🤖 Agent Loop Demos 🤖
We announced the public preview of agent loop at Build 2025. Agent Loop is a new feature in Logic Apps to build AI Agents for use cases that span across industry domains and patterns. Here are some resources to learn more about them Agent loop concepts Agent loop how-to Agent loop public preview announcement In this article, share with you use cases implemented in Logic Apps using agent loop and other features. This video shows an autonomous Loan Approval Agent specifically that handles auto loans for a bank. The demo features an AI Agent that uses an Azure Open AI model, company's policies, and several tools to process loan application. For edge cases, huma in involved via Teams connector. This video shows an autonomous Product Return Agent for Fourth Coffee company. The returns are processed by agent based on company policy, and other criterions. In this case also, a human is involved when decisions are outside the agent's boundaries This video shows a commercial agent that grants credits for purchases of groceries and other products, for Northwind Stores. The Agent extracts financial information from an IBM Mainframe and an IBM i system to assess each requestor and updates the internal Northwind systems with the approved customers information. Multi-Agent scenario including both a codeful and declarative method of implementation. Note: This is pre-release functionality and is subject to change. If you are interested in further discussing Logic Apps codeful Agents, please fill out the following feedback form. Operations Agent (part 1): In this conversational agent, we will perform Logic Apps operations such as repair and resubmit to ensure our integration platform is healthy and processing transactions. To ensure of compliance we will ensure all operational activities are logged in ServiceNow. Operations Agent (part 2): In this autonomous agent, we will perform Logic Apps operations such as repair and resubmit to ensure our integration platform is healthy and processing transactions. To ensure of compliance we will ensure all operational activities are logged in ServiceNow.🎙️ Announcement: Logic Apps connectors in Azure AI Search for Integrated Vectorization
We’re excited to announce that Azure Logic Apps connectors are now supported within AI Search as data sources for ingestion into Azure AI Search vector stores. This unlocks the ability to ingest unstructured documents from a variety of systems—including SharePoint, Amazon S3, Dropbox and many more —into your vector index using a low-code experience. This new capability is powered by Logic Apps templates, which orchestrate the entire ingestion pipeline—from extracting documents to embedding generation and indexing—so you can build Retrieval-Augmented Generation (RAG) applications with ease. Grounding AI with RAG: Why Document Ingestion Matters Retrieval-Augmented Generation (RAG) has become a cornerstone technique for building grounded and trustworthy AI systems. Instead of generating answers from the model’s pretraining alone, RAG applications fetch relevant information from external knowledge bases—giving LLMs access to accurate and up-to-date enterprise data. To power RAG, enterprises need a scalable way to ingest and index documents into a vector store. Whether you're working with policy documents, legal contracts, support tickets, or financial reports, getting this content into a searchable, semantic format is step one. Simplified Ingestion with Integrated Vectorization Azure AI Search’s Integrated Vectorization capability automates the process of turning raw content into semantically indexed vectors: Chunking: Documents are split into meaningful text segments Embedding: Each chunk is transformed into a vector using an embedding model like text-embedding-3-small or a custom model Indexing: Vectors and associated metadata are written into a searchable vector store Projection: Metadata is preserved to enable filtering, ranking, and hybrid queries This eliminates the need to build or maintain custom pipelines, making it significantly easier to adopt RAG in production environments. Ingest from Anywhere: Logic Apps + AI Search With today’s release, we’re extending ingestion to a variety of new data sources by integrating Logic Apps connectors directly with AI Search. This allows you to retrieve unstructured content from enterprise systems and seamlessly ingest it into the vector store. Here’s how the ingestion process works with Logic Apps: Connect to Source Systems Using prebuilt connectors, Logic Apps can fetch content from various data sources including Sharepoint document libraries, messages from Service Bur or Azure Queues, files from OneDrive or SFTP Server and more. You can trigger ingestion on demand or at schedule. Parse and Chunk Documents Next, Logic Apps uses built-in AI-powered document parsing actions to extract raw text. This is followed by the “Chunk Document” action, which: Tokenizes the document based on language