Issues with FSLogix Profiles on Win11 25H2 Multiuser sessionhost's
Hey guys we have currently lot of issues with AVD and FSLogix 26.01. There seems to be an issue that the profile container isnt't unmounted correctly. We have lot's of users who are not able to login correctly because the profile can't be mounted because its already in use by another process. I'm currently looking what could cause that. We use a Azure files storage were i don't see any issues. It looks like a process within the userprofile is blocking the unload of the profile. Should i be able to see in the logs of FSLogix which process is causing this. Or what is a effective way to troubleshoot that? Thanks for any help Best regards MarcA Visual Introduction To Azure Fundamentals
Are you a visual learner? Do you like to see "the big picture" before you dive into details? Does seeing visual notes or metaphors help you understand new concepts better, and retain or recall them more effectively? Then this is for you. A Visual Introduction To Azure Fundamentals - the first in a series of visualized modules that I hope will be helpful for anyone exploring Azure Fundamentals, or preparing for the AZ-900 exam! Want to learn more? Check out this accompanying article at A Cloud Guru! Have questions, or want to see other modules visualized similarly? Leave me a comment on this post!How to Build Safe Natural Language-Driven APIs
TL;DR Building production natural language APIs requires separating semantic parsing from execution. Use LLMs to translate user text into canonical structured requests (via schemas), then execute those requests deterministically. Key patterns: schema completion for clarification, confidence gates to prevent silent failures, code-based ontologies for normalization, and an orchestration layer. This keeps language as input, not as your API contract. Introduction APIs that accept natural language as input are quickly becoming the norm in the age of agentic AI apps and LLMs. From search and recommendations to workflows and automation, users increasingly expect to "just ask" and get results. But treating natural language as an API contract introduces serious risks in production systems: Nondeterministic behavior Prompt-driven business logic Difficult debugging and replay Silent failures that are hard to detect In this post, I'll describe a production-grade architecture for building safe, natural language-driven APIs: one that embraces LLMs for intent discovery and entity extraction while preserving the determinism, observability, and reliability that backend systems require. This approach is based on building real systems using Azure OpenAI and LangGraph, and on lessons learned the hard way. The Core Problem with Natural Language APIs Natural language is an excellent interface for humans. It is a poor interface for systems. When APIs accept raw text directly and execute logic based on it, several problems emerge: The API contract becomes implicit and unversioned Small prompt changes cause behavioral changes Business logic quietly migrates into prompts In short: language becomes the contract, and that's fragile. The solution is not to avoid natural language, but to contain it. A Key Principle: Natural Language Is Input, Not a Contract So how do we contain it? The answer lies in treating natural language fundamentally differently than we treat traditional API inputs. The most important design decision we made was this: Natural language should be translated into structure, not executed directly. That single principle drives the entire architecture. Instead of building "chatty APIs," we split responsibilities clearly: Natural language is used for intent discovery and entity extraction Structured data is used for execution Two Explicit API Layers This principle translates into a concrete architecture with two distinct API layers, each with a single, clear responsibility. 1. Semantic Parse API (Natural Language → Structure) This API: Accepts user text Extracts intent and entities using LLMs Completes a predefined schema Asks clarifying questions when required Returns a canonical, structured request Does not execute business logic Think of this as a compiler, not an engine. 2. Structured Execution API (Structure → Action) This API: Accepts only structured input Calls downstream systems to process the request and get results Is deterministic and versioned Contains no natural language handling Is fully testable and replayable This is where execution happens. Why This Separation Matters Separating these layers gives you: A stable, versionable API contract Freedom to improve NLP without breaking clients Clear ownership boundaries Deterministic execution paths Most importantly, it prevents LLM behavior from leaking into core business logic. Canonical Schemas Are the Backbone Now that we've established the two-layer architecture, let's dive into what makes it work: canonical schemas. Each supported intent is defined by a canonical schema that lives in code. Example (simplified): This schema is used when a user is looking for similar product recommendations. The entities capture which product to use as reference and how to bias the recommendations toward price or quality. { "intent": "recommend_similar", "entities": { "reference_product_id": "string", "price_bias": "number (-1 to 1)", "quality_bias": "number (-1 to 1)" } } Schemas define: Required vs optional fields Allowed ranges and types Validation rules They are the contract, not the prompt. When a user says "show me products like the blue backpack but cheaper", the LLM extracts: Intent: recommend_similar reference_product_id: "blue_backpack_123" price_bias: -0.8 (strongly prefer cheaper) quality_bias: 0.0 (neutral) The schema ensures that even if the user phrased it as "find alternatives to item 123 with better pricing" or "cheaper versions of that blue bag", the output is always the same structure. The natural language variation is absorbed at the semantic layer. The execution layer receives a consistent, validated request every time. This decoupling is what makes the system maintainable. Schema Completion, Not Free-Form Chat But what happens when the user's input doesn't contain all the information needed to complete the schema? This is where structured clarification comes in. A common misconception is that clarification means "chatting until it feels right." In production systems, clarification is schema completion. If required fields are missing or ambiguous, the semantic API responds with: What information is missing A targeted clarification question The current schema state Example response: { "status": "needs_clarification", "missing_fields": ["reference_product_id"], "question": "Which product should I compare against?", "state": { "intent": "recommend_similar", "entities": { "reference_product_id": null, "price_bias": -0.3, "quality_bias": 0.4 } } } The state object is the memory. The API itself remains stateless. A Complete Conversation Flow To illustrate how schema completion works in practice, here's a full conversation flow where the user's initial request is missing required information: Initial Request: User: "Show me cheaper alternatives with good quality" API Response (needs clarification): { "status": "needs_clarification", "missing_fields": ["reference_product_id"], "question": "Which product should I compare against?", "state": { "intent": "recommend_similar", "entities": { "reference_product_id": null, "price_bias": -0.3, "quality_bias": 0.4 } } } Follow-up Request: User: "The blue backpack" Client sends: { "user_input": "The blue backpack", "state": { "intent": "recommend_similar", "entities": { "reference_product_id": null, "price_bias": -0.3, "quality_bias": 0.4 } } } API Response (complete): { "status": "complete", "canonical_request": { "intent": "recommend_similar", "entities": { "reference_product_id": "blue_backpack_123", "price_bias": -0.3, "quality_bias": 0.4 } } } The client passes the state back with each clarification. The API remains stateless, while the client manages the conversation context. Once complete, the canonical_request can be sent directly to the execution API. Why LangGraph Fits This Problem Perfectly With schemas and clarification flows defined, we need a way to orchestrate the semantic parsing workflow reliably. This is where LangGraph becomes valuable. LangGraph allows semantic parsing to be modeled as a structured, deterministic workflow with explicit decision points: Classify intent: Determine what the user wants to do from a predefined set of supported actions Extract candidate entities: Pull out relevant parameters from the natural language input using the LLM Merge into schema state: Map the extracted values into the canonical schema structure Validate required fields: Check if all mandatory fields are present and values are within acceptable ranges Either complete or request clarification: Return the canonical request if complete, or ask a targeted question if information is missing Each node has a single responsibility. Validation and routing are done in code, not by the LLM. LangGraph provides: Explicit state transitions Deterministic routing Observable execution Safe retries Used this way, it becomes a powerful orchestration tool, not a conversational agent. Confidence Gates Prevent Silent Failures Structured workflows handle the process, but there's another critical safety mechanism we need: knowing when the LLM isn't confident about its extraction. Even when outputs are structurally valid, they may not be reliable. We require the semantic layer to emit a confidence score. If confidence falls below a threshold, execution is blocked and clarification is requested. This simple rule eliminates an entire class of silent misinterpretations that are otherwise very hard to detect. Example: When a user says "Show me items similar to the bag", the LLM might extract: { "intent": "recommend_similar", "confidence": 0.55, "entities": { "reference_product_id": "generic_bag_001", "confidence_scores": { "reference_product_id": 0.4 } } } The overall confidence is low (0.55), and the entity confidence for reference_product_id is very low (0.4) because "the bag" is ambiguous. There might be hundreds of bags in the catalog. Instead of proceeding with a potentially wrong guess, the API responds: { "status": "needs_clarification", "reason": "low_confidence", "question": "I found multiple bags. Did you mean the blue backpack, the leather tote, or the travel duffel?", "confidence": 0.55 } This prevents the system from silently executing the wrong recommendation and provides a better user experience. Lightweight Ontologies (Keep Them in Code) Beyond confidence scoring, we need a way to normalize the variety of terms users might use into consistent canonical values. We also introduced lightweight, code-level ontologies: Allowed intents Required entities per intent Synonym-to-canonical mappings Cross-field validation rules These live in code and configuration, not in prompts. LLMs propose values. Code enforces meaning. Example: Consider these user inputs that all mean the same thing: "Show me cheaper options" "Find budget-friendly alternatives" "I want something more affordable" "Give me lower-priced items" The LLM might extract different values: "cheaper", "budget-friendly", "affordable", "lower-priced". The ontology maps all of these to a canonical value: PRICE_BIAS_SYNONYMS = { "cheaper": -0.7, "budget-friendly": -0.7, "affordable": -0.7, "lower-priced": -0.7, "expensive": 0.7, "premium": 0.7, "high-end": 0.7 } When the LLM extracts "budget-friendly", the code normalizes it to -0.7 for the price_bias field. Similarly, cross-field validation catches logical inconsistencies: if entities["price_bias"] < -0.5 and entities["quality_bias"] > 0.5: return clarification("You want cheaper items with higher quality. This might be difficult. Should I prioritize price or quality?") The LLM proposes. The ontology normalizes. The validation enforces business rules. What About Latency? A common concern with multi-step semantic parsing is performance. In practice, we observed: Intent classification: ~40 ms Entity extraction: ~200 ms Validation and routing: ~1 ms Total overhead: ~250–300 ms. For chat-driven user experiences, this is well within acceptable bounds and far cheaper than incorrect or inconsistent execution. Key Takeaways Let's bring it all together. If you're building APIs that accept natural language in production: Do not make language your API contract Translate language into canonical structure Own schema completion server-side Use LLMs for discovery and extraction, not execution Treat safety and determinism as first-class requirements Natural language is an input format. Structure is the contract. Closing Thoughts LLMs make it easy to build impressive demos. Building safe, reliable systems with them requires discipline. By separating semantic interpretation from execution, and by using tools like Azure OpenAI and LangGraph thoughtfully, you can build natural language-driven APIs that scale, evolve, and behave predictably in production. Hopefully, this architecture saves you a few painful iterations.The Perfect Fusion of GitHub Copilot SDK and Cloud Native
