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How to Fix Azure Event Grid Entra Authentication issue for ACS and Dynamics 365 integrated Webhooks
Introduction: Azure Event Grid is a powerful event routing service that enables event-driven architectures in Azure. When delivering events to webhook endpoints, security becomes paramount. Microsoft provides a secure webhook delivery mechanism using Microsoft Entra ID (formerly Azure Active Directory) authentication through the AzureEventGridSecureWebhookSubscriber role. Problem Statement: When integrating Azure Communication Services with Dynamics 365 Contact Center using Microsoft Entra ID-authenticated Event Grid webhooks, the Event Grid subscription deployment fails with an error: "HTTP POST request failed with unknown error code" with empty HTTP status and code. For example: Important Note: Before moving forward, please verify that you have the Owner role assigned on app to create event subscription. Refer to the Microsoft guidelines below to validate the required prerequisites before proceeding: Set up incoming calls, call recording, and SMS services | Microsoft Learn Why This Happens: This happens because AzureEventGridSecureWebhookSubscriber role is NOT properly configured on Microsoft EventGrid SP (Service Principal) and event subscription entra ID or application who is trying to create event grid subscription. What is AzureEventGridSecureWebhookSubscriber Role: The AzureEventGridSecureWebhookSubscriber is an Azure Entra application role that: Enables your application to verify the identity of event senders Allows specific users/applications to create event subscriptions Authorizes Event Grid to deliver events to your webhook How It Works: Role Creation: You create this app role in your destination webhook application's Azure Entra registration Role Assignment: You assign this role to: Microsoft Event Grid service principal (so it can deliver events) Either Entra ID / Entra User or Event subscription creator applications (so they can create event grid subscriptions) Token Validation: When Event Grid delivers events, it includes an Azure Entra token with this role claim Authorization Check: Your webhook validates the token and checks for the role Key Participants: Webhook Application (Your App) Purpose: Receives and processes events App Registration: Created in Azure Entra Contains: The AzureEventGridSecureWebhookSubscriber app role Validates: Incoming tokens from Event Grid Microsoft Event Grid Service Principal Purpose: Delivers events to webhooks App ID: Different per Azure cloud (Public, Government, etc.) Public Azure: 4962773b-9cdb-44cf-a8bf-237846a00ab7 Needs: AzureEventGridSecureWebhookSubscriber role assigned Event Subscription Creator Entra or Application Purpose: Creates event subscriptions Could be: You, Your deployment pipeline, admin tool, or another application Needs: AzureEventGridSecureWebhookSubscriber role assigned Although the full PowerShell script is documented in the below Event Grid documentation, it may be complex to interpret and troubleshoot. Azure PowerShell - Secure WebHook delivery with Microsoft Entra Application in Azure Event Grid - Azure Event Grid | Microsoft Learn To improve accessibility, the following section provides a simplified step-by-step tested solution along with verification steps suitable for all users including non-technical: Steps: STEP 1: Verify/Create Microsoft.EventGrid Service Principal Azure Portal → Microsoft Entra ID → Enterprise applications Change filter to Application type: Microsoft Applications Search for: Microsoft.EventGrid Ideally, your Azure subscription should include this application ID, which is common across all Azure subscriptions: 4962773b-9cdb-44cf-a8bf-237846a00ab7. If this application ID is not present, please contact your Azure Cloud Administrator. STEP 2: Create the App Role "AzureEventGridSecureWebhookSubscriber" Using Azure Portal: Navigate to your Webhook App Registration: Azure Portal → Microsoft Entra ID → App registrations Click All applications Find your app by searching OR use the Object ID you have Click on your app Create the App Role: Display name: AzureEventGridSecureWebhookSubscriber Allowed member types: Both (Users/Groups + Applications) Value: AzureEventGridSecureWebhookSubscriber Description: Azure Event Grid Role Do you want to enable this app role?: Yes In left menu, click App roles Click + Create app role Fill