analysis on success, structured error on failure. Validation details are handled out‑of‑band via email (and optionally via IDoc creation). Note: the analysis email sent by the destination workflow is there for testing purposes, to verify that the html content remains the same as it is sent back to the source workflow. Useful snippets Snippet 1 - Join each CSV line in the XML to make a CSV table: join( xpath( xml(triggerBody()?['content']), '/*[local-name()=\"Z_GET_ORDERS_ANALYSIS\"] /*[local-name()=\"IT_CSV\"] /*[local-name()=\"ZTY_CSV_LINE\"] /*[local-name()=\"LINE\"]/text()' ), '\r\n') Note: For the sake of simplicity, XPath is used here and throughout all places where XML is parsed. In the general case however, the Parse XML with schema action is the better and recommended way to strictly enforce the data contract between senders and receivers. More information about Parse XML with schema is provided in Appendix 1. Snippet 2 - Format markdown to html (simplified): const raw = workflowContext.actions.Extract_analysis.outputs; // Basic HTML escaping for safety (keeps <code> blocks clean) const escapeHtml = s => s.replace(/[&<>"]/g, c => ({'&':'&','<':'<','>':'>','"':'"'}[c])); // Normalize line endings let md = raw; // raw.replace(/\r\n/g, '\n').trim(); // Convert code blocks (``` ... ```) md = md.replace(/```([\s\S]*?)```/g, (m, p1) => `<pre><code>${escapeHtml(p1)}</code></pre>`); // Horizontal rules --- or *** md = md.replace(/(?:^|\n)---+(?:\n|$)/g, '<hr/>'); // Headings ###### to # for (let i = 6; i >= 1; i--) { const re = new RegExp(`(?:^|\\n)${'#'.repeat(i)}\\s+(.+?)\\s*(?=\\n|$)`, 'g'); md = md.replace(re, (m, p1) => `<h${i}>${p1.trim()}</h${i}>`); } // Bold and italic md = md.replace(/\*\*([^*]+)\*\*/g, '<strong>$1</strong>'); md = md.replace(/\*([^*]+)\*/g, '<em>$1</em>'); // Unordered lists (lines starting with -, *, +) md = md.replace(/(?:^|\n)([-*+]\s.+(?:\n[-*+]\s.+)*)/g, (m) => { const items = m.trim().split(/\n/).map(l => l.replace(/^[-*+]\s+/, '').trim()); return '\n<ul>' + items.map(i => `<li>${i}</li>`).join('\n') + '</ul>'; }); // Paragraphs: wrap remaining text blocks in <p>...</p> const blocks = md.split(/\n{2,}/).map(b => { if (/^<h\d>|^<ul>|^<pre>|^<hr\/>/.test(b.trim())) return b; return `<p>${b.replace(/\n/g, '<br/>')}</p>`; }); const html = blocks.join(''); return { html }; 5. Exception Handling To illustrate exception handling, we supposed that multiple workflows may listen to the same program id (by design or unexpectedly) and could therefore receive messages that were meant for others. So the first thing that happens is validate that the function name is as expected. It is shown below. In this section I show three practical ways to surface workflow failures back to SAP using the Logic Apps action “Send exception to SAP server”, and the corresponding ABAP patterns used to handle them. The core idea is the same in all three: Logic Apps raises an exception on the SAP side, SAP receives it as an RFC exception, and your ABAP wrapper converts that into something predictable (for example, a readable EXCEPTIONMSG , a populated RETURN , or both). The differences are in how much control you want over the exception identity and whether you want to leverage SAP message classes for consistent, localized messages. 5.1 Default exception This first example shows the default behavior of Send exception to SAP server. When the action runs without a custom exception name configuration, the connector raises a pre-defined exception that can be handled explicitly in ABAP. On the Logic Apps side, the action card “Send exception to SAP server” sends an Exception Error Message (for example, “Unexpected action in request: …”). On the ABAP side, the RFC call lists SENDEXCEPTIONTOSAPSERVER = 1 under EXCEPTIONS , and the code uses CASE sy-subrc to map that exception to a readable message. The key takeaway is that you get a reliable “out-of-the-box” exception path: ABAP can treat sy-subrc = 1 as the workflow‑raised failure and generate a consistent EXCEPTIONMSG . This is the simplest option and works well when you don’t need multiple exception names—just one clear “workflow failed” signal. 