In today's rapidly evolving AI landscape, we've witnessed the transformation from simple chatbots to sophisticated agent systems. As a developer and technology evangelist, I've observed an emerging trend—it's not about making AI omnipotent, but about enabling each AI Agent to achieve excellence in specific domains. Today, I want to share an exciting technology stack: GitHub Copilot SDK (a development toolkit that embeds production-grade agent engines into any application) + Agent-to-Agent (A2A) Protocol (a communication standard enabling standardized agent collaboration) + Cloud Native Deployment (the infrastructure foundation for production systems). Together, these three components enable us to build truly collaborative multi-agent systems. 1. From AI Assistants to Agent Engines: Redefining Capability Boundaries Traditional AI assistants often pursue "omnipotence"—attempting to answer any question you throw at them. However, in real production environments, this approach faces serious challenges: Inconsistent Quality: A single model trying to write code, perform data analysis, and generate creative content struggles to achieve professional standards in each domain Context Pollution: Mixing prompts from different tasks leads to unstable model outputs Difficult Optimization: Adjusting prompts for one task type may negatively impact performance on others High Development Barrier: Building agents from scratch requires handling planning, tool orchestration, context management, and other complex logic GitHub proposed a revolutionary approach—instead of forcing developers to build agent frameworks from scratch, provide a production-tested, programmable agent engine. This is the core value of the GitHub Copilot SDK. Evolution from Copilot CLI to SDK GitHub Copilot CLI is a powerful command-line tool that can: Plan projects and features Modify files and execute commands Use custom agents Delegate tasks to cloud execution Integrate with MCP servers The GitHub Copilot SDK extracts the agentic core behind Copilot CLI and offers it as a programmable layer for any application. This means: You're no longer confined to terminal environments You can embed this agent engine into GUI applications, web services, and automation scripts You gain access to the same execution engine validated by millions of users Just like in the real world, we don't expect one person to be a doctor, lawyer, and engineer simultaneously. Instead, we provide professional tools and platforms that enable professionals to excel in their respective domains. 2. GitHub Copilot SDK: Embedding Copilot CLI's Agentic Core into Any App Before diving into multi-agent systems, we need to understand a key technology: GitHub Copilot SDK. What is GitHub Copilot SDK? GitHub Copilot SDK (now in technical preview) is a programmable agent execution platform. It allows developers to embed the production-tested agentic core from GitHub Copilot CLI directly into any application. Simply put, the SDK provides: Out-of-the-box Agent Loop: No need to build planners, tool orchestration, or context management from scratch Multi-model Support: Choose different AI models (like GPT-4, Claude Sonnet) for different task phases Tool and Command Integration: Built-in file editing, command execution, and MCP server integration capabilities Streaming Real-time Responses: Support for progress updates on long-running tasks Multi-language Support: SDKs available for Node.js, Python, Go, and .NET Why is the SDK Critical for Building Agents? Building an agentic workflow from scratch is extremely difficult. You need to handle: Context management across multiple conversation turns Orchestration of tools and commands Routing between different models MCP server integration Permission control, safety boundaries, and error handling GitHub Copilot SDK abstracts away all this underlying complexity. You only need to focus on: Defining agent professional capabilities (through Skill files) Providing domain-specific tools and constraints Implementing business logic SDK Usage Examples Python Example (from actual project implementation): from copilot import CopilotClient # Initialize client copilot_client = CopilotClient() await copilot_client.start() # Create session and load Skill session = await copilot_client.create_session({ "model": "claude-sonnet-4.5", "streaming": True, "skill_directories": ["/path/to/skills/blog/SKILL.md"] }) # Send task await session.send_and_wait({ "prompt": "Write a technical blog about multi-agent systems" }, timeout=600) Skill System: Professionalizing Agents While the SDK provides a powerful execution engine, how do we make agents perform professionally in specific domains? The answer is Skill files. A Skill file is a standardized capability definition containing: Capability Declaration: Explicitly tells the system "what I can do" (e.g., blog generation, PPT creation) Domain Knowledge: Preset best practices, standards, and terminology guidelines Workflow: Defines the complete execution path from input to output Output Standards: Ensures generated content meets format and quality requirements Through the combination of Skill files + SDK, we can build truly professional agents rather than generic "jack-of-all-trades assistants." 3. A2A Protocol: Enabling Seamless Agent Collaboration Once we have professional agents, the next challenge is: how do we make them work together? This is the core problem the Agent-to-Agent (A2A) Protocol aims to solve. Three Core Mechanisms of A2A Protocol 1. Agent Discovery (Service Discovery) Each agent exposes its capability card through the standardized /.well-known/agent-card.json endpoint, acting like a business card that tells other agents "what I can do": { "name": "blog_agent", "description": "Blog generation with DeepSearch", "primaryKeywords": ["blog", "article", "write"], "skills": [{ "id": "blog_generation", "tags": ["blog", "writing"], "examples": ["Write a blog about..."] }], "capabilities": { "streaming": true } } 2. Intelligent Routing The Orchestrator matches tasks with agent capabilities through scoring. The project's routing algorithm implements keyword matching and exclusion detection: Positive Matching: If a task contains an agent's primaryKeywords, score +0.5 Negative Exclusion: If a task contains other agents' keywords, score -0.3 This way, when users say "write a blog about cloud native," the system automatically selects the Blog Agent; when they say "create a tech presentation PPT," it routes to the PPT Agent. 3. SSE Streaming (Real-time Streaming) For time-consuming tasks (like generating a 5000-word blog), A2A uses Server-Sent Events to push real-time progress, allowing users to see the agent working instead of just waiting. This is crucial for user experience. 4. Cloud Native Deployment: Making Agent Systems Production-Ready Even the most powerful technology is just a toy if it can't be deployed to production environments. This project demonstrates a complete deployment of a multi-agent system to a cloud-native platform (Azure Container Apps). Why Choose Cloud Native? Elastic Scaling: When blog generation requests surge, the Blog Agent can auto-scale; it scales down to zero during idle times to save costs Independent Evolution: Each agent has its own Docker image and deployment pipeline; updating the Blog Agent doesn't affect the PPT Agent Fault Isolation: If one agent crashes, it won't bring down the entire system; the Orchestrator automatically degrades Global Distribution: Through Azure Container Apps, agents can be deployed across multiple global regions to reduce latency Container Deployment Essentials Each agent in the project has a standardized Dockerfile: FROM python:3.12-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY . . EXPOSE 8001 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8001"] Combined with the deploy-to-aca.sh script, one-click deployment to Azure: # Build and push image az acr build --registry myregistry --image blog-agent:latest . # Deploy to Container Apps az containerapp create \ --name blog-agent \ --resource-group my-rg \ --environment my-env \ --image myregistry.azurecr.io/blog-agent:latest \ --secrets github-token=$COPILOT_TOKEN \ --env-vars COPILOT_GITHUB_TOKEN=secretref:github-token 5. Real-World Results: From "Works" to "Works Well" Let's see how this system performs in real scenarios. Suppose a user initiates a request: "Write a technical blog about Kubernetes multi-tenancy security, including code examples and best practices" System Execution Flow: Orchestrator receives the request and scans all agents' capability cards Keyword matching: "write" + "blog" → Blog Agent scores 1.0, PPT Agent scores 0.0 Routes to Blog Agent, loads technical writing Skill Blog Agent initiates DeepSearch to collect latest K8s security materials SSE real-time push: "Collecting materials..." → "Generating outline..." → "Writing content..." Returns complete blog after 5 minutes, including code highlighting, citation sources, and best practices summary Compared to traditional "omnipotent" AI assistants, this system's advantages: ✅ Professionalism: Blog Agent trained with technical writing Skills produces content with clear structure, accurate terminology, and executable code ✅ Visibility: Users see progress throughout, knowing what the AI is doing ✅ Extensibility: Adding new agents (video script, data analysis) in the future requires no changes to existing architecture 6. Key Technical Challenges and Solutions Challenge 1: Inaccurate Agent Capability Descriptions Leading to Routing Errors Solution: Define clear primaryKeywords and examples in Agent Cards Implement exclusion detection mechanism to prevent tasks from being routed to unsuitable agents Challenge 2: Poor User Experience for Long-Running Tasks Solution: Fully adopt SSE streaming, pushing working/completed/error status in real-time Display progress hints in status messages so users know what the system is doing Challenge 3: Sensitive Information Leakage Risk Solution: Use Azure Key Vault or Container Apps Secrets to manage GitHub Tokens Inject via environment variables, never hardcode in code or images Check required environment variables in deployment scripts to prevent configuration errors 7. Future Outlook: SDK-Driven Multi-Agent Ecosystem This project is just the beginning. As GitHub Copilot SDK and A2A Protocol mature, we can build richer agent ecosystems: Actual SDK Application Scenarios According to GitHub's official blog, development teams have already used the Copilot SDK to build: YouTube Chapter Generator: Automatically generates timestamped chapter markers for videos Custom Agent GUIs: Visual agent interfaces for specific business scenarios Speech-to-Command Workflows: Control desktop applications through voice AI Battle Games: Interactive competitive experiences with AI Intelligent Summary Tools: Automatic extraction and summarization of key information Multi-Agent System Evolution Directions 🏪 Agent Marketplace: Developers can publish specialized agents (legal documents, medical reports, etc.) that plug-and-play via A2A protocol 🔗 Cascade Orchestration: Orchestrator automatically breaks down complex tasks, calling multiple agents collaboratively (e.g., "write blog + generate images + create PPT") 🌐 Cross-Platform Interoperability: Based on A2A standards, agents developed by different companies can call each other, breaking down data silos ⚙️ Automated Workflows: Delegate routine repetitive work to agent chains, letting humans focus on creative work 🎯 Vertical Domain Specialization: Combined with Skill files, build high-precision agents in professional fields like finance, healthcare, and legal Core Value of the SDK The significance of GitHub Copilot SDK lies in: it empowers every developer to become a builder of agent systems. You don't need deep learning experts, you don't need to implement agent frameworks yourself, and you don't even need to manage GPU clusters. You only need to: Install the SDK (npm install github/copilot-sdk) Define your business logic and tools Write Skill files describing professional capabilities Call the SDK's execution engine And you can build production-grade intelligent agent applications. Summary: From Demo to Production GitHub Copilot SDK + A2A + Cloud Native isn't three independent technology stacks, but a complete methodology: GitHub Copilot SDK provides an out-of-the-box agent execution engine—handling planning, tool orchestration, context management, and other underlying complexity Skill files enable agents with domain-specific professional capabilities—defining best practices, workflows, and output standards A2A Protocol enables standardized communication and collaboration between agents—implementing service discovery, intelligent routing, and streaming Cloud Native makes the entire system production-ready—containerization, elastic scaling, fault isolation For developers, this means we no longer need to build agent frameworks from scratch or struggle with the black magic of prompt engineering. We only need to: Use GitHub Copilot SDK to obtain a production-grade agent execution engine Write domain-specific Skill files to define professional capabilities Follow A2A protocol to implement standard interfaces between agents Deploy to cloud platforms through containerization And we can build AI Agent systems that are truly usable, well-designed, and production-ready. 🚀 Start Building Complete project code is open source: https://github.com/kinfey/Multi-AI-Agents-Cloud-Native/tree/main/code/GitHubCopilotAgents_A2A Follow the README guide and deploy your first Multi-Agent system in 30 minutes! References GitHub Copilot SDK Official Announcement - Build an agent into any app with the GitHub Copilot SDK GitHub Copilot SDK Repository - github.com/github/copilot-sdk A2A Protocol Official Specification - a2a-protocol.org/latest/ Project Source Code - Multi-AI-Agents-Cloud-Native Azure Container Apps Documentation - learn.microsoft.com/azure/container-appsLogic Apps Aviators Newsletter - February 2026