in the form: Click Apply STEP 3: Assign YOUR USER to the Role Using Azure Portal: Switch to Enterprise Application view: Azure Portal → Microsoft Entra ID → Enterprise applications Search for your webhook app (by name) Click on it Assign yourself: In left menu, click Users and groups Click + Add user/group Under Users, click None Selected Search for your user account (use your email) Select yourself Click Select Under Select a role, click None Selected Select AzureEventGridSecureWebhookSubscriber Click Select Click Assign STEP 4: Assign Microsoft.EventGrid Service Principal to the Role This step MUST be done via PowerShell or Azure CLI (Portal doesn't support this directly as we have seen) so PowerShell is recommended You will need to execute this step with the help of your Entra admin. # Connect to Microsoft Graph Connect-MgGraph -Scopes "AppRoleAssignment.ReadWrite.All" # Replace this with your webhook app's Application (client) ID $webhookAppId = "YOUR-WEBHOOK-APP-ID-HERE" #starting with c5 # Get your webhook app's service principal $webhookSP = Get-MgServicePrincipal -Filter "appId eq '$webhookAppId'" Write-Host " Found webhook app: $($webhookSP.DisplayName)" # Get Event Grid service principal $eventGridSP = Get-MgServicePrincipal -Filter "appId eq '4962773b-9cdb-44cf-a8bf-237846a00ab7'" Write-Host " Found Event Grid service principal" # Get the app role $appRole = $webhookSP.AppRoles | Where-Object {$_.Value -eq "AzureEventGridSecureWebhookSubscriber"} Write-Host " Found app role: $($appRole.DisplayName)" # Create the assignment New-MgServicePrincipalAppRoleAssignment ` -ServicePrincipalId $eventGridSP.Id ` -PrincipalId $eventGridSP.Id ` -ResourceId $webhookSP.Id ` -AppRoleId $appRole.Id Write-Host "Successfully assigned Event Grid to your webhook app!" Verification Steps: Verify the App Role was created: Your App Registration → App roles You should see: AzureEventGridSecureWebhookSubscriber Verify your user assignment: Enterprise application (your webhook app) → Users and groups You should see your user with role AzureEventGridSecureWebhookSubscriber Verify Event Grid assignment: Same location → Users and groups You should see Microsoft.EventGrid with role AzureEventGridSecureWebhookSubscriber Sample Flow: Analogy For Simplification: Lets think it similar to the construction site bulding where you are the owner of the building. Building = Azure Entra app (webhook app) Building (Azure Entra App Registration for Webhook) ├─ Building Name: "MyWebhook-App" ├─ Building Address: Application ID ├─ Building Owner: You ├─ Security System: App Roles (the security badges you create) └─ Security Team: Azure Entra and your actual webhook auth code (which validates tokens) like doorman Step 1: Creat the badge (App role) You (the building owner) create a special badge: - Badge name: "AzureEventGridSecureWebhookSubscriber" - Badge color: Let's say it's GOLD - Who can have it: Companies (Applications) and People (Users) This badge is stored in your building's system (Webhook App Registration) Step 2: Give badge to the Event Grid Service: Event Grid: "Hey, I need to deliver messages to your building" You: "Okay, here's a GOLD badge for your SP" Event Grid: *wears the badge* Now Event Grid can: - Show the badge to Azure Entra - Get tokens that say "I have the GOLD badge" - Deliver messages to your webhook Step 3: Give badge to yourself (or your deployment tool) You also need a GOLD badge because: - You want to create event grid event subscriptions - Entra checks: "Does this person have a GOLD badge?" - If yes: You can create subscriptions - If no: "Access denied" Your deployment pipeline also gets a GOLD badge: - So it can automatically set up event subscriptions during CI/CD deployments Disclaimer: The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you found it helpful and informative for this specific integration use case 😀How to deploy n8n on Azure App Service and leverage the benefits provided by Azure.