5.2 Message exception If you want more control than the default, you can configure the action to raise a named exception declared in your ABAP function module interface. This makes it easier to route different failure types without parsing free-form text. The picture shows Advanced parameters under the Logic Apps action, including “Exception Name” with helper text indicating it must match an exception declared in the ABAP function module definition. This option is useful when you want to distinguish workflow error categories (e.g., validation vs. routing vs. downstream failures) using exception identity, not just message text. The contract stays explicit: Logic Apps raises a named exception, and ABAP can branch on that name (or on sy-subrc mapping) with minimal ambiguity. 5.3 Message class exception The third approach uses SAP’s built-in message class mechanism so that the exception raised by the workflow can map cleanly into SAP’s message catalog ( T100 ). This is helpful when you want consistent formatting and localization aligned with standard SAP patterns. On the Logic Apps side, the action shows advanced fields including Message Class, Message Number, and an Is ABAP Message toggle, with helper text stating the message class can come from message maintenance ( SE91 ) or be custom. On the ABAP side, the code highlights an error-handling block that calls using sy-msgid , sy-msgno , and variables sy-msgv1 … sy-msgv4 , then stores the resulting text in EXCEPTIONMSG . This pattern is ideal when you want workflow exceptions to look and behave like “native” SAP messages. Instead of hard-coding strings, you rely on the message catalog and let ABAP produce a consistent final message via FORMAT_MESSAGE . The result is easier to standardize across teams and environments—especially if you already manage message classes as part of your SAP development process. Refer to Appendix 2 for further information on FORMAT_MESSAGE . 5.4 Choosing an exception strategy that SAP can act on Across these examples, the goal is consistent: treat workflow failures as first‑class outcomes in SAP, not as connector noise buried in run history. The Logic Apps action Send exception to SAP server gives you three increasingly structured ways to do that, and the “right” choice depends on how much semantics you want SAP to understand. Default exception (lowest ceremony): Use this when you just need a reliable “workflow failed” signal. The connector raises a pre-defined exception name (for example, SENDEXCEPTIONTOSAPSERVER ), and ABAP can handle it with a simple EXCEPTIONS … = 1 mapping and a sy-subrc check. This is the fastest way to make failures visible and deterministic. Named exception(s) (more routing control): Use this when you want SAP to distinguish failure types without parsing message text. By raising an exception name declared in the ABAP function module interface, you can branch cleanly in ABAP (or map to different return handling) and keep the contract explicit and maintainable. Message class + number (most SAP-native): Use this when you want errors to look and behave like standard SAP messages—consistent wording, centralized maintenance, and better alignment with SAP operational practices. In this mode, ABAP can render the final localized string using FORMAT_MESSAGE and return it as EXCEPTIONMSG (and optionally BAPIRET2 - MESSAGE ), which makes the failure both human-friendly and SAP-friendly. A practical rule of thumb: start with the default exception while you stabilize the integration, move to named exceptions when you need clearer routing semantics, and adopt message classes when you want SAP-native error governance (standardization, maintainability, and localization). Regardless of the option, the key is to end with a predictable SAP-side contract: a clear success path, and a failure path that produces a structured return and a readable message. 