model-friendly units Splits the content into semantically coherent chunks This ensures optimal chunk size for downstream embedding and retrieval. Note – Currently we default to a chunk size of 5000 in the workflows created for document ingestion. We’ll be updating the default chunk size to a smaller number in our next release. Meanwhile, you can update it in the workflow if you need a smaller chunk size. Generate Embeddings with Azure OpenAI The chunked text is then passed to the Azure OpenAI connector, where the text-embedding-3-small or another configured embedding model is used to generate high-dimensional vector representations. These vectors capture the semantic meaning of the content and are key to enabling accurate retrieval in RAG applications. Write to Azure AI Search Finally, the embeddings, along with any relevant metadata (e.g., document title, tags, timestamps), are written into the AI Search index. The index schema is created for you ——and can include fields for filtering, sorting, and semantic ranking. Logic Apps Templates: Fast Start, Flexible Design To help you get started, we’ve created Logic Apps templates specifically for RAG ingestion. These templates: Include all the steps mentioned above Are customizable if you want to update the default configuration Whether you’re ingesting thousands of PDFs from SharePoint or syncing files from Amazon S3 bucket, these templates provide a production-grade foundation for building your pipeline. Getting Started Here is step by step detailed documentation to get started using Integrated Vectorization with Logic Apps data sources 👉 Get started with Logic Apps data sources for AI Search ingestion 👉 Learn more about Integrated Vectorization in Azure AI Search We'd Love Your Feedback We're just getting started. Tell us: What other data sources would you like to ingest? What enhancements would make ingestion easier for your use case? Are there specific industry templates or formats we should support? 👉 Reply to this post or share your ideas through our feedback form We’re building this with you—so your feedback helps shape the future of AI-powered automation and RAG.
🤖 AI Procurement assistant using prompt templates in Standard Logic Apps
📘 Introduction Answering procurement-related questions doesn't have to be a manual process. With the new Chat Completions using Prompt Template action in Logic Apps (Standard), you can build an AI-powered assistant that understands context, reads structured data, and responds like a knowledgeable teammate. 🏢 Scenario: AI assistant for IT procurement Imagine an employee wants to know: "When did we last order laptops for new hires in IT?" Instead of forwarding this to the procurement team, a Logic App can: Accept the question Look up catalog details and past orders Pass all the info to a prompt template Generate a polished, AI-powered response 🧠 What Are Prompt Templates? Prompt Templates are reusable text templates that use Jinja2 syntax to dynamically inject data at runtime. In Logic Apps, this means you can: Define a prompt with placeholders like {{ customer.orders }} Automatically populate it with outputs from earlier actions Generate consistent, structured prompts with minimal effort ✨ Benefits of Using Prompt Templates in Logic Apps Consistency: Centralized prompt logic instead of embedding prompt strings in each action. Reusability: Easily apply the same prompt across multiple workflows. Maintainability: Tweak prompt logic in one place without editing the entire flow. Dynamic control: Logic Apps inputs (e.g., values from a form, database, or API) flow right into the template. This allows you to create powerful, adaptable AI-driven flows without duplicating effort — making it perfect for scalable enterprise automation. 💡 Try it Yourself Grab the sample prompt template and sample inputs from our GitHub repo and follow along. 👉 Sample logic app 🧰 Prerequisites To get started, make sure you have: A Logic App (Standard) resource in Azure An Azure OpenAI resource with a deployed GPT model (e.g., GPT-3.5 or GPT-4) 💡 You’ll configure your OpenAI API connection during the workflow setup. 🔧 Build the Logic App workflow Here’s how to build the flow in Logic Apps using the Prompt Template action. This setup assumes you're simulating procurement data with test inputs. 📌 Step 0: Start by creating a Stateful Workflow in your Logic App (Standard) resource. Choose "Stateful" when prompted during workflow creation. This allows the run history and variables to be preserved for testing. 📸 Creating a new Stateful Logic App (Standard) workflow Here’s how to build the flow in Logic Apps using the Prompt Template action. This setup assumes you're simulating procurement data with test inputs. 