Lately, n8n has been gaining serious traction in the automation world—and it’s easy to see why. With its open-source core, visual workflow builder, and endless integration capabilities, it has become a favorite for developers and tech teams looking to automate processes without being locked into a single vendor. Given all the buzz, I thought it would be the perfect time to share a practical way to run n8n on Microsoft Azure using App Service. Why? Because Azure offers a solid, scalable, and secure platform that makes deployment easy, while still giving you full control over your container and configurations. Whether you're building a quick demo or setting up a production-ready instance, Azure App Service brings a lot of advantages to the table—like simplified scaling, integrated monitoring, built-in security features, and seamless CI/CD support. In this post, I’ll walk you through how to get your own n8n instance up and running on Azure—from creating the resource group to setting up environment variables and deploying the container. If you're into low-code automation and cloud-native solutions, this is a great way to combine both worlds. The first step is to create our Resource Group (RG); in my case, I will name it "n8n-rg". Now we proceed to create the App Service. At this point, it's important to select the appropriate configuration depending on your needs—for example, whether or not you want to include a database. If you choose to include one, Azure will handle the connections for you, and you can select from various types. In my case, I will proceed without a database. Proceed to configure the instance details. First, select the instance name, the 'Publish' option, and the 'Operating System'. In this case, it is important to choose 'Publish: Container', set the operating system to Linux, and most importantly select the region closest to you or your clients. Service Plan configuration. Here, you should select the plan based on your specific needs. Keep in mind that we are using a PaaS offering, which means that underlying compute resources like CPU and RAM are still being utilized. Depending on the expected workload, you can choose the most appropriate plan. Secondly—and very importantly—consider the features offered by each tier, such as redundancy, backup, autoscaling, custom domains, etc. In my case, I will use the Basic B1 plan. In the Database section, we do not select any option. Remember that this will depend on your specific requirements. In the Container section, under 'Image Source', select 'Other container registries'. For production environments, I recommend using Azure Container Registry (ACR) and pulling the n8n image from there. Now we will configure the Docker Hub options. This step is related to the previous one, as the available options vary depending on the image source. In our case, we will use the public n8n image from Docker Hub, so we select 'Public' and proceed to fill in the required fields: the first being the server, and the second the image name. This step is very important—use the exact same values to avoid issues. In the Networking section, we will select the values as shown in the image. This configuration will depend on your specific use case—particularly whether to enable Virtual Network (VNet) integration or not. VNet integration is typically used when the App Service needs to securely communicate with private resources (such as databases, APIs, or services) that reside within an Azure Virtual Network. Since this is a demo environment, we will leave the default settings without enabling VNet integration. In the 'Monitoring and Security' section, it is essential to enable these features to ensure traceability, observability, and additional security layers. This is considered a minimum requirement in production environments. At the very least, make sure to enable Application Insights by selecting 'Yes'. Finally, click on 'Create' and wait for the deployment process to complete. Now we will 'stop' our Web App, as we need to make some preliminary modifications. To do this, go to the main overview page of the Web App and click on 'Stop'. In the same Web App overview page, navigate through the left-hand panel to the 'Settings' section. Once there, click on it and select 'Environment Variables'. Environment variables are key-value pairs used to configure the behavior of your application without changing the source code. In the case of n8n, they are essential for defining authentication, webhook behavior, port configuration, timezone settings, and more. Environment variables within Azure specifically in Web Apps function the same way as they do outside of Azure. They allow you to configure your application's behavior without modifying the source code. In this case, we will add the following variables required for n8n to operate properly. Note: The variable APP_SERVICE_STORAGE should only be modified