In this issue: Ace Aviator of the Month News from our product group News from our community Ace Aviator of the Month February 2026’s Ace Aviator: Camilla Bielk What's your role and title? What are your responsibilities? I’m a developer and solution architect, working as a consultant (at XLENT) in teams involved across the integration lifecycle—from understanding business needs and building new solutions to operations. Previously I worked a lot with BizTalk and with customers using both Azure and BizTalk. In my current role, I work exclusively with Azure. Can you give us some insights into your day-to-day activities and what a typical day in your role looks like? A typical day starts by syncing operational status with the team to ensure everything is running as expected (Logic Apps, Service Bus, Functions, API Management, etc.) and discussing any findings from the previous day. Then I continue with ongoing work, ranging from stakeholder meetings and designing new integration flows to C# development, operations, and maintenance of the existing landscape. I enjoy being involved across the entire chain. Working closely with operational issues provides valuable input into design decisions, and vice versa, which strengthens me as a designer and architect. What motivates and inspires you to be an active member of the Aviators/Microsoft community? The openness to feedback and knowledge sharing from both community developers and the product team motivates me to stay active, continuously learn, and become the best consultant I can be. Passing knowledge to new team members is rewarding and completes the circle. Integration conferences are inspiring - networking with amazing people and discussing the technology we use every day. That’s also why we started the Nordic Integration Summit: to give more people the chance to take part in such an inspiring event. Looking back, what advice do you wish you had been given earlier that you'd now share with those looking to get into STEM/technology? Your social skills are more valuable than you might think. Teamwork is everything, and understanding business needs—even from non-technical colleagues—is just as important as technology. Listen to your heart and find environments where you can thrive. Programming languages change over time; the real takeaway is mastering common concepts and patterns and developing the ability to learn continuously. Stay curious and embrace new things as they come! What has helped you grow professionally? 100% of my professional growth comes from working with kind, humble, and collaborative people—teams where knowledge is shared freely in all directions, mistakes are allowed, and learning from them is encouraged. If you had a magic wand that could create a feature in Logic Apps, what would it be and why? Better cost transparency—clearly showing which actions drive costs (executions, retries, data volume, logging) with visibility before production deployment. Design-time guidance that warns about expensive operations, anti-patterns, and inefficient configurations. News from our product group Introducing Unit Test Agent Profiles for Logic Apps & Data Maps Focused unit test agent profiles help Logic Apps Standard teams discover workflows and data maps, write reusable specifications, generate typed mocks/test data, and implement MSTest suites against the Automated Test SDK. The approach promotes spec-first testing, consistent artifacts, and reliable validations across scenarios (happy path, error handling, boundaries). The project demonstrates how GitHub Copilot custom agents and prompts can accelerate unit test authoring while enforcing constraints for maintainability in enterprise integrations. Automated Test Framework - Missing Tests in Test Explorer If Logic Apps Standard tests disappear from VS Code’s Test Explorer, the cause is typically a MSTest version mismatch introduced by a recent C# DevKit update. The fix is to update package references in the project (MSTest, Test SDK, and related dependencies) to supported versions. After adjusting packages and restarting VS Code, tests reappear and run as expected. The extension is being updated, but existing projects should apply the manual changes to restore a stable testing experience. Upcoming Agentic Azure Logic Apps Workshops Free workshops introduce Agentic Business Processes in Azure Logic Apps, including MCP Server integrations and agent loop patterns. Sessions cover connecting the enterprise with MCP Servers from Copilot Studio and building agentic workflows in a day using Logic Apps (Standard). Registration links are provided, with dates set in January. These events help practitioners explore agent tools, orchestration patterns, and practical scenarios for intelligent automation in modern integration landscapes. News from our community Enterprise AI ≠ Copilot Post by Al Ghoniem, MBA Deploying Copilot is not the same as building an enterprise AI strategy. This article distinguishes between adopting a product versus developing a core organizational capability. It explores why many AI rollouts stall when treated as point solutions and outlines what it takes to embed AI as a strategic, scalable function across the enterprise. For leaders evaluating their AI roadmaps, this piece offers a framework for thinking beyond tools toward sustainable transformation. BizTalk Server and WinSCP Error Post by Sandro Pereira A common SFTP adapter issue resurfaces when BizTalk Server cumulative updates are applied. This troubleshooting guide explains why the WinSCPnet assembly fails to load after upgrading from CU5 to CU6 and provides a version compatibility matrix for BizTalk Server 2020. It includes step-by-step instructions to resolve the error by copying the correct WinSCP binaries to the BizTalk installation folder, along with an automated PowerShell script for streamlined remediation. More on finding application registrations used by Logic Apps Post by Mikael Sand Building on earlier work around API connection discovery, this post extends KQL queries to find Logic Apps that authenticate via Client ID and secret in HTTP actions. Using Azure Resource Graph Explorer, the technique scans workflow parameters to identify application registration references. While the approach assumes parameters are used for credentials, it provides a practical method for auditing which Logic Apps depend on a given app registration across subscriptions. MCP Servers in Azure Logic Apps Agent Loops (Step-by-Step) Video by Stephen W. Thomas This walkthrough demonstrates how to move tool logic out of Logic App Agent loops and into reusable Model Context Protocol servers. The video covers setting up an MCP server, registering tools, and invoking them from within an agent workflow. By decoupling tool definitions from orchestration logic, developers can build modular, maintainable agentic systems that scale across multiple workflows and scenarios. From Rigid Choreography to Intelligent Collaboration: Agentic Orchestration as the Evolution of SOA Post by Steef-Jan Wiggers Integration has evolved from rigid, deterministic workflows to adaptive, goal-driven orchestrations powered by intelligent agents. This article traces the journey from BizTalk-era SOA composites to modern agentic patterns, explaining how AI-enabled orchestrators dynamically plan, self-correct, and communicate intent rather than follow fixed sequences. With examples from Azure Logic Apps Agent Loop, it shows how integration professionals can leverage existing connectors and APIs as tools within reasoning-based architectures. Azure Integrations That Actually Work in Production Post by Devarajan Gurusamy Architecture diagrams often look perfect in presentations but fail under real-world conditions. This article examines what it takes to build Azure integrations that survive production workloads, covering resilience patterns, error handling strategies, and operational considerations that separate proof-of-concepts from enterprise-grade solutions. It offers practical guidance for teams moving beyond demos toward reliable, maintainable integration implementations. Configuring BizTalk Server Backup Jobs for Azure Blob Storage Post by Sandro Pereira Modernizing BizTalk infrastructure can start with small, targeted improvements. This guide shows how to redirect native BizTalk backup jobs to Azure Blob Storage using SQL Server’s BACKUP TO URL feature. It covers generating SAS tokens, creating SQL credentials, updating job steps, and validating backups. The approach improves disaster recovery posture with geo-redundant cloud storage while keeping the BizTalk environment unchanged—no new components or custom code required. Building an AI Agent with Logic Apps Consumption and Agent Loop Post by Stephen W. Thomas Logic Apps Consumption with AgentLoop offers a fast path to practical AI agents, particularly for experimentation and low-volume workloads. This post walks through building a stateful poker-playing agent, demonstrating tool orchestration, agent instructions, and execution tracing. It compares Consumption and Standard tiers, explores cost efficiency, and shows how to offload reasoning to external models. At roughly a dollar for 25 runs, it’s an accessible entry point for agentic development. Microsoft Agent Framework Post by Al Ghoniem, MBA Microsoft’s open-source Agent Framework provides a comprehensive toolkit for building, orchestrating, and deploying AI agents in Python and .NET. It features graph-based workflows with streaming and checkpointing, multi-agent patterns, built-in observability via OpenTelemetry, and support for multiple LLM providers. The repository includes quickstart samples, migration guides from Semantic Kernel and AutoGen, and experimental labs for benchmarking and reinforcement learning. Storage Account: You do not have permissions to list the data using your user account with Entra ID Post by Sandro Pereira Being an Owner of an Azure Storage Account does not automatically grant access to its data. This common confusion trips up many developers working with Logic Apps and blob storage. The article explains the distinction between management roles and data-plane permissions, then walks through assigning Storage Blob Data roles via Access Control (IAM) to resolve the “You do not have permissions” error when authenticating with Microsoft Entra ID. Using Office 365 Outlook Connector in Logic Apps Standard Post by Srikanth Gunnala When configuring Office 365 Outlook (V2) with Managed Identity in Logic Apps Standard, the workflow designer may fail to auto-bind existing API connections—even when runtime works correctly. This post details the issue, explaining how a null referenceName in the workflow JSON causes the trigger to appear unbound. The fix involves manually setting the connection reference name in the workflow definition, after which the designer immediately recognizes the connection. Inside Logic Apps Standard: Understanding Compute Units (CU) for Storage Scaling Post by Daniel Jonathan High-volume Logic Apps can hit Azure Storage throttling limits. This deep-dive explains how Compute Units enable horizontal storage scaling by distributing workflow execution data across up to 32 storage accounts. It covers CU routing via Run ID suffixes, table distribution patterns, and configuration via host.json. Understanding this architecture is essential for building monitoring tools, querying run histories programmatically, or troubleshooting performance at scale. E59 - Roles & Personas Video by Sebastian Meyer This episode recaps highlights from SAP TechEd and Microsoft Ignite, with a focus on the SAP Innovation Guide and Logic Apps announcements. It explores roles and personas in the integration space, discussing how different team members contribute to enterprise integration projects. The conversation provides context for practitioners following both SAP and Microsoft ecosystems and offers insights into recent platform developments relevant to hybrid integration scenarios. Architecting Trust: Leveraging Microsoft Foundry to Solve AI Governance Challenges Post by Kent Weare As organizations scale AI deployments, governance becomes critical. This article explores how Microsoft Foundry addresses common AI governance challenges including model management, access controls, auditability, and compliance. It outlines architectural patterns for building trust into AI systems from the ground up and provides guidance for teams navigating the balance between innovation velocity and responsible AI practices in enterprise environments.Building Interactive Agent UIs with AG-UI and Microsoft Agent Framework