📌 Trigger: "When an HTTP request is received" 📌 Step 1: Add three Compose actions to store your test data. documents: This stores your internal product catalog entries [ { "id": "1", "title": "Dell Latitude 5540 Laptop", "content": "Intel i7, 16GB RAM, 512GB SSD, standard issue for IT new hire onboarding" }, { "id": "2", "title": "Docking Station", "content": "Dell WD19S docking stations for dual monitor setup" } ] 📸 Compose action for documents input question: This holds the employee’s natural language question. [ { "role": "user", "content": "When did we last order laptops for new hires in IT?" } ] 📸 Compose action for question input customer: This includes employee profile and past procurement orders { "firstName": "Alex", "lastName": "Taylor", "department": "IT", "employeeId": "E12345", "orders": [ { "name": "Dell Latitude 5540 Laptop", "description": "Ordered 15 units for Q1 IT onboarding", "date": "2024/02/20" }, { "name": "Docking Station", "description": "Bulk purchase of 20 Dell WD19S docking stations", "date": "2024/01/10" } ] } 📸 Compose action for customer input 📌 Step 2: Add the "Chat Completions using Prompt Template" action 📸 OpenAI connector view 💡Tip: Always prefer the in-app connector (built-in) over the managed version when choosing the Azure OpenAI operation. Built-in connectors allow better control over authentication and reduce latency by running natively inside the Logic App runtime. 📌 Step 3: Connect to Azure OpenAI Navigate to your Azure OpenAI resource and click on Keys and Endpoint for connecting using key-based authentication 📸 Create Azure OpenAI connection 📝 Prompt template: Building the message for chat completions Once you've added the Get chat completions using Prompt Template action, here's how to set it up: 1. Deployment Identifier Enter the name of your deployed Azure OpenAI model here (e.g., gpt-4o). 📌 This should match exactly with what you configured in your Azure OpenAI resource. 2. Prompt Template This is the structured instruction that the model will use. Here’s the full template used in the action — note that the variable names exactly match the Compose action names in your Logic App: documents, question, and customer. system: You are an AI assistant for Contoso's internal procurement team. You help employees get quick answers about previous orders and product catalog details. Be brief, professional, and use markdown formatting when appropriate. Include the employee’s name in your response for a personal touch. # Product Catalog Use this documentation to guide your response. Include specific item names and any relevant descriptions. {% for item in documents %} Catalog Item ID: {{item.id}} Name: {{item.title}} Description: {{item.content}} {% endfor %} # Order History Here is the employee's procurement history to use as context when answering their question. {% for item in customer.orders %} Order Item: {{item.name}} Details: {{item.description}} — Ordered on {{item.date}} {% endfor %} # Employee Info Name: {{customer.firstName}} {{customer.lastName}} Department: {{customer.department}} Employee ID: {{customer.employeeId}} # Question The employee has asked the following: {% for item in question %} {{item.role}}: {{item.content}} {% endfor %} Based on the product documentation and order history above, please provide a concise and helpful answer to their question. Do not fabricate information beyond the provided inputs. 📸 Prompt template action view 3. Add your prompt template variables Scroll down to Advanced parameters → switch the dropdown to Prompt Template Variable. Then: Add a new item for each Compose action and reference it dynamically from previous outputs: documents question customer 📸 Prompt template variable references 🔍 How the template works Template element What it does {{ customer.firstName }} {{ customer.lastName }} Displays employee name {{ customer.department }} Adds department context {{ question[0].content }} Injects the user’s question from the Compose action named question {% for doc in documents %} Loops through catalog data from the Compose action named documents {% for order in customer.orders %} Loops through employee’s order history from customer Each of these values is dynamically pulled from your Logic App Compose actions — no code, no external services needed. You can apply the exact same approach to reference data from any connector, like a SharePoint list, SQL row, email body, or even AI Search results. Just map those outputs into the Prompt Template and let Logic Apps do the rest. ✅ Final Output When you run the flow, the model might respond with something like: "The last order for Dell Latitude 5540 laptops was placed on February 20, 2024 — 15 units were procured for IT new hire onboarding." This is based entirely on the structured context passed in through your Logic App — no extra fine-tuning required. 📸 Output from run history 💬 Feedback Let us know what other kinds of demos and content you would like to see using this formAnnouncing AI building blocks in Logic Apps (Consumption)
We’re thrilled to announce that the Azure OpenAI and AI Search connectors, along with the Parse Document and Chunk Text actions, are now available in the Logic Apps Consumption SKU! These capabilities, already available in the Logic Apps Standard SKU, can now be leveraged in serverless, pay-as-you-go workflows to build powerful AI-driven applications providing cost-efficiency and flexibility. What’s new in Consumption SKU? This release brings almost all the advanced AI capabilities from Logic Apps Standard to Consumption SKU, enabling lightweight, event-driven workflows that automatically scale with your needs. Here’s a summary of the operations now available: Azure OpenAI connector operations Get Completions: Generate text with Azure OpenAI’s GPT models for tasks such as summarization, content creation, and more. Get Embeddings: Generate vector embeddings from text for advanced scenarios like semantic search and knowledge mining. AI Search connector operations Index Document: Add or update a single document in an AI Search index. Index Multiple Documents: Add or update multiple documents in an AI Search index in one operation. *Note: The Vector Search operation for enabling retrieval pattern will be highlighted in an upcoming release in December.* Parse Document and Chunk Text Actions Under the Data operations connector: Parse Document: Extract structured data from uploaded files like PDFs or images. Chunk Text: Split large text blocks into smaller chunks for downstream processing, such as generating embeddings or summaries. Demo workflow: Automating document ingestion with AI To showcase these capabilities, here’s an example workflow that automates document ingestion, processing, and indexing: Trigger: Start the workflow with an HTTP request or an event like a file upload to Azure Blob Storage. Get Blob Content: Retrieve the document to be processed. Parse Document: Extract structured information, such as key data points from a service agreement. Chunk Text: Split the document content into smaller, manageable text chunks. Generate Embeddings: Use the Azure OpenAI connector to create vector embeddings for the text chunks. Select array: To compose the inputs being passed to Index documents operation Index Data: Store the embeddings and metadata for downstream applications, like search or analytics Why choose Consumption SKU? With this release, Logic Apps Consumption SKU allows you to: - Build smarter, scalable workflows: Leverage advanced AI capabilities without upfront infrastructure costs. - Pay only for what you use: Ideal for event-driven workloads where cost-efficiency is key. - Integrate seamlessly: Combine AI capabilities with hundreds of existing Logic Apps connectors. What’s next? In December, we’ll be announcing the Vector Search operation for the AI Search connector, enabling retrieval capability in Logic Apps Consumption SKU to bring feature parity with Standard SKU. This will allow you to perform advanced search scenarios by matching queries with contextually similar content. Stay tuned for updates!📢Announcement: Power your Agents in Azure AI Foundry Agent Service with Azure Logic Apps
We’re excited to announce the Public Preview of two major integrations that bring the power of Azure Logic Apps to AI Agents in Foundry: Logic Apps as tools: You can now use Logic Apps workflows—and their 1400+ connectors—as tools within the Azure Foundry AI Agent Service. This unlocks seamless integration between AI agents and enterprise-grade automation—enabling agents to reason and act through Logic Apps. AI Agent Service connector: A new Logic Apps connector for the AI Agent Service is now available, allowing you to build workflows that can trigger agents based on events across hundreds of applications. This enables your agents to respond proactively and continuously—bringing event-driven autonomy to your AI solutions. Checkout the blogpost for these announcements from Foundry as well. Logic Apps as tool for Agents in Foundry Logic Apps now powers the tool layer for AI Agents in Foundry Agent Service —bringing together the strengths of business process automation and intelligent reasoning. AI agents need more than powerful models to be effective—they need the ability to act and the context to act appropriately. Tools play a critical role in this: they don’t just let agents perform actions—they provide the inputs, signals, and structure that anchor the agent’s reasoning and guide consistent behavior. Well-designed tools help ensure that agents make decisions based on reliable, real-world data and aligned business rules. With over 1400+ connectors, Logic Apps lets agents tap into real-world enterprise systems—such as reading records from a SQL database, retrieving order data from an ERP system like SAP, managing support tickets in ServiceNow, or triggering actions in CRM platforms like Dynamics or Salesforce. This integration transforms agents from passive responders into intelligent actors that can take meaningful, context-aware action across your organization. Requirements for using Logic Apps as Tools To use a Logic App as a tool within the AI Agent Service, your workflow must meet the following criteria: Consumption SKU: Currently, only Logic Apps in Consumption plan are supported. Request Trigger: The workflow must begin with a Request trigger so that it can be invoked by the agent via a REST call. Tool Description: Each workflow should include a clear, concise description to help the agent understand its purpose and appropriate usage. Getting Started with Logic Apps in AI Foundry There are two ways to bring Logic Apps into your agents’ toolset. You can find step by step instructions in the docs here. To summarize, Use prebuilt Microsoft authored templates Select from a library of curated Logic Apps templates designed for agent scenarios. After selecting a template: Configure the tool’s name and description Authenticate any services used in the workflow Set required parameters Once configured, the workflow will be deployed to your selected subscription and resource group, ready to be used by your agent. Import existing workflows If you already have Logic Apps powering key operations in your business, you can import them directly: Go to the Your Actions tab Select your existing workflow Provide a name and description for agent usage This makes it easy to extend your existing APIs and business logic to AI agents—no need to start from scratch. Tool Calling Demo In this demo video we build an AI Agent that can respond to any questions about GitHub issues and send an email report about them. The opportunities to unlock scenarios are endless and we can’t wait to hear from you. Logic Apps as a trigger for Agents in Foundry We’re excited to launch the AI Agent Service connector in Logic Apps—making it easier than ever to bring autonomy to your business processes. With this connector, you can now use any Logic Apps trigger—from HTTP requests to Service Bus messages, file drops, or scheduled events—to kick off a workflow that invokes an AI agent. This means your agents can now respond to real-world events in near real time, making decisions and taking actions based on dynamic context. Whether it’s processing a new order, reviewing a document, or triaging support tickets, Logic Apps + AI Agent Service gives you the power to build truly autonomous, intelligent workflows. Start Building Ready to try it out? Check out the documentation for step-by-step guidance on using Logic Apps as tools in the AI Agent Service. We’d love to hear what you build! Try the feature and share your feedback—your input helps shape the future of AI-powered automation in Azure.🚀 Announcement: Azure Logic Apps Document Indexer in Azure Cosmos DB
We’re excited to announce the public preview of Azure Logic Apps as a document indexer for Azure Cosmos DB! With this release, you can now use Logic Apps connectors and templates to ingest documents directly into Cosmos DB’s vector store—powering AI workloads like Retrieval-Augmented Generation (RAG) with ease. This new capability orchestrates the full ingestion pipeline—from fetching documents to parsing, chunking, embedding, and indexing—allowing you to unlock insights from unstructured content across your enterprise systems. Check out the announcement from Azure Cosmos team about this capability! How It Works Here’s how Logic Apps powers the ingestion flow: Connect to Source Systems While Logic Apps has more than 1400+ prebuilt connectors to pull documents from various systems, this experience streamlines the entire process via out of box templates to pull data from sources like Azure Blob Storage. Parse and Chunk Documents AI-powered parsing actions extract raw text. Then, the Chunk Document action: Tokenizes content into language model-friendly units Splits it into semantically meaningful chunks This ensures optimal size and quality for embedding and retrieval. Generate Embeddings with Azure OpenAI The chunks are passed to Azure OpenAI via connector to generate embeddings (e.g., using text-embedding-3-small). These vectors capture the meaning of your content for precise semantic search. Write to Azure Cosmos DB Vector Store Embeddings and metadata (like title, tags, and timestamps) are indexed in Cosmos DB’s, using a schema optimized for filtering, semantic ranking, and retrieval. Logic Apps Templates: Fast Start, Full Flexibility We’ve created ready-to-use templates to help you get started fast: 📄 Blob Storage – Simple Text Parsing 🧾 Blob Storage – OCR with Azure Document Intelligence 📁 SharePoint – Simple Text Parsing 🧠 SharePoint – OCR with Azure Document Intelligence Each template is customizable—so you can adapt it to your business needs or expand it with additional steps. We’d Love Your Feedback We’re just getting started—and we’re building this with you. Tell us: What data sources should we support next? Are there specific formats or verticals you need (e.g., legal docs, invoices, contracts)? What enhancements would make ingestion even easier? 👉 Reply to this post or share feedback through this form. Your input shapes the future of AI-powered document indexing in Cosmos DB.