6. Response Handling This section shows how the destination workflow returns either a successful analysis response or a workflow exception back to SAP, and how the source (caller) workflow interprets the RFC response structure to produce a single, human‑readable outcome (an email body). The key idea is to keep the SAP-facing contract stable: SAP always returns a Z_GET_ORDERS_ANALYSISResponse envelope, and the caller workflow decides between success and error using just two fields: EXCEPTIONMSG and RETURN / MESSAGE . To summarize the steps: Destination workflow either: sends a normal response via Respond to SAP server, or raises an exception via Send exception to SAP server (with an error message). SAP server exposes those outcomes through the RFC wrapper: sy-subrc = 0 → success ( EXCEPTIONMSG = 'ok') sy-subrc = 1 → workflow exception ( SENDEXCEPTIONTOSAPSERVER ) sy-subrc = 2/3 → system/communication failures Source workflow calls the RFC, extracts: EXCEPTIONMSG RETURN / MESSAGE and uses an Has errors gate to choose between a success email body (analysis) or a failure email body (error summary). The figure below shows the full return path for results and failures. On the right, the destination workflow either responds normally (Respond to SAP server) or raises a workflow exception (Send exception to SAP server). SAP then maps that into the RFC outcome ( sy-subrc and message fields). On the left, the source workflow parses the RFC response structure and populates a single EmailBody variable using two cases: failure (error details) or success (analysis text). Figure: Response/exception flow Two things make this pattern easy to operationalize. First, the caller workflow does not need to understand every SAP field—only EXCEPTIONMSG and RETURN / MESSAGE are required to decide success vs failure. Second, the failure path intentionally aggregates details ( MESSAGE_V1 … MESSAGE_V4 plus the exception text) into a single readable string so errors don’t get trapped in run history. Callout: The caller workflow deliberately treats EXCEPTIONMSG != "ok" or RETURN / MESSAGE present as the single source of truth for failure, which keeps the decision logic stable even if the response schema grows. Detailed description Phase 1 — Destination workflow: choose “response” vs “exception” Respond to SAP server returns the normal response payload back to SAP. Send exception to SAP server raises a workflow failure with an Exception Error Message (the screenshot shows an example beginning with “Unexpected action in request:” and a token for Function Name). Outcome: SAP receives either a normal response or a raised exception for the RFC call. Phase 2 — SAP server: map workflow outcomes to RFC results The SAP-side wrapper code shown in the figure calls: CALL FUNCTION ' Z_GET_ORDERS_ANALYSIS ' DESTINATION DEST ... It declares exception mappings including: SENDEXCEPTIONTOSAPSERVER = 1 system_failure = 2 MESSAGE EXCEPTIONMSG communication_failure = 3 MESSAGE EXCEPTIONMSG OTHERS = 4 Then it uses CASE sy-subrc . to normalize outcomes (the figure shows WHEN 0. setting EXCEPTIONMSG = 'ok'., and WHEN 1. building a readable message for the workflow exception). Outcome: regardless of why it failed, SAP can provide a consistent set of fields back to the caller: a return structure and an exception/status message. Phase 3 — Source workflow: parse response and build one “email body” After the RFC action ([RFC] Call Z GET ORDERS ANALYSIS ) the source workflow performs: Save EXCEPTION message Extracts EXCEPTIONMSG from the response XML using XPath. Save RETURN message Extracts RETURN / MESSAGE from the response XML using XPath. Initialize email body Creates EmailBody once, then sets it in exactly one of two cases. Has errors (two cases) The condition treats the run as “error” if either: EXCEPTIONMSG is not equal to "ok", or RETURN / MESSAGE is not empty. Set email body (failure) / Set email body (success) Failure: builds a consolidated string containing RETURN / MESSAGE , message details ( MESSAGE_V1 ..V4), and EXCEPTIONMSG . Success: sets EmailBody to the ANALYSIS field extracted from the response. Outcome: the caller produces a single artifact (EmailBody) that is readable and actionable, without requiring anyone to inspect the raw RFC response. Note: the email recipient is set as a logic app parameter. 7. Destination Workflow #2: Persisting failed rows as custom IDocs In this section I zoom in on the optional “IDoc persistence” branch at the end of the destination workflow. After the workflow identifies invalid rows (via the Data Validation Agent) and emails a verification summary, it can optionally call a second SAP RFC to save the failed rows as IDocs for later processing. This is mainly included to showcase another common SAP integration scenario—creating/handling IDocs—and to highlight that you can combine “AI-driven validation” with traditional enterprise workflows. The deeper motivation for invoking this as part of the agent tooling is covered in Part 2; here, the goal is to show the connector pattern and the custom RFC used to create IDocs from CSV input. The figure below shows the destination workflow at two levels: a high-level overview at the top, and a zoomed view of the post-validation remediation steps at the bottom. The zoom starts from Data Validation Agent → Summarize CSV payload review and then expands the sequence that runs after Send verification summary: Transform CSV to XML followed by an SAP RFC call that creates IDocs from the failed data. The key point is that this branch is not the main “analysis response” path. It’s a practical remediation option: once invalid rows are identified and reported, the workflow can persist them into SAP using a dedicated RFC ( Z_CREATE_ONLINEORDER_IDOC ) and a simple IT_CSV payload. This keeps the end-to-end flow modular: analysis can remain focused on validated data, while failed records can be routed to SAP for follow-up processing on their own timeline. Callout: This branch exists to showcase an IDoc-oriented connector scenario. The “why this is invoked from the agent tooling” context is covered in Part 2; here the focus is the mechanics of calling Z_CREATE_ONLINEORDER_IDOC with IT_CSV and receiving ET_RETURN / ET_DOCNUMS . The screenshot shows an XML body with the RFC root element and an SAP namespace: <z_create_onlineorder_idoc xmlns="http://Microsoft.LobServices.Sap/2007/03/Rfc/"> <iv_direction>...</iv_direction> <iv_sndptr>...</iv_sndptr> <iv_sndprn>...</iv_sndprn> <iv_rcvptr>...</iv_rcvptr> <iv_rcvprn>...</iv_rcvprn> <it_csv> @{ ...Outputs... } </it_csv> <et_return></et_return> <et_docnums></et_docnums> </z_create_onlineorder_idoc> What to notice: the workflow passes invalid CSV rows in IT_CSV , and SAP returns a status table ( ET_RETURN ) and created document numbers ( ET_DOCNUMS ) for traceability. The payload includes standard-looking control fields ( IV_DIRECTION , IV_SNDPTR , IV_SNDPRN , IV_RCVPTR , IV_RCVPRN ) and the actual failed-row payload as IT_CSV . IT_CSV is populated via a Logic Apps expression (shown as @{ ...Outputs... } in the screenshot), which is the bridge between the prior transform step and the RFC call. The response side indicates table-like outputs: ET_RETURN and ET_DOCNUMS . 7.1 From CSV to IDocs I’ll cover the details of Destination workflow #2 in Part 2. In this post (Part 1), I focus on the contract and the end-to-end mechanics: what the RFC expects, what it returns, and how the created IDocs show up in the receiving workflow. Before looking at the RFC itself, it helps to understand the payload we’re building inside the IDoc. The screenshot below shows the custom segment definition used by the custom IDoc type. This segment is intentionally shaped to mirror the columns of the CSV input so the mapping stays direct and easy to reason about. Figure: Custom segment ZONLINEORDER000 (segment type ZONLINEORDER ) This segment definition is the contract anchor: it makes the CSV-to-IDoc mapping explicit and stable. Each CSV record becomes one segment instance with the same 14 business fields. That keeps the integration “boringly predictable,” which is exactly what you want when you’re persisting rejected records for later processing. The figure below shows the full loop for persisting failed rows as IDocs. The source workflow calls the custom RFC and sends the invalid CSV rows as XML. SAP converts each row into the custom segment and creates outbound IDocs. Those outbound IDocs are then received by Destination workflow #2, which processes them asynchronously (one workflow instance per IDoc) and appends results into shared storage for reporting. This pattern deliberately separates concerns: the first destination workflow identifies invalid rows and decides whether to persist them, SAP encapsulates the mechanics of IDoc creation behind a stable RFC interface, and a second destination workflow processes those IDocs asynchronously (one per IDoc), which is closer to how IDoc-driven integrations typically operate in production. Destination workflow #2 is included here to show the end-to-end contract and the “receipt” side of the connector scenario: Triggered by the SAP built-in trigger and checks FunctionName = IDOC_INBOUND_ASYNCHRONOUS extracts DOCNUM from the IDoc control record ( EDI_DC40 / DOCNUM ) reconstructs a CSV payload from the IDoc data segment (the fields shown match the segment definition) appends a “verification info” line to shared storage for reporting The implementation details of that workflow (including why it is invoked from the agent tooling) are covered in Part 2. 7.2 Z_CREATE_ONLINEORDER_IDOC - Contract overview The full source code for Z_CREATE_ONLINEORDER_IDOC is included in the supporting material. It’s too long to reproduce inline, so this post focuses on the contract—the part you need to call the RFC correctly and interpret its results. A quick note on authorship: most of the implementation was generated with Copilot, with manual review and fixes to resolve build errors and align the behavior with the intended integration pattern. The contract is deliberately generic because the goal was to produce an RFC that’s reusable across more than one scenario, rather than tightly coupled to a single workflow. At a high level, the RFC is designed to support: Both inbound and outbound IDoc creation It can either write IDocs to the SAP database (inbound-style persistence) or create/distribute IDocs outbound. Multiple IDoc/message/segment combinations IDoc type ( IDOCTYP ), message type ( MESTYP ), and segment type ( SEGTP ) are configurable so the same RFC can be reused. Explicit partner/port routing control Optional sender/receiver partner/port fields can be supplied when routing matters. Traceability of created artifacts The RFC returns created IDoc numbers so the caller can correlate “these failed rows” to “these IDocs.” Contract: Inputs (import parameters) IV_DIRECTION (default: 'O') — 'I' for inbound write-to-db, 'O' for outbound distribute/dispatch IV_IDOCTYP (default: ZONLINEORDERIDOC ) IV_MESTYP (default: ZONLINEORDER ) IV_SEGTP (default: ZONLINEORDER ) Optional partner/port routing fields: IV_SNDPRT , IV_SNDPRN , IV_RCVPRT , IV_RCVPRN , IV_RCVPOR Tables IT_CSV (structure ZTY_CSV_LINE ) — each row is one CSV line (the “table-of-lines” pattern) ET_RETURN (structure BAPIRET2 ) — success/warning/error messages (per-row and/or aggregate) ET_DOCNUMS (type ZTY_DOCNUM_TT ) — list of created IDoc numbers for correlation/traceability Outputs EV_DOCNUM — a convenience “primary / last created” DOCNUM value returned by the RFC 8. Concluding Remarks Part 1 established a stable SAP ↔ Logic Apps integration baseline: CSV moves end‑to‑end using explicit contracts, and failures are surfaced predictably. The source workflow reads CSV from Blob, wraps rows into the IT_CSV table‑of‑lines payload, calls Z_GET_ORDERS_ANALYSIS , and builds one outcome using two fields from the RFC response: EXCEPTIONMSG and RETURN / MESSAGE . The destination workflow gates requests, validates input, and returns only analysis (or errors) back to SAP while handling invalid rows operationally (notification + optional persistence). On the error path, we covered three concrete patterns to raise workflow failures back into SAP: the default connector exception ( SENDEXCEPTIONTOSAPSERVER ), named exceptions (explicit ABAP contract), and message‑class‑based errors (SAP‑native formatting via FORMAT_MESSAGE ). On the remediation side, we added a realistic enterprise pattern: persist rejected rows as custom IDocs via Z_CREATE_ONLINEORDER_IDOC ( IT_CSV in, ET_RETURN + ET_DOCNUMS out), using the custom segment ZONLINEORDER000 as the schema anchor and enabling downstream receipt in Destination workflow #2 (one run per IDoc, correlated via DOCNUM ). Part 2 is separate because it tackles a different problem: the AI layer. With contracts and error semantics now fixed, Part 2 can focus on the agent/tooling details that tend to iterate—rule retrieval, structured validation outputs, prompt constraints, token/history controls, and how the analysis output is generated and shaped—without muddying the transport story. Appendix 1: Parse XML with schema In this section I consider the CSV payload creation as an example, but parsing XML with schema applies in every place where we get an XML input to process, such as when receiving SAP responses, exceptions, or request/responses from other RFCs. Strong contract The Create_CSV_payload step in the shown implementation uses an xpath() + join() expression to extract LINE values from the incoming XML: join( xpath( xml(triggerBody()?['content']), '/*[local-name()="Z_GET_ORDERS_ANALYSIS"] /*[local-name()="IT_CSV"] /*[local-name()="ZTY_CSV_LINE"] /*[local-name()="LINE"]/text()' ), '\r\n' ) That approach works, but it’s essentially a “weak contract”: it assumes the message shape stays stable and that your XPath continues to match. By contrast, the Parse XML with schema action turns the XML payload into structured data based on an XSD, which gives you a “strong contract” and enables downstream steps to bind to known fields instead of re-parsing XML strings. The figure below compares two equivalent ways to build the CSV payload from the RFC input. On the left is the direct xpath() compose (labeled “weak contract”). On the right is the schema-based approach (labeled “strong contract”), where the workflow parses the request first and then builds the CSV payload by iterating over typed rows. What’s visible in the diagram is the key tradeoff: XPath compose path (left): the workflow creates the CSV payload directly using join(xpath(...), '\r\n') , with the XPath written using local-name() selectors. This is fast to prototype, but the contract is implicit—your workflow “trusts” the XML shape and your XPath accuracy. Parse XML with schema path (right): the workflow inserts a Parse XML with schema step (“ Parse Z GET ORDERS ANALYSIS request ”), initializes variables, loops For each CSV row, and Appends to CSV payload, then performs join(variables('CSVPayload'), '\r\n') . Here, the contract is explicit—your XSD defines what IT_CSV and LINE mean, and downstream steps bind to those fields rather than re-parsing XML. A good rule of thumb is: XPath is great for lightweight extraction, while Parse XML with schema is better when you want contract enforcement and long-term maintainability, especially in enterprise integration / BizTalk migration scenarios where schemas are already part of the integration culture. Implementation details The next figure shows the concrete configuration for Parse XML with schema and how its outputs flow into the “For each CSV row” loop. This is the “strong contract” version of the earlier XPath compose. This screenshot highlights three practical implementation details: The Parse action is schema-backed. In the Parameters pane, the action uses: Content: the incoming XML Response Schema source: LogicApp Schema name: Z_GET_ORDERS_ANALYSIS The code view snippet shows the same idea: type: "XmlParse" with content: " @triggerBody()?['content'] " and schema: { source: "LogicApp", name: "Z_GET_ORDERS_ANALYSIS.xsd" }. The parsed output becomes typed “dynamic content.” The loop input is shown as “ JSON Schema for element 'Z_GET_ORDERS_ANALYSIS: IT_CSV' ”. This is the key benefit: you are no longer scraping strings—you are iterating over a structured collection that was produced by schema-based parsing. The LINE extraction becomes trivial and readable. The “Append to CSV payload” step appends @item()?['LINE'] to the CSVpayload variable (as shown in the code snippet). Then the final Create CSV payload becomes a simple join(variables('CSVPayload'), '\r\n') . This is exactly the kind of “workflow readability” benefit you get once XML parsing is schema-backed. Schema generation The Parse action requires XSD schemas, which can be stored in the Logic App (or via a linked Integration Account). The final figure shows a few practical ways to obtain and manage those XSDs: Generate Schema (SAP connector): a “Generate Schema” action with Operation Type = RFC and an RFC Name field, which is a practical way to bootstrap schema artifacts when you already know the RFC you’re calling. Run Diagnostics / Fetch RFC Metadata: a “Run Diagnostics” action showing Operation type = Fetch RFC Metadata and RFC Name, which is useful to confirm the shape of the RFC interface and reconcile it with your XSD/contract. If you don’t want to rely solely on connector-side schema generation, there are also classic “developer tools” approaches: Infer XSD from a sample XML using .NET’s XmlSchemaInference (good for quick starting points). Generate XSD from an XML instance using xsd.exe (handy when you already have representative sample payloads) or by asking your favorite AI prompt. When to choose XPath vs Parse XML with schema (practical guidance) Generally speaking, choose XPath when… You need a quick extraction and you’re comfortable maintaining a single XPath. You don’t want to manage schema artifacts yet (early prototypes). Choose Parse XML with schema when… You want a stronger, explicit contract (XSD defines what the payload is). You want the designer to expose structured outputs (“JSON Schema for element …”) so downstream steps are readable and less brittle. You expect the message shape to evolve over time and prefer schema-driven changes over XPath surgery. Appendix 2: Using FORMAT_MESSAGE to produce SAP‑native error text When propagating failures from Logic Apps back into SAP (for example via Send exception to SAP server), I want the SAP side to produce a predictable, human‑readable message without forcing callers to parse connector‑specific payloads. ABAP’s FORMAT_MESSAGE is ideal for this because it converts SAP’s message context—message class, message number, and up to four variables—into the final message text that SAP would normally display, but without raising a UI message. What FORMAT_MESSAGE does FORMAT_MESSAGE formats a message defined in SAP’s message catalog ( T100 / maintained via SE91 ) using the values in sy-msgid , sy-msgno , and sy-msgv1 … sy-msgv4 . Conceptually, it answers the question: “Given message class + number + variables, what is the rendered message string?” This is particularly useful after an RFC call fails, where ABAP may have message context available even if the exception itself is not a clean string. Why this matters in an RFC wrapper In the message class–based exception configuration, the workflow can provide message metadata (class/number/type) so that SAP can behave “natively”: ABAP receives a failure ( sy-subrc <> 0), formats the message using FORMAT_MESSAGE , and returns the final text in a field like EXCEPTIONMSG (and/or in BAPIRET2 - MESSAGE ). The result is: consistent wording across systems and environments easier localization (SAP selects language-dependent text) separation of concerns: code supplies variables; message content lives in message maintenance A robust pattern After the RFC call, I use this order of precedence: Use any explicit text already provided (for example via system_failure … MESSAGE exceptionmsg), because it’s already formatted. If that’s empty but SAP message context exists ( sy-msgid / sy-msgno ), call FORMAT_MESSAGE to produce the final string. If neither is available, fall back to a generic message that includes sy-subrc . Here is a compact version of that pattern: DATA: lv_text TYPE string. CALL FUNCTION 'Z_GET_ORDERS_ANALYSIS' DESTINATION dest IMPORTING analysis = analysis TABLES it_csv = it_csv CHANGING return = return EXCEPTIONS sendexceptiontosapserver = 1 system_failure = 2 MESSAGE exceptionmsg communication_failure = 3 MESSAGE exceptionmsg OTHERS = 4. IF sy-subrc <> 0. "Prefer explicit message text if it already exists IF exceptionmsg IS INITIAL. "Otherwise format SAP message context into a string IF sy-msgid IS NOT INITIAL AND sy-msgno IS NOT INITIAL. CALL FUNCTION 'FORMAT_MESSAGE' EXPORTING id = sy-msgid no = sy-msgno v1 = sy-msgv1 v2 = sy-msgv2 v3 = sy-msgv3 v4 = sy-msgv4 IMPORTING msg = lv_text. exceptionmsg = lv_text. ELSE. exceptionmsg = |RFC failed (sy-subrc={ sy-subrc }).|. ENDIF. ENDIF. "Optionally normalize into BAPIRET2 for structured consumption return-type = 'E'. return-message = exceptionmsg. ENDIF. Common gotchas FORMAT_MESSAGE only helps if sy-msgid and sy-msgno are set. If the failure did not originate from an SAP message (or message mapping is disabled), these fields may be empty—so keep a fallback. Message numbers are typically 3-digit strings (e.g., 001, 012), matching how messages are stored in the catalog. FORMAT_MESSAGE formats text; it does not raise or display a message. That makes it safe to use in RFC wrappers and background processing. Bottom line: FORMAT_MESSAGE is a simple tool that helps workflow‑originated failures “land” in SAP as clean, SAP‑native messages—especially when using message classes to standardize and localize error text. References Logic Apps Agentic Workflows with SAP - Part 2: AI Agents Handling Errors in SAP BAPI Transactions | Microsoft Community Hub Access SAP from workflows | Microsoft Learn Create common SAP workflows | Microsoft Learn Generate Schemas for SAP Artifacts via Workflows | Microsoft Learn Parse XML using Schemas in Standard workflows - Azure Logic Apps | Microsoft Learn Announcing XML Parse and Compose for Azure Logic Apps GA Exception Handling | ABAP Keyword Documentation Handling and Propagating Exceptions - ABAP Keyword Documentation SAP .NET Connector 3.1 Overview SAP .NET Connector 3.1 Programming Guide All supporting content for this post may be found in the companion GitHub repository.
🎉 Announcing General Availability of AI & RAG Connectors in Logic Apps (Standard)
We’re excited to share that a comprehensive set of AI and Retrieval-Augmented Generation (RAG) capabilities is now Generally Available in Azure Logic Apps (Standard). This release brings native support for document processing, semantic retrieval, embeddings, and grounded reasoning directly into the Logic Apps workflow engine. 🔌 Available AI Connectors in Logic Apps Standard Logic Apps (Standard) had previously previewed four AI-focused connectors that open the door for a new generation of intelligent automation across the enterprise. Whether you're processing large volumes of documents, enriching operational data with intelligence, or enabling employees to interact with systems using natural language, these connectors provide the foundation for building solutions that are smarter, faster, and more adaptable to business needs. These are now in GA. They allow teams to move from routine workflow automation to AI-assisted decisioning, contextual responses, and multi-step orchestration that reflects real business intent. Below is the full set of built-in connectors and their actions as they appear in the designer. 1. Azure OpenAI Actions Get an embedding Get chat completions Get chat completions using Prompt Template Get completion Get multiple chat completions Get multiple embeddings What this unlocks Bring natural language reasoning and structured AI responses directly into workflows. Common scenarios include guided decisioning, user-facing assistants, classification and routing, or preparing embeddings for semantic search and RAG workflows. 2. Azure AI Search Actions Delete a document Delete multiple documents Get agentic retrieval output (Preview) Index a document Index multiple documents Merge document Search vectors Search vectors with natural language What this unlocks Add vector, hybrid semantic, and natural language search directly to workflow logic. Ideal for retrieving relevant content from enterprise data, powering search-driven workflows, and grounding AI responses with context from your own documents. 3. Azure AI Document Intelligence Action Analyze document What this unlocks Document Intelligence serves as the entry point for document-heavy scenarios. It extracts structured information from PDFs, images, and forms, allowing workflows to validate documents, trigger downstream processes, or feed high-quality data into search and embeddings pipelines. 4. AI Operations Actions Chunk text with metadata Parse document with metadata What this unlocks Transform unstructured files into enriched, structured content. Enables token-aware chunking, page-level metadata, and clean preparation of content for embeddings and semantic search at scale. 🤖 Advanced AI & Agentic Workflows with AgentLoop Logic Apps (Standard) also supports AgentLoop (also Generally Available), allowing AI models to use workflow actions as tools and iterate until the task is complete. Combined with chunking, embeddings, and natural language search, this opens the door to advanced agentic scenarios such as document intelligence agents, RAG-based assistants, and iterative evaluators. Conclusion With these capabilities now built into Logic Apps Standard, teams can bring AI directly into their integration workflows without additional infrastructure or complexity. Whether you’re streamlining document-heavy processes, enabling richer search experiences, or exploring more advanced agentic patterns, these capabilities provide a strong foundation to start building today.