Introduction Picture this: You've built an AI agent that analyzes financial data. A user uploads a quarterly report and asks: "What are the top three expense categories?" Behind the scenes, your agent parses the spreadsheet, aggregates thousands of rows, and generates visualizations. All in 20 seconds. But the user? They see a loading spinner. Nothing else. No "reading file" message, no "analyzing data" indicator, no hint that progress is being made. They start wondering: Is it frozen? Should I refresh? The problem isn't the agent's capabilities - it's the communication gap between the agent running on the backend and the user interface. When agents perform multi-step reasoning, call external APIs, or execute complex tool chains, users deserve to see what's happening. They need streaming updates, intermediate results, and transparent progress indicators. Yet most agent frameworks force developers to choose between simple request/response patterns or building custom solutions to stream updates to their UIs. This is where AG-UI comes in. AG-UI is a fairly new event-based protocol that standardizes how agents communicate with user interfaces. Instead of every framework and development team inventing their own streaming solution, AG-UI provides a shared vocabulary of structured events that work consistently across different agent implementations. When an agent starts processing, calls a tool, generates text, or encounters an error, the UI receives explicit, typed events in real time. The beauty of AG-UI is its framework-agnostic design. While this blog post demonstrates integration with Microsoft Agent Framework (MAF), the same AG-UI protocol works with LangGraph, CrewAI, or any other compliant framework. Write your UI code once, and it works with any AG-UI-compliant backend. (Note: MAF supports both Python and .NET - this blog post focuses on the Python implementation.) TL;DR The Problem: Users don't get real-time updates while AI agents work behind the scenes - no progress indicators, no transparency into tool calls, and no insight into what's happening. The Solution: AG-UI is an open, event-based protocol that standardizes real-time communication between AI agents and user interfaces. Instead of each development team and framework inventing custom streaming solutions, AG-UI provides a shared vocabulary of structured events (like TOOL_CALL_START, TEXT_MESSAGE_CONTENT, RUN_FINISHED) that work across any compliant framework. Key Benefits: Framework-agnostic - Write UI code once, works with LangGraph, Microsoft Agent Framework, CrewAI, and more Real-time observability - See exactly what your agent is doing as it happens Server-Sent Events - Built on standard HTTP for universal compatibility Protocol-managed state - No manual conversation history tracking In This Post: You'll learn why AG-UI exists, how it works, and build a complete working application using Microsoft Agent Framework with Python - from server setup to client implementation. What You'll Learn This blog post walks through: Why AG-UI exists - how agent-UI communication has evolved and what problems current approaches couldn't solve How the protocol works - the key design choices that make AG-UI simple, reliable, and framework-agnostic Protocol architecture - the generic components and how AG-UI integrates with agent frameworks Building an AG-UI application - a complete working example using Microsoft Agent Framework with server, client, and step-by-step setup Understanding events - what happens under the hood when your agent runs and how to observe it Thinking in events - how building with AG-UI differs from traditional APIs, and what benefits this brings Making the right choice - when AG-UI is the right fit for your project and when alternatives might be better Estimated reading time: 15 minutes Who this is for: Developers building AI agents who want to provide real-time feedback to users, and teams evaluating standardized approaches to agent-UI communication To appreciate why AG-UI matters, we need to understand the journey that led to its creation. Let's trace how agent-UI communication has evolved through three distinct phases. The Evolution of Agent-UI Communication AI agents have become more capable over time. As they evolved, the way they communicated with user interfaces had to evolve as well. Here's how this evolution unfolded. Phase 1: Simple Request/Response In the early days of AI agent development, the interaction model was straightforward: send a question, wait for an answer, display the result. This synchronous approach mirrored traditional API calls and worked fine for simple scenarios. # Simple, but limiting response = agent.run("What's the weather in Paris?") display(response) # User waits... and waits... Works for: Quick queries that complete in seconds, simple Q&A interactions where immediate feedback and interactivity aren't critical. Breaks down: When agents need to call multiple tools, perform multi-step reasoning, or process complex queries that take 30+ seconds. Users see nothing but a loading spinner, with no insight into what's happening or whether the agent is making progress. This creates a poor user experience and makes it impossible to show intermediate results or allow user intervention. Recognizing these limitations, development teams began experimenting with more sophisticated approaches. Phase 2: Custom Streaming Solutions As agents became more sophisticated, teams recognized the need for incremental feedback and interactivity. Rather than waiting for the complete response, they implemented custom streaming solutions to show partial results as they became available. # Every team invents their own format for chunk in agent.stream("What's the weather?"): display(chunk) # But what about tool calls? Errors? Progress? This was a step forward for building interactive agent UIs, but each team solved the problem differently. Also, different frameworks had incompatible approaches - some streamed only text tokens, others sent structured JSON, and most provided no visibility into critical events like tool calls or errors. The problem: No standardization across frameworks - client code that works with LangGraph won't work with Crew AI, requiring separate implementations for each agent backend Each implementation handles tool calls differently - some send nothing during tool execution, others send unstructured messages Complex state management - clients must track conversation history, manage reconnections, and handle edge cases manually The industry needed a better solution - a common protocol that could work across all frameworks while maintaining the benefits of streaming. Phase 3: Standardized Protocol (AG-UI) AG-UI emerged as a response to the fragmentation problem. Instead of each framework and development team inventing their own streaming solution, AG-UI provides a shared vocabulary of events that work consistently across different agent implementations. # Standardized events everyone understands async for event in agent.run_stream("What's the weather?"): if event.type == "TEXT_MESSAGE_CONTENT": display_text(event.delta) elif event.type == "TOOL_CALL_START": show_tool_indicator(event.tool_name) elif event.type == "TOOL_CALL_RESULT": show_tool_result(event.result) The key difference is structured observability. Rather than guessing what the agent is doing from unstructured text, clients receive explicit events for every stage of execution: when the agent starts, when it generates text, when it calls a tool, when that tool completes, and when the entire run finishes. What's different: A standardized vocabulary of event types, complete observability into agent execution, and framework-agnostic clients that work with any AG-UI-compliant backend. You write your UI code once, and it works whether the backend uses Microsoft Agent Framework, LangGraph, or any other framework that speaks AG-UI. Now that we've seen why AG-UI emerged and what problems it solves, let's examine the specific design decisions that make the protocol work. These choices weren't arbitrary - each one addresses concrete challenges in building reliable, observable agent-UI communication. The Design Decisions Behind AG-UI Why Server-Sent Events (SSE)? Aspect WebSockets SSE (AG-UI) Complexity Bidirectional Unidirectional (simpler) Firewall/Proxy Sometimes blocked Standard HTTP Reconnection Manual implementation Built-in browser support Use case Real-time games, chat Agent responses (one-way) For agent interactions, you typically only need server→client communication, making SSE a simpler choice. SSE solves the transport problem - how events travel from server to client. But once connected, how does the protocol handle conversation state across multiple interactions? Why Protocol-Managed Threads? # Without protocol threads (client manages): conversation_history = [] conversation_history.append({"role": "user", "content": message}) response = agent.complete(conversation_history) conversation_history.append({"role": "assistant", "content": response}) # Complex, error-prone, doesn't work with multiple clients # With AG-UI (protocol manages): thread = agent.get_new_thread() # Server creates and manages thread agent.run_stream(message, thread=thread) # Server maintains context # Simple, reliable, shareable across clients With transport and state management handled, the final piece is the actual messages flowing through the connection. What information should the protocol communicate, and how should it be structured? Why Standardized Event Types? Instead of parsing unstructured text, clients get typed events: RUN_STARTED - Agent begins (start loading UI) TEXT_MESSAGE_CONTENT - Text chunk (stream to user) TOOL_CALL_START - Tool invoked (show "searching...", "calculating...") TOOL_CALL_RESULT - Tool finished (show result, update UI) RUN_FINISHED - Complete (hide loading) This lets UIs react intelligently without custom parsing logic. Now that we understand the protocol's design choices, let's see how these pieces fit together in a complete system. Architecture Overview Here's how the components interact: The communication between these layers relies on a well-defined set of event types. Here are the core events that flow through the SSE connection: Core Event Types AG-UI provides a standardized set of event types to describe what's happening during an agent's execution: RUN_STARTED - agent begins execution TEXT_MESSAGE_START, TEXT_MESSAGE_CONTENT, TEXT_MESSAGE_END - streaming segments of text TOOL_CALL_START, TOOL_CALL_ARGS, TOOL_CALL_END, TOOL_CALL_RESULT - tool execution events RUN_FINISHED - agent has finished execution RUN_ERROR - error information This model lets the UI update as the agent runs, rather than waiting for the final response. The generic architecture above applies to any AG-UI implementation. Now let's see how this translates to Microsoft Agent Framework. AG-UI with Microsoft Agent Framework While AG-UI is framework-agnostic, this blog post demonstrates integration with Microsoft Agent Framework (MAF) using Python. MAF is available in both Python and .NET, giving you flexibility to build AG-UI applications in your preferred language. Understanding how MAF implements the protocol will help you build your own applications or work with other compliant frameworks. Integration Architecture The Microsoft Agent Framework integration involves several specialized layers that handle protocol translation and execution orchestration: Understanding each layer: FastAPI Endpoint - Handles HTTP requests and establishes SSE connections for streaming AgentFrameworkAgent - Protocol wrapper that translates between AG-UI events and Agent Framework operations Orchestrators - Manage execution flow, coordinate tool calling sequences, and handle state transitions ChatAgent - Your agent implementation with instructions, tools, and business logic ChatClient - Interface to the underlying language model (Azure OpenAI, OpenAI, or other providers) The good news? When you call add_agent_framework_fastapi_endpoint, all the middleware layers are configured automatically. You simply provide your ChatAgent, and the integration handles protocol translation, event streaming, and state management behind the scenes. Now that we understand both the protocol architecture and the Microsoft Agent Framework integration, let's build a working