by setting it to true. Once the environment variables have been added, proceed to save them by clicking 'Apply' and confirming the changes. A confirmation dialog will appear to finalize the operation. Restart the Web App. This second startup may take longer than usual, typically around 5 to 7 minutes, as the environment initializes with the new configuration. Now, as we can see, the application has loaded successfully, and we can start using our own n8n server hosted on Azure. As you can observe, it references the host configured in the App Service. I hope you found this guide helpful and that it serves as a useful resource for deploying n8n on Azure App Service. If you have any questions or need further clarification, feel free to reach out—I'd be happy to help.Building Multi-Agent Orchestration Using Microsoft Semantic Kernel: A Complete Step-by-Step Guide
What You Will Build By the end of this guide, you will have a working multi-agent system where 4 specialist AI agents collaborate to diagnose production issues: ClientAnalyst — Analyzes browser, JavaScript, CORS, uploads, and UI symptoms NetworkAnalyst — Analyzes DNS, TCP/IP, TLS, load balancers, and firewalls ServerAnalyst — Analyzes backend logs, database, deployments, and resource limits Coordinator — Synthesizes all findings into a root cause report with a prioritized action plan These agents don't just run in sequence — they debate, cross-examine, and challenge each other's findings through a shared conversation, producing a diagnosis that's better than any single agent could achieve alone. Table of Contents Why Multi-Agent? The Problem with Single Agents Architecture Overview Understanding the Key SK Components The Actor Model — How InProcessRuntime Works Setting Up Your Development Environment Step-by-Step: Building the Multi-Agent Analyzer The Agent Interaction Flow — Round by Round Bugs I Found & Fixed — Lessons Learned Running with Different AI Providers What to Build Next 1. Why Multi-Agent? The Problem with Single Agents A single AI agent analyzing a production issue is like having one doctor diagnose everything — they'll catch issues in their specialty but miss cross-domain connections. Consider this problem: "Users report 504 Gateway Timeout errors when uploading files larger than 10MB. Started after Friday's deployment. Worse during peak hours." A single agent might say "it's a server timeout" and stop. But the real root cause often spans multiple layers: The client is sending chunked uploads with an incorrect Content-Length header (client-side bug) The load balancer has a 30-second timeout that's too short for large uploads (network config) The server recently deployed a new request body parser that's 3x slower (server-side regression) The combination only fails during peak hours because connection pool saturation amplifies the latency No single perspective catches this. You need specialists who analyze independently, then debate to find the cross-layer causal chain. That's what multi-agent orchestration gives you. The 5 Orchestration Patterns in SK Semantic Kernel provides 5 built-in patterns for agent collaboration: SEQUENTIAL: A → B → C → Done (pipeline — each builds on previous) CONCURRENT: ↗ A ↘ Task → B → Aggregate ↘ C ↗ (parallel — results merged) GROUP CHAT: A ↔ B ↔ C ↔ D ← We use this one (rounds, shared history, debate) HANDOFF: A → (stuck?) → B → (complex?) → Human (escalation with human-in-the-loop) MAGENTIC: LLM picks who speaks next dynamically (AI-driven speaker selection) We use GroupChatOrchestration with RoundRobinGroupChatManager because our problem requires agents to see each other's work, challenge assumptions, and build on each other's analysis across two rounds. 2. Architecture Overview Here's the complete architecture of what we're building: 3. Understanding the Key SK Components Before we write code, let's understand the 5 components we'll use and the design pattern each implements: ChatCompletionAgent — Strategy Pattern The agent definition. Each agent is a combination of: name — unique identifier (used in round-robin ordering) instructions — the persona and rules (this is the prompt engineering) service — which AI provider to call (Strategy Pattern — swap providers without changing agent logic) description — what other agents/tools understand about this agent agent = ChatCompletionAgent( name="ClientAnalyst", instructions="You are ONLY ClientAnalyst...", service=gemini_service, # ← Strategy: swap to OpenAI with zero changes description="Analyzes client-side issues", ) GroupChatOrchestration — Mediator Pattern The orchestration defines HOW agents interact. It's the Mediator — agents don't talk to each other directly. Instead, the orchestration manages a shared ChatHistory and routes messages through the Manager. RoundRobinGroupChatManager — Strategy Pattern The Manager decides WHO speaks next. RoundRobinGroupChatManager cycles through agents in a fixed order. SK also provides AutomaticGroupChatManager where the LLM decides who speaks next. max_rounds is the total number of messages per agent or cycle. With 4 agents and max_rounds=8, each agent speaks exactly twice. InProcessRuntime — Actor Model Abstraction The execution engine. Every agent becomes an "actor" with its own kind of mailbox (message queue). The runtime delivers messages between actors. Key properties: No shared state — agents communicate only through messages Sequential processing — each agent processes one message at a time Location transparency — same code works in-process today, distributed tomorrow agent_response_callback — Observer Pattern A function that fires after EVERY agent response. We use it to display each agent's output in real-time with emoji labels and round numbers. 4. The Actor Model — How InProcessRuntime Works The Actor Model is a concurrency pattern where each entity is an isolated "actor" with a private mailbox. Here's what happens inside InProcessRuntime when we run our demo: runtime.start() │ ├── Creates internal message loop (asyncio event loop) │ orchestration.invoke(task="504 timeout...", runtime=runtime) │ ├── Creates Actor[Orchestrator] → manages overall flow ├── Creates Actor[Manager] → RoundRobinGroupChatManager ├── Creates Actor[ClientAnalyst] → mailbox created, waiting ├── Creates Actor[NetworkAnalyst] → mailbox created, waiting ├── Creates Actor[ServerAnalyst] → mailbox created, waiting └── Creates Actor[Coordinator] → mailbox created, waiting Manager receives "start" message │ ├── Checks turn order: [Client, Network, Server, Coordinator] ├── Sends task to ClientAnalyst mailbox │ → ClientAnalyst processes: calls LLM → response │ → Response added to shared ChatHistory │ → callback fires (displayed in Notebook UI) │ → Sends "done" back to Manager │ ├── Manager updates: turn_index=1 ├── Sends to NetworkAnalyst mailbox │ → Same flow... │ ├── ... (ServerAnalyst, Coordinator for Round 1) │ ├── Manager checks: messages=4, max_rounds=8 → continue │ ├── Round 2: same cycle with cross-examination │ └── After message 8: Manager sends "complete" → OrchestrationResult resolves → result.get() returns final answer runtime.stop_when_idle() → All mailboxes empty → clean shutdown The Actor Model guarantees: No race conditions (each actor processes one message at a time) No deadlocks (no shared locks to contend for) No shared mutable state (agents communicate only via messages) 5. Setting Up Your Development Environment Prerequisites Python 3.11 or 3.12 (3.13+ may have compatibility issues with some SK connectors) Visual Studio Code with the Python and Jupyter extensions An API key from one of: Google AI Studio (free), OpenAI Step 1: Install Python Download from python.org. During installation, check "Add Python to PATH". Verify: python --version # Python 3.12.x Step 2: Install VS Code Extensions Open VS Code, go to Extensions (Ctrl+Shift+X), and install: Python (by Microsoft) — Python language support Jupyter (by Microsoft) — Notebook support Pylance (by Microsoft) — IntelliSense and type checking Step 3: Create Project Folder mkdir sk-multiagent-demo cd sk-multiagent-demo Open in VS Code: code . Step 4: Create Virtual Environment Open the VS Code terminal (Ctrl+`) and run: # Create virtual environment python -m venv sk-env # Activate it # Windows: sk-env\Scripts\activate # macOS/Linux: source sk-env/bin/activate You should see (sk-env) in your terminal prompt. Step 5: Install Semantic Kernel For Google Gemini (free tier — recommended for getting started): pip install semantic-kernel[google] python-dotenv ipykernel For OpenAI (paid API key): pip install semantic-kernel openai python-dotenv ipykernel For Azure AI Foundry (enterprise, Entra ID auth): pip install semantic-kernel azure-identity python-dotenv ipykernel Step 6: Register the Jupyter Kernel python -m ipykernel install --user --name=sk-env --display-name="Semantic Kernel (Python 3.12)" You