application. Hands-On: Building Your First AG-UI Application This section demonstrates how to build an AG-UI server and client using Microsoft Agent Framework and FastAPI. Prerequisites Before building your first AG-UI application, ensure you have: Python 3.10 or later installed Basic understanding of async/await patterns in Python Azure CLI installed and authenticated (az login) Azure OpenAI service endpoint and deployment configured (setup guide) Cognitive Services OpenAI Contributor role for your Azure OpenAI resource You'll also need to install the AG-UI integration package: pip install agent-framework-ag-ui --pre This automatically installs agent-framework-core, fastapi, and uvicorn as dependencies. With your environment configured, let's create the server that will host your agent and expose it via the AG-UI protocol. Building the Server Let's create a FastAPI server that hosts an AI agent and exposes it via AG-UI: # server.py import os from typing import Annotated from dotenv import load_dotenv from fastapi import FastAPI from pydantic import Field from agent_framework import ChatAgent, ai_function from agent_framework.azure import AzureOpenAIChatClient from agent_framework_ag_ui import add_agent_framework_fastapi_endpoint from azure.identity import DefaultAzureCredential # Load environment variables from .env file load_dotenv() # Validate environment configuration openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") model_deployment = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME") if not openai_endpoint: raise RuntimeError("Missing required environment variable: AZURE_OPENAI_ENDPOINT") if not model_deployment: raise RuntimeError("Missing required environment variable: AZURE_OPENAI_DEPLOYMENT_NAME") # Define tools the agent can use @ai_function def get_order_status( order_id: Annotated[str, Field(description="The order ID to look up (e.g., ORD-001)")] ) -> dict: """Look up the status of a customer order. Returns order status, tracking number, and estimated delivery date. """ # Simulated order lookup orders = { "ORD-001": {"status": "shipped", "tracking": "1Z999AA1", "eta": "Jan 25, 2026"}, "ORD-002": {"status": "processing", "tracking": None, "eta": "Jan 23, 2026"}, "ORD-003": {"status": "delivered", "tracking": "1Z999AA3", "eta": "Delivered Jan 20"}, } return orders.get(order_id, {"status": "not_found", "message": "Order not found"}) # Initialize Azure OpenAI client chat_client = AzureOpenAIChatClient( credential=DefaultAzureCredential(), endpoint=openai_endpoint, deployment_name=model_deployment, ) # Configure the agent with custom instructions and tools agent = ChatAgent( name="CustomerSupportAgent", instructions="""You are a helpful customer support assistant. You have access to a get_order_status tool that can look up order information. IMPORTANT: When a user mentions an order ID (like ORD-001, ORD-002, etc.), you MUST call the get_order_status tool to retrieve the actual order details. Do NOT make up or guess order information. After calling get_order_status, provide the actual results to the user in a friendly format.""", chat_client=chat_client, tools=[get_order_status], ) # Initialize FastAPI application app = FastAPI( title="AG-UI Customer Support Server", description="Interactive AI agent server using AG-UI protocol with tool calling" ) # Mount the AG-UI endpoint add_agent_framework_fastapi_endpoint(app, agent, path="/chat") def main(): """Entry point for the AG-UI server.""" import uvicorn print("Starting AG-UI server on http://localhost:8000") uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info") # Run the application if __name__ == "__main__": main() What's happening here: We define a tool: get_order_status with the AI_function decorator Use Annotated and Field for parameter descriptions to help the agent understand when and how to use the tool We create an Azure OpenAI chat client with credential authentication The ChatAgent is configured with domain-specific instructions and the tools parameter add_agent_framework_fastapi_endpoint automatically handles SSE streaming and tool execution The server exposes the agent at the /chat endpoint Note: This example uses Azure OpenAI, but AG-UI works with any chat model. You can also integrate with Azure AI Foundry's model catalog or use other LLM providers. Tool calling is supported by most modern LLMs including GPT-4, GPT-4o, and Claude models. To run this server: # Set your Azure OpenAI credentials export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o" # Start the server python server.py With your server running and exposing the AG-UI endpoint, the next step is building a client that can connect and consume the event stream. Streaming Results to Clients With the server running, clients can connect and stream events as the agent processes requests. Here's a Python client that demonstrates the streaming capabilities: # client.py import asyncio import os from dotenv import load_dotenv from agent_framework import ChatAgent, FunctionCallContent, FunctionResultContent from agent_framework_ag_ui import AGUIChatClient # Load environment variables from .env file load_dotenv() async def interactive_chat(): """Interactive chat session with streaming responses.""" # Connect to the AG-UI server base_url = os.getenv("AGUI_SERVER_URL", "http://localhost:8000/chat") print(f"Connecting to: {base_url}\n") # Initialize the AG-UI client client = AGUIChatClient(endpoint=base_url) # Create a local agent representation agent = ChatAgent(chat_client=client) # Start a new conversation thread conversation_thread = agent.get_new_thread() print("Chat started! Type 'exit' or 'quit' to end the session.\n") try: while True: # Collect user input user_message = input("You: ") # Handle empty input if not user_message.strip(): print("Please enter a message.\n") continue # Check for exit commands if user_message.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break # Stream the agent's response print("Agent: ", end="", flush=True) # Track tool calls to avoid duplicate prints seen_tools = set() async for update in agent.run_stream(user_message, thread=conversation_thread): # Display text content if update.text: print(update.text, end="", flush=True) # Display tool calls and results for content in update.contents: if isinstance(content, FunctionCallContent): # Only print each tool call once if content.call_id not in seen_tools: seen_tools.add(content.call_id) print(f"\n[Calling tool: {content.name}]", flush=True) elif isinstance(content, FunctionResultContent): # Only print each result once result_id = f"result_{content.call_id}" if result_id not in seen_tools: seen_tools.add(result_id) result_text = content.result if isinstance(content.result, str) else str(content.result) print(f"[Tool result: {result_text}]", flush=True) print("\n") # New line after response completes except KeyboardInterrupt: print("\n\nChat interrupted by user.") except ConnectionError as e: print(f"\nConnection error: {e}") print("Make sure the server is running.") except Exception as e: print(f"\nUnexpected error: {e}") def main(): """Entry point for the AG-UI client.""" asyncio.run(interactive_chat()) if __name__ == "__main__": main() Key features: The client connects to the AG-UI endpoint using AGUIChatClient with the endpoint parameter run_stream() yields updates containing text and content as they arrive Tool calls are detected using FunctionCallContent and displayed with [Calling tool: ...] Tool results are detected using FunctionResultContent and displayed with [Tool result: ...] Deduplication logic (seen_tools set) prevents printing the same tool call multiple times as it streams Thread management maintains conversation context across messages Graceful error handling for connection issues To use the client: # Optional: specify custom server URL export AGUI_SERVER_URL="http://localhost:8000/chat" # Start the interactive chat python client.py Example Session: Connecting to: http://localhost:8000/chat Chat started! Type 'exit' or 'quit' to end the session. You: What's the status of order ORD-001? Agent: [Calling tool: get_order_status] [Tool result: {"status": "shipped", "tracking": "1Z999AA1", "eta": "Jan 25, 2026"}] Your order ORD-001 has been shipped! - Tracking Number: 1Z999AA1 - Estimated Delivery Date: January 25, 2026 You can use the tracking number to monitor the delivery progress. You: Can you check ORD-002? Agent: [Calling tool: get_order_status] [Tool result: {"status": "processing", "tracking": null, "eta": "Jan 23, 2026"}] Your order ORD-002 is currently being processed. - Status: Processing - Estimated Delivery: January 23, 2026 Your order should ship soon, and you'll receive a tracking number once it's on the way. You: exit Goodbye! The client we just built handles events at a high level, abstracting away the details. But what's actually flowing through that SSE connection? Let's peek under the hood. Event Types You'll See As the server streams back responses, clients receive a series of structured events. If you were to observe the raw SSE stream (e.g., using curl), you'd see events like: curl -N http://localhost:8000/chat \ -H "Content-Type: application/json" \ -H "Accept: text/event-stream" \ -d '{"messages": [{"role": "user", "content": "What'\''s the status of order ORD-001?"}]}' Sample event stream (with tool calling): data: {"type":"RUN_STARTED","threadId":"eb4d9850-14ef-446c-af4b-23037acda9e8","runId":"chatcmpl-xyz"} data: {"type":"TEXT_MESSAGE_START","messageId":"e8648880-a9ff-4178-a17d-4a6d3ec3d39c","role":"assistant"} data: {"type":"TOOL_CALL_START","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","toolCallName":"get_order_status","parentMessageId":"e8648880-a9ff-4178-a17d-4a6d3ec3d39c"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"{\""} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"order"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"_id"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"\":\""} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"ORD"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"-"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"001"} data: {"type":"TOOL_CALL_ARGS","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","delta":"\"}"} data: {"type":"TOOL_CALL_END","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y"} data: {"type":"TOOL_CALL_RESULT","messageId":"f048cb0a-a049-4a51-9403-a05e4820438a","toolCallId":"call_GTWj2N3ZyYiiQIjg3fwmiQ8y","content":"{\"status\": \"shipped\", \"tracking\": \"1Z999AA1\", \"eta\": \"Jan 25, 2026\"}","role":"tool"} data: {"type":"TEXT_MESSAGE_START","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","role":"assistant"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":"Your"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" order"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" ORD"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":"-"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":"001"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" has"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" been"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":" shipped"} data: {"type":"TEXT_MESSAGE_CONTENT","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf","delta":"!"} ... (additional TEXT_MESSAGE_CONTENT events streaming the response) ... data: {"type":"TEXT_MESSAGE_END","messageId":"8215fc88-8cb6-4ce4-8bdb-a8715dcd26cf"} data: {"type":"RUN_FINISHED","threadId":"eb4d9850-14ef-446c-af4b-23037acda9e8","runId":"chatcmpl-xyz"} Understanding the flow: RUN_STARTED - Agent begins processing the request TEXT_MESSAGE_START - First message starts (will contain tool calls) TOOL_CALL_START - Agent invokes the get_order_status tool Multiple TOOL_CALL_ARGS events - Arguments stream incrementally as JSON chunks ({"order_id":"ORD-001"}) TOOL_CALL_END - Tool invocation structure complete TOOL_CALL_RESULT - Tool execution finished with result data TEXT_MESSAGE_START - Second message starts (the final response) Multiple TEXT_MESSAGE_CONTENT events - Response text streams word-by-word TEXT_MESSAGE_END - Response message complete RUN_FINISHED - Entire run completed successfully This granular event model enables rich UI experiences - showing tool execution indicators ("Searching...", "Calculating..."), displaying intermediate results, and providing complete transparency into the agent's reasoning process. Seeing the raw events helps, but truly working with AG-UI requires a shift in how you think about agent interactions. Let's explore this conceptual change. The Mental Model Shift Traditional API Thinking # Imperative: Call and wait response = agent.run("What's 2+2?") print(response) # "The answer is 4" Mental model: Function call with return value AG-UI Thinking # Reactive: Subscribe to events async for event in agent.run_stream("What's 2+2?"): match event.type: case "RUN_STARTED": show_loading() case "TEXT_MESSAGE_CONTENT": display_chunk(event.delta) case "RUN_FINISHED": hide_loading() Mental model: Observable stream of events This shift feels similar to: Moving from synchronous to async code Moving from REST to event-driven architecture Moving from polling to pub/sub This mental shift isn't just philosophical - it unlocks concrete benefits that weren't possible with request/response patterns. What You Gain Observability # You can SEE what the agent is doing TOOL_CALL_START: "get_order_status" TOOL_CALL_ARGS: {"order_id": "ORD-001"} TOOL_CALL_RESULT: {"status": "shipped", "tracking": "1Z999AA1", "eta": "Jan 25, 2026"} TEXT_MESSAGE_START: "Your order ORD-001 has been shipped..." Interruptibility # Future: Cancel long-running operations async for event in agent.run_stream(query): if user_clicked_cancel: await agent.cancel(thread_id, run_id) break Transparency # Users see the reasoning process "Looking up order ORD-001..." "Order found: Status is 'shipped'" "Retrieving tracking information..." "Your order has been shipped with tracking number 1Z999AA1..." To put these benefits in context, here's how AG-UI compares to traditional approaches across key dimensions: AG-UI vs. Traditional Approaches Aspect Traditional REST Custom Streaming AG-UI Connection Model Request/Response Varies Server-Sent Events State Management Manual Manual Protocol-managed Tool Calling Invisible Custom format Standardized events Framework Varies Framework-locked Framework-agnostic Browser Support Universal Varies Universal Implementation Simple Complex Moderate Ecosystem N/A Isolated Growing You've now seen AG-UI's design principles, implementation details, and conceptual foundations. But the most important question remains: should you actually use it? Conclusion: Is AG-UI Right for Your Project? AG-UI represents a shift toward standardized, observable agent interactions. Before adopting it, understand where the protocol stands and whether it fits your needs. Protocol Maturity The protocol is stable enough for production use but still evolving: Ready now: Core specification stable, Microsoft Agent Framework integration available, FastAPI/Python implementation mature, basic streaming and threading work reliably. Choose AG-UI If You Building new agent projects - No legacy API to maintain, want future compatibility with emerging ecosystem Need streaming observability - Multi-step workflows where users benefit from seeing each stage of execution Want framework flexibility - Same client code works with any AG-UI-compliant backend Comfortable with evolving standards - Can adapt to protocol changes as it matures Stick with Alternatives If You Have working solutions - Custom streaming working well, migration cost not justified Need guaranteed stability - Mission-critical systems where breaking changes are unacceptable Build simple agents - Single-step request/response without tool calling or streaming needs Risk-averse environment - Large existing implementations where proven approaches are required Beyond individual project decisions, it's worth considering AG-UI's role in the broader ecosystem. The Bigger Picture While this blog post focused on Microsoft Agent Framework, AG-UI's true power lies in its broader mission: creating a common language for agent-UI communication across the entire ecosystem. As more frameworks adopt it, the real value emerges: write your UI once, work with any compliant agent framework. Think of it like GraphQL for APIs or OpenAPI for REST - a standardization layer that benefits the entire ecosystem. The protocol is young, but the problem it solves is real. Whether you adopt it now or wait for broader adoption, understanding AG-UI helps you make informed architectural decisions for your agent applications. Ready to dive deeper? Here are the official resources to continue your AG-UI journey. Resources AG-UI & Microsoft Agent Framework Getting Started with AG-UI (Microsoft Learn) - Official tutorial AG-UI Integration Overview - Architecture and concepts AG-UI Protocol Specification - Official protocol documentation Backend Tool Rendering - Adding function tools Security Considerations - Production security guidance Microsoft Agent Framework Documentation - Framework overview AG-UI Dojo Examples - Live demonstrations UI Components & Integration CopilotKit for Microsoft Agent Framework - React component library Community & Support Microsoft Q&A - Community support Agent Framework GitHub - Source code and issues Related Technologies Azure AI Foundry Documentation - Azure AI platform FastAPI Documentation - Web framework Server-Sent Events (SSE) Specification - Protocol standard This blog post introduces AG-UI with Microsoft Agent Framework, focusing on fundamental concepts and building your first interactive agent application.
Azure Migrate Physical Server Discovery - ServerDiscoveryService.exe Crash Bug
Summary The Azure Migrate appliance for physical server discovery fails to complete discovery due to a crash bug in ServerDiscoveryService.exe. The service successfully connects to target servers but crashes during WSMan transport cleanup before any discovery data is collected. Environment Appliance OS: Windows Server 2022 Standard Evaluation (Build 20348) Appliance Type: Physical server discovery (script-based installation) ServerDiscoveryService.exe Version: 2.0.3300.663 .NET Version: 8.0.22 (CoreCLR 8.0.2225.52707) Target Servers: Windows Server (various) and Linux, all on-premises Discovery Agent Version: 2.0.03300.663 Appliance Configuration Manager Version: 6.1.294.1847 Symptoms Target server validation succeeds in the appliance configuration manager CIM sessions connect successfully (logs show "TestConnection succeeded for CIM Session with HTTP protocol") Connections are immediately disposed with "Disposing all connections when the process is shutdown" No discovery data is collected Azure portal shows error 60001 with misleading "Could not load file or assembly 'Microsoft.Management.Infrastructure'" message Discovery status remains "Discovery Incomplete" for all Windows servers Root Cause The ServerDiscoveryService.exe process crashes repeatedly with an unhandled NullReferenceException in the WSMan transport finalizer. This is visible in the Windows Application Event Log: Application: ServerDiscoveryService.exe CoreCLR Version: 8.0.2225.52707 .NET Version: 8.0.22 Description: The process was terminated due to an unhandled exception. Exception Info: System.NullReferenceException: Object reference not set to an instance of an object. at System.Management.Automation.Remoting.Client.BaseClientTransportManager.CloseAsync() at System.Management.Automation.Remoting.Client.WSManClientSessionTransportManager.CloseAsync() at System.Management.Automation.Remoting.Client.BaseClientTransportManager.Finalize() The crash also triggers an access violation: Faulting application name: ServerDiscoveryService.exe, version: 2.0.3300.663 Exception code: 0xc0000005 Faulting application path: C:\Program Files\Microsoft Azure Server Discovery Service\ServerDiscoveryService.exe These crashes occur approximately every 10 minutes. Troubleshooting Completed Verified manual connectivity works: PowerShell Invoke-Command and New-CimSession both succeed from the appliance to target servers using the same credentials Verified WinRM configuration: Targets have WinRM HTTP listener on port 5985, LocalAccountTokenFilterPolicy is set to 1 Verified assemblies exist: Microsoft.Management.Infrastructure.dll is present in the GAC on both the appliance and target servers Tested both FQDNs and IP addresses: Same failure occurs with both Tested both local and domain credentials: Same failure with properly formatted credentials (domain\user) Verified time synchronization: Appliance clock is accurate Verified appliance is up to date: All components show current versions Tested with fresh appliance: Previously tried OVA-based appliance with similar results; rebuilt using Microsoft's PowerShell script installer on clean Server 2022—same issue Relevant Log Locations C:\ProgramData\Microsoft Azure\Logs\ConfigManager\ClientOperations_*.log - Shows successful CIM connections followed by immediate disposal C:\ProgramData\Microsoft Azure\Logs\ConfigManager\ApplianceOnboarding-Portal-*.log - Shows error 60000 "UnhandledException" with message "Internal error occured." (note: typo is in original) Windows Event Log (Application) - Contains the actual crash stack traces Conclusion This is a code defect in ServerDiscoveryService.exe—a null reference exception in a finalizer is a programming error that cannot be caused by configuration or environmental factors. The service connects successfully but crashes before completing its work. Request Please escalate to the Azure Migrate engineering team for a bug fix in ServerDiscoveryService.exe version 2.0.3300.663.Tableau to Power BI Migration: Semantic Layer-First Approach for Cloud Architects
Author's: Lavanya Sreedhar LavanyaSreedhar, Peter Lo PeterLo, Aryan Anmol aryananmol, Shreya Harvu shreyaharvu and Rafia Aqil Rafia_Aqil In this guide, we provide practical guidance for migrating from Tableau to Power BI, with a focus on technical best practices and architecture. Unifying business intelligence on the Microsoft Fabric platform, enterprises gain closer integration with Microsoft 365 (Teams, Copilot, Excel). For cloud solution architects and BI developers, a successful migration is not just about rebuilding dashboards in a new tool. It requires thoughtful architectural planning and a shift to a more model-centric approach to BI. Why Semantic Layer-First Architecture Matters The Traditional Migration Challenge Most Tableau to Power BI migrations follow a dashboard-centric approach: teams attempt to replicate existing Tableau workbooks, calculated fields, and LOD (Level of Detail) expressions directly into Power BI reports. While this may seem efficient initially, it creates significant downstream challenges: Duplicated logic: Each report embeds its own calculations and business rules, leading to conflicting KPIs across the organization Maintenance overhead: Changes to business logic require updating dozens or hundreds of individual reports Governance gaps: Without centralized definitions, semantic drift occurs—different teams calculate "Revenue" or "Active Customer" differently Scalability issues: As data volumes grow, report-level transformations become performance bottlenecks The Semantic Layer-First Alternative Microsoft's recommended approach centers on semantic models (formerly called datasets)—centralized, governed data models that separate business logic from visualization. In this architecture: The payoff is substantial: when data evolves or business rules change, you update the semantic model once, and all dependent reports automatically reflect the changes—no manual redesign required. Understanding Migration Complexity: Simple to Very Complex Dashboards Not all Tableau dashboards are created equal. The migration strategy should align with dashboard complexity, and the semantic layer approach becomes increasingly valuable as complexity grows. Follow a Step-by-Step Migration Strategy Migrating from Tableau to Power BI is not a one-click effort – it requires a mix of automated and manual refactoring, plus a sound change management plan. Below are key strategies and best practices for a successful migration: Audit your Tableau estate: Start by taking inventory of all existing Tableau workbooks, data sources, and dashboards. Determine what needs to be migrated (focus on high-value, widely used reports first) and identify any redundant or obsolete content that can be retired rather than converted. Conduct a proof-of-concept (PoC): Before migrating everything, pick a representative complex dashboard (or a subset of your data) and perform a pilot migration. This will help you validate that Power BI can connect to your data (e.g. setting up the Power BI gateways for on-premises sources), test performance (Import vs DirectQuery modes), and experiment with replicating key visuals or calculations. Use the PoC to uncover any surprises early – for example, test that any Level of Detail expressions or table calculations in Tableau can be re-created in DAX. The lessons learned here should inform your overall project plan. Use a phased migration approach: Plan to run Tableau and Power BI in parallel for some period, rather than switching everything at once. Migrate in waves – for example, by business unit or subject area – and incorporate user feedback as you go. This phased approach reduces risk and allows your team to improve the process with each iteration. It also gives end users time to adjust gradually. Migrate high-impact dashboards first: Prioritize the migration of key reports and dashboards that are critical to the business or have the most usage. Delivering these early wins will not only surface any technical challenges to solve but will also help demonstrate the value of Power BI’s capabilities to stakeholders. Early success builds buy-in and momentum for the rest of the migration. Reimagine (don’t just replicate) the experience: It’s rarely possible – or desirable – to exactly re-create every Tableau visualization pixel-for-pixel in Power BI. Embrace the opportunity to focus on business questions and improve user experience with Power BI’s features. For example, rather than replicating a complex Tableau workaround, you might implement a cleaner solution in Power BI using native features (like bookmarks, drilldowns, or simpler navigation between pages). Engage business users and subject matter experts during this redesign to ensure the new reports meet their needs. Enable dataset reusability: One major benefit of the Power BI approach is the ability to create shared datasets and dataflows. As you migrate, look for opportunities to create central semantic models (datasets) that can serve multiple reports. For instance, if several Tableau workbooks are all using similar data about sales, you can create one central Sales dataset in Power BI. Report creators across the organization can then build different Power BI reports on that single dataset without duplicating data or logic. This reduces maintenance and promotes a “build once, reuse often” strategy. Provide training and support: Expect a learning curve for teams moving to Power BI – especially those who are very fluent in Tableau. Plan for user upskilling and training programs. Establish a support community or office hours where new users can ask questions and get help. If possible, identify Power BI champions or recruit a Power BI Center of Excellence (COE) team who can guide others. During the transition, ensure there are subject matter experts (SMEs) available to address questions and validate that the new reports are correct. Manage change and expectations: It’s important to communicate why the organization is moving to Power BI (e.g. benefits like deeper integration, lower TCO, better governance) to get buy-in from end users. Some users may be resistant to change, especially if they’ve invested a lot of time in mastering Tableau. Prepare to handle varying responses – emphasize the personal benefits (like improved performance, new capabilities, or career growth with popular skills) to encourage adoption. Also, involve influential business users early and gather their feedback, so they feel ownership in the new solution. Establish governance from Day 1: Don’t wait until after migration to think about governance. Use this chance to set up Power BI governance aligned to best practices. Decide on important aspects such as workspace naming conventions, who can create or publish content, how you’ll monitor usage and costs, and how to manage data access and security (for example, designing a strategy for RLS/OLS/CLS, and deciding when to use per-user datasets vs. organizational semantic models). Good governance will ensure your shiny new Power BI environment doesn’t sprawl into chaos over time. Allow time for adjustment and iteration: Finally, be patient and iterative. Depending on the scale of your organization and the number of Tableau assets, a full migration can take months or even a year or more. Plan realistic transition periods where both systems might coexist. Continuously refine your approach with each wave of migration. Power BI’s frequent update cadence (monthly releases) means new features may emerge even during your project – stay updated, as new capabilities could simplify your migration (for example, the introduction of field parameters or Copilot might let you modernize certain Tableau features more easily). Reimagine (don’t just replicate) the experience (Step 5): Phase 1: Assessment and Planning 1. Audit Your Tableau Estate Inventory all workbooks, data sources, and calculated fields Identify high-traffic dashboards (prioritize for early migration) Categorize by complexity (Simple/Medium/Complex/Very Complex) 2. Design Your Semantic Architecture Map Tableau data sources to Power BI data sources (DirectQuery, Import, or Direct Lake) Plan star schema for fact/dimension tables Identify shared calculations that should live in semantic models vs. report-specific logic 3. Choose Storage Modes Source Type Recommended Mode Rationale Databricks Delta Lake Direct Lake Real-time analytics, no refresh lag Azure SQL Database DirectQuery or Import Based on data volume and refresh SLAs On-Premises SQL Server Import (via Gateway) Network latency considerations Excel/CSV files Import Small reference data Phase 2: Build the Semantic Layer 1. Create Star Schema Data Models Tableau often relies on flat, denormalized datasets. Power BI performs best with star schemas: Fact tables: Transactional data (sales, orders, events) with foreign keys to dimensions Dimension tables: Descriptive attributes (customers, products, dates) with primary keys Relationships: One-to-many from dimension to fact, leveraging bidirectional filtering sparingly 2. Migrate Calculations to DAX Measures Convert Tableau calculated fields to DAX measures in the semantic model: --Example of DAX: -- Define as measure: Total Revenue = SUMX( 'Sales', 'Sales'[Quantity] * 'Sales'[Unit Price] ) 2.1 Use Copilot to Accelerate DAX Development Leverage Copilot in Power BI Desktop to generate and validate DAX: Describe the calculation in natural language Copilot suggests DAX syntax Review, test, and refine 2.2 Document your Semantic Model Invest in creating an AI-ready foundation for your semantic model. AI systems need to understand unique business contexts in order to prioritize correct information to provide consistent and reliable responses to your end users. Name Tables and Columns Clearly: Avoid ambiguity in your semantic model. Use human-readable, business-friendly names. Avoid abbreviations, acronyms, or technical terms. This improves Copilot’s ability to interpret user intent. Create Meaningful Measures: Define reusable DAX measures for key business metrics (e.g., Revenue, Profit Margin). AI features rely on these to generate insights and summaries. Document Semantic Model objects: Add descriptions and synonyms to your Tables, Columns and measures. This enhances natural language querying and improves Copilot’s contextual understanding. Build an AI Data Schema: prepare your semantic model for AI by utilizing tooling features such as Prep data for AI. Phase 3: Understanding Migration Complexity: Simple to Very Complex Dashboards Not all Tableau dashboards are created equal. The migration strategy should align with dashboard complexity, and the semantic layer approach becomes increasingly valuable as complexity grows. 1. Dashboard Conversion Best Practices Think in "pages" not "sheets": Power BI reports combine multiple visuals per page; group related visuals logically Use slicers for interactivity: Replace Tableau filters with Power BI slicers and filter pane Leverage bookmarks for navigation: Create dynamic report experiences with show/hide containers Simple Complexity Level Category Tableau Feature Power BI Equivalent Microsoft Fabric Enhancements Best Practice Notes Data Model Single custom SQL Power Query for data shaping and ETL. OneLake Shortcuts for unified data access. Use star schema for optimized performance; push logic into the semantic layer rather than visuals. Calculations Basic IF/ELSE, SUM Data Analysis Expressions (DAX) for measures and calculated columns. Copilot for Power BI to assist with DAX creation. Fabric IQ for natural language queries. Centralize calculations in semantic models for consistency and governance. Medium Complexity Level Category Tableau Feature Power BI Equivalent Fabric Enhancements Best Practice Notes Data Model Multiple custom SQL (up to 3) Connect live to databases (Azure Databricks): DirectQuery in Power BI Connect with cloud data sources: Power BI data sources OneLake Shortcuts for unified access without databricks compute cost. Semantic Models can combine multiple sources. Optimize with star schema; Prefer OneLake Shortcuts for performance; avoid heavy transformations in visuals. Calculations Nested IFs, CASE Data Analysis Expressions (DAX) for measures and calculated columns. Copilot for Power BI to assist with DAX creation. Fabric Data Agent for conversational BI. Fabric IQ for natural language queries: Fabric IQ Centralize logic in semantic models; use Copilot for automation and validation; keep calculations reusable. Reporting Tooltip format in Bar and Map visuals Select All/Clear option for Single Select dropdown Standard tooltips offer help tooltips, text, and background formatting. Dynamic tooltip will be able to create the Tooltip page and reuse it in multiple visuals The customization is so much better than the OOB tooltips Create report tooltip pages in Power BI - Power BI | Microsoft Learn Use Clear All Slicers Button. Disable Single Select, Add Clear All Slicers button, Customize the Button and Use the Button Complex Complexity Level Category Tableau Feature Power BI Equivalent Fabric Enhancements Best Practice Notes Data Model Multiple sources Create relationship using more than one column Composite Models in Power BI (DirectQuery + Import) for combining multiple sources, also connect to various cloud services. Dataflows for pre-processing. Power BI allows a relationship between 2 tables based on only one active column. OneLake Shortcuts for unified access without Azure Databricks compute cost; Microsoft Fabric Dataflows Gen2 offers multiple ways to ingest, transform, and load data efficiently. Consolidate sources into semantic models; use Direct Lake for performance; Plan and design data model to comply with star schema supported by Power BI Relationship DAX USERELATIONSHIP DAX for activating relationships in Power BI for a specific calculation Calculations LOD, window functions Data Analysis Expressions (DAX) for measures and calculated columns. Copilot to assist with complex DAX. Fabric IQ Ontology for semantic alignment. Change how visuals interact in a Power BI report. Centralize calculations in semantic layer; use variables in DAX for readability and performance. Fabric Data Agent for a conversational BI. Very Complex Complexity Level Category Tableau Feature Power BI Equivalent Fabric Enhancements Best Practice Notes Data Model Multi-source, Excel, SQL Composite Models in Power BI (DirectQuery + Import) for combining multiple sources, also connect to various cloud services. Dataflows for pre-processing. OneLake Shortcuts for unified access; Connector overview build-in support. Mirroring for real-time sync. Combine multiple sources into well-structured semantic models for consistency and optimized performance. Calculations Predictive logic Data Analysis Expressions (DAX) for measures and calculated columns. Fabric AutoML, ML models, AI Insights, Python/R, Notebook‑based ML (Spark/Scikit‑Learn), Fabric AI Functions, Fabric IQ Ontology Fabric Data Agent for a conversational BI. Centralize logic in semantic models; leverage Copilot for automation and parameter-driven workflows. Prepare for Copilot. 2. Tableau Feature Equivalents Tableau Feature Power BI Equivalent Microsoft Learn Link Calculated Fields DAX Measures DAX Documentation Parameters Field Parameters / Bookmarks Use report readers to change visuals Actions Drillthrough / Bookmarks Drillthrough Tableau Prep Power Query / Dataflows Differences between Dataflow Gen1 and Dataflow Gen2 Tableau Server Power BI Service What is Power BI? Overview of Components and Benefits Phase 4: Governance and Deployment Workspace Planning (Dev / Test / Prod Separation) A proper workspace strategy is essential for governed deployments in Fabric and Power BI. Fabric supports separate Development, Test, and Production stages using Deployment Pipelines, enabling controlled promotions of semantic models, reports, dataflows, notebooks, lakehouses, and other items. You can assign each workspace to a pipeline stage (Dev → Test → Prod) to ensure safe lifecycle management. Sensitivity Labeling (Microsoft Purview Information Protection) Sensitivity labels allow governed classification and protection of data across Fabric items. Sensitivity labels can be applied directly to Fabric items (semantic models, reports, dataflows, etc.) through the item's header flyout or the item settings. Labels from Microsoft Purview Information Protection enforce data access rules and help organizations meet compliance requirements. Endorsement & Certification (Promoted, Certified, Master Data) Endorsement improves discoverability and trust in shared organizational content. Promoted: Item creators mark content as recommended for broader use. Certified: Administrators or authorized reviewers validate content meets organizational quality standards. Master Data: Indicates authoritative single‑source‑of‑truth items such as semantic models or lakehouses. All Fabric items except dashboards can be promoted or certified; data‑containing items can be designated as Master Data. Monitoring & Capacity Planning Determine the appropriate size for fabric capacity when migrating from Tableau to PowerBI. The Fabric SKU Estimator can generate a SKU recommendation (estimate) for your capacity requirements. Ensuring performance and cost efficiency requires ongoing monitoring of your Fabric capacity. Microsoft recommends evaluating workloads using Fabric Capacity Metrics and planning SKU sizes based on real usage. Fabric uses bursting and smoothing to handle spikes while enforcing capacity limits. Monitoring helps identify high compute usage, background refreshes, and interactive workloads to optimize performance. Fabric Data Source Connections (OneLake+ Manage Connections) Microsoft Fabric is designed as an end‑to‑end analytics platform that integrates data from many different source systems into a unified environment powered by OneLake, Data Factory, Real‑Time Analytics, Dataflows , Lakehouses, Warehouses, and Mirrored Databases. The Strategic Advantage: Semantic Layer + Fabric IQ The semantic layer-first approach sets the foundation for the next evolution in enterprise analytics. Fabric IQ (announced at Ignite 2025) is Microsoft's semantic intelligence platform that auto-elevates semantic models into ontologies—structured knowledge graphs that power AI agents, Copilot experiences, and cross-domain data reasoning. What this means for your migration: Semantic models you build today become the foundation for AI-driven analytics tomorrow Data Agents can reason across multiple semantic models, answering questions that span domains Business users transition from "report consumers" to "data explorers" via natural language interfaces Conclusion: Build for the Future, Not Just for Today Migrating from Tableau to Power BI is more than a technology swap—it's an opportunity to re-architect your analytics strategy for the cloud-native, AI-powered era. The semantic layer-first approach requires upfront investment in data modeling, DAX expertise, and Fabric platform adoption. But the payoff is transformative: Consistency: Single source of truth for all business metrics Scalability: Semantic models that serve hundreds of reports and thousands of users Agility: Changes to business logic propagate instantly across the enterprise Future-readiness: Foundation for Fabric IQ, Data Agents, and AI-driven insights Start your migration with the end in mind: not just convert dashboards, but a modern, governed, AI-ready analytics platform that scales with your business. Addressing Key Migration Concerns (1) Why a semantic‑layered model approach is better than recreating Tableau dashboards A semantic‑layered modeling approach is the optimal strategy for migration and is significantly more effective than attempting to recreate Tableau dashboards exactly as they exist. By contrast, Power BI and Fabric encourage a semantic model–first architecture, where all business rules, relationships, calculations, and transformations are centralized in a governed model that serves many dashboards. The approach not only provides consistency and reuse across the enterprise but also ensures that report authors build on a single certified version of the truth. (2) How semantic-layered model approach reduces the constant redesign caused by changing data needs. A semantic‑layered modeling approach directly addresses concern about constant changes and frequent redesigns of dashboards when data evolves. With a semantic layer, changes are absorbed in the model layer—so the logic is updated once and flows automatically into all dependent reports. Combined with Fabric features like OneLake shortcuts, Direct Lake mode, and centralized governance, the semantic layer drastically reduces breakage, minimizes rework, and ensures scalability as data continues to grow and shift. Additional Resources Direct Lake in Microsoft Fabric Create Fabric Data Agents OneLake Shortcuts Write DAX queries with Copilot - DAX Prepare Your Data for AI - Power BI | Microsoft LearnLearn about Pulumi on Azure Thurs Aug 12 on LearnTV
AzureFunBytes is a weekly opportunity to learn more about the fundamentals and foundations that make up Azure. It's a chance for me to understand more about what people across the Azure organization do and how they do it. Every week we get together at 11 AM Pacific on Microsoft LearnTV and learn more about Azure. When: August 12, 2021 11 AM Pacific / 2 PM Eastern Where: Microsoft LearnTV Agnostic tools for building your cloud infrastructure can be incredibly helpful to provide a strategy that looks beyond one single cloud. There are many alternatives in the market to using native tooling for your cloud provider or handle the work manually. Each of these "Infrastructure as Code" tools brings you the tools to deploy anywhere, anytime with the reliability and consistency you expect. You can use programming languages you might already know such as Node.js, Python, Go, and .NET and use standard constructs like loops and conditionals. Pulumi allows you to build, deploy, and manage modern cloud applications and infrastructure using familiar languages, tools, and engineering practices. With a tool like Pulumi you can build your architecture required for your IT operations to nearly 50 different cloud providers. If you also need on-prem or hybrid environments configured, Pulumi has you covered. Installing Pulumi just takes a few commands on your local environment. Pulumi uses different providers to support the various cloud services you may need. If Azure is your cloud of choice you can provision any of the services via Azure Resource Manager (ARM). The Azure provider must be configured with credentials to deploy and update resources in Azure. This can be done by either using the Azure CLI or by creating an Azure Active Directory Service Principal. To help me understand how to start working with Pulumi, I've reached out to one of my favorite people from the world of DevOps, Principal Developer Advocate at Pulumi, Matty Stratton. Matt Stratton is a Staff Developer Advocate at Pulumi, founder and co-host of the popular Arrested DevOps podcast, and the global chair of the DevOpsDays set of conferences. Matt has over 20 years of experience in IT operations and is a sought-after speaker internationally, presenting at Agile, DevOps, and cloud engineering focused events worldwide. Demonstrating his keen insight into the changing landscape of technology, he recently changed his license plate from DEVOPS to KUBECTL . He lives in Chicago and has three awesome kids, whom he loves just a little bit more than he loves Diet Coke. Matt is the keeper of the Thought Leaderboard for the DevOps Party Games online game show and you can find him on Twitter at @mattstratton. We will work together to learn how to get started, how to use the programming language you may already know, and find out if Pulumi for your Azure deployments is right for you. Here's our planned agenda for our show on LearnTV: Why bother writing automation code anyway I'm a developer. why do I can about infrastructure automation? I'm an ops person. Why should I write code? Why Pulumi when there are other tools and stuff already? We'll answer these questions and more this Thursday, August 12, 2021 at 11 AM PT / 2PM ET. Learn about Azure fundamentals with me! Live stream is normally found on Twitch, YouTube, and LearnTV at 11 AM PT / 2 PM ET Thursday. You can also find the recordings here as well: AzureFunBytes on Twitch AzureFunBytes on YouTube Azure DevOps YouTube Channel Follow AzureFunBytes on Twitter Useful Docs: Get $200 in free Azure Credit Microsoft Learn: Introduction to Azure fundamentals Cloud Engineering Summit Getting Started with Pulumi Upcoming workshops, etc What is Infrastructure as Code? What is Azure Resource Manager? Azure Command-Line Interface (CLI) - Overview | Microsoft Docs Application and service principal objects in Azure Active DirectoryBuilding a Reliable Real Time Data Pipeline with Microsoft Fabric
The Two Pillars That Determine Success Data Quality Cannot Be an Afterthought The most common mistake we see is treating data quality as something to address after the pipeline is running. This approach creates technical debt that compounds over time and erodes trust in your data. Your CDC pipeline will ingest millions of events daily. Without proper validation at each layer, small issues become major problems. A single source system changing a column from integer to string can silently corrupt downstream analytics for days before anyone notices. What you need to implement from day one: Validation at the Bronze layer should focus on structural integrity. Every record landing in your raw layer needs verification that required fields exist, timestamps are valid, and CDC operation types are recognized. The Silver layer is where business validation happens. Here you check referential integrity, apply domain specific rules, and flag anomalies. A customer ID that does not exist in your customer master table needs to be caught here. Schema drift detection deserves special attention. Source systems change without warning. Your pipeline needs to detect these changes before they break downstream processes. The quality score approach works well in practice. Rather than binary pass or fail checks, calculate a quality score for each batch. A score above 95 percent proceeds normally. Between 90 and 95 percent sends a warning. Below 90 percent halts processing. Replication Lag Requires Active Management The second pillar is understanding and managing replication lag. In a real time pipeline, the value of data degrades rapidly with age. A five minute delay might be acceptable for daily reporting but catastrophic for fraud detection or inventory management. Lag accumulates at multiple points in your pipeline. There is capture lag between when a change occurs in the source database and when the CDC mechanism detects it. Processing lag occurs within Eventstream as events are transformed and routed. Ingestion lag happens between Eventstream and your destination tables. Each component adds latency, and under load, these delays compound. Building effective lag management: Monitor each stage independently. Knowing your total lag is useful, but knowing where lag accumulates is actionable. Establish baselines before setting alerts. Collect at least two weeks of baseline metrics before configuring alert thresholds. Implement automatic recovery procedures. When lag exceeds acceptable thresholds, your system should respond without waiting for human intervention. Operational Foundations You Cannot Skip Capacity Planning Prevents Expensive Surprises Microsoft Fabric uses a capacity unit model where all workloads draw from a shared pool. Underprovisioning leads to throttling and failed jobs. Overprovisioning wastes budget. Start with realistic estimates based on your data volumes. The F4 SKU handles most development and small production workloads comfortably. Medium deployments with 10 to 25 sources typically need F8. Large enterprise deployments should start at F16 and scale based on observed utilization. Watch for sustained utilization above 70 percent as a signal to consider scaling up. Network Security Shapes Your Architecture For production deployments handling sensitive data, network isolation is not optional. Private endpoints keep traffic on the Microsoft backbone network, eliminating exposure to the public internet. Plan your network architecture before building pipelines. Retrofitting private connectivity into an existing deployment is significantly more complex than designing it from the start. Logging Enables Troubleshooting When something goes wrong at 2 AM, your ability to diagnose the problem depends entirely on what information you captured beforehand. Centralized logging using Eventhouse gives you a queryable record of everything that happened across your pipeline. Log more than you think you need initially. Storage is inexpensive compared to the cost of troubleshooting without adequate information. Key Decisions You Need to Make Before proceeding with implementation, your team should align on several important decisions. Data retention requirements affect storage costs and query performance. How long do you need to keep Bronze layer data versus aggregated Gold layer data? Recovery time objectives determine how you architect for resilience. If the pipeline can be down for four hours without business impact, your approach differs from a scenario where even 15 minutes causes significant problems. Who owns data quality shapes how you design validation and alerting. If source system teams are responsible, your pipeline detects and reports. If your team owns quality, you implement correction and enrichment. Moving Forward The patterns and practices in this guide reflect lessons learned from real implementations. Every organization has unique requirements, but the fundamentals of data quality, lag management, capacity planning, and operational readiness apply universally. Start with the foundations. A pipeline that handles one source reliably is more valuable than one that theoretically handles fifty but fails unpredictably. Build observability from day one. Automate responses to common problems. Document what you learn. Your data is a strategic asset. The pipeline that delivers it reliably deserves the same careful engineering you would apply to any critical business system.