can also select if this is already available from your environment from VSCode as below: Step 7: Get Your API Key Option A — Google Gemini (FREE, recommended for demo): Go to https://aistudio.google.com/apikey Click "Create API Key" Copy the key Free tier limits: 15 requests/minute, 1 million tokens/minute — more than enough for this demo. Option B — OpenAI: Go to https://platform.openai.com/api-keys Create a new key Copy the key Option C — Azure AI Foundry: Deploy a model in Azure AI Foundry portal Note the endpoint URL and deployment name If key-based auth is disabled, you'll need Entra ID with permissions Step 8: Create the .env File In your project root, create a file named .env: For Gemini: GOOGLE_AI_API_KEY=AIzaSy...your-key-here GOOGLE_AI_GEMINI_MODEL_ID=gemini-2.5-flash For OpenAI: OPENAI_API_KEY=sk-...your-key-here OPENAI_CHAT_MODEL_ID=gpt-4o For Azure AI Foundry: AZURE_OPENAI_ENDPOINT=https://your-resource.cognitiveservices.azure.com AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=gpt-4o AZURE_OPENAI_API_KEY=your-key Step 9: Create the Notebook In VS Code: Click File > New File Save as multi_agent_analyzer.ipynb In the top-right of the notebook, click Select Kernel Choose Semantic Kernel (Python 3.12) (or your sk-env) Your environment is ready. Let's build. 6. Step-by-Step: Building the Multi-Agent Analyzer Cell 1: Verify Setup import semantic_kernel print(f"Semantic Kernel version: {semantic_kernel.__version__}") from semantic_kernel.agents import ( ChatCompletionAgent, GroupChatOrchestration, RoundRobinGroupChatManager, ) from semantic_kernel.agents.runtime import InProcessRuntime from semantic_kernel.contents import ChatMessageContent print("All imports successful") Cell 2: Load API Key and Create Service For Gemini: import os from dotenv import load_dotenv load_dotenv() from semantic_kernel.connectors.ai.google.google_ai import ( GoogleAIChatCompletion, GoogleAIChatPromptExecutionSettings, ) from semantic_kernel.contents import ChatHistory GEMINI_API_KEY = os.getenv("GOOGLE_AI_API_KEY") GEMINI_MODEL = os.getenv("GOOGLE_AI_GEMINI_MODEL_ID", "gemini-2.5-flash") service = GoogleAIChatCompletion( gemini_model_id=GEMINI_MODEL, api_key=GEMINI_API_KEY, ) print(f"Service created: Gemini {GEMINI_MODEL}") # Smoke test settings = GoogleAIChatPromptExecutionSettings() test_history = ChatHistory(system_message="You are a helpful assistant.") test_history.add_user_message("Say 'Connected!' and nothing else.") response = await service.get_chat_message_content( chat_history=test_history, settings=settings ) print(f"Model says: {response.content}") For OpenAI: import os from dotenv import load_dotenv load_dotenv() from semantic_kernel.connectors.ai.open_ai import ( OpenAIChatCompletion, OpenAIChatPromptExecutionSettings, ) from semantic_kernel.contents import ChatHistory service = OpenAIChatCompletion( ai_model_id=os.getenv("OPENAI_CHAT_MODEL_ID", "gpt-4o"), ) print(f"Service created: OpenAI {os.getenv('OPENAI_CHAT_MODEL_ID', 'gpt-4o')}") # Smoke test settings = OpenAIChatPromptExecutionSettings() test_history = ChatHistory(system_message="You are a helpful assistant.") test_history.add_user_message("Say 'Connected!' and nothing else.") response = await service.get_chat_message_content( chat_history=test_history, settings=settings ) print(f"Model says: {response.content}") Cell 3: Define All 4 Agents This is the most important cell — the prompt engineering that makes the demo work: from semantic_kernel.agents import ChatCompletionAgent # ═══════════════════════════════════════════════════ # AGENT 1: Client-Side Analyst # ═══════════════════════════════════════════════════ client_agent = ChatCompletionAgent( name="ClientAnalyst", description="Analyzes problems from the client-side: browser, JS, CORS, caching, UI symptoms", instructions="""You are ONLY **ClientAnalyst**. You must NEVER speak as NetworkAnalyst, ServerAnalyst, or Coordinator. Every word you write is from ClientAnalyst's perspective only. You are a senior front-end and client-side diagnostics expert. When given a problem statement, analyze it EXCLUSIVELY from the client side: 1. **Browser & Rendering**: DOM issues, JavaScript errors, CSS rendering, browser compatibility, memory leaks, console errors. 2. **Client-Side Caching**: Stale cache, service worker issues, local storage corruption. 3. **Network from Client View**: CORS errors, preflight failures, request timeouts, client-side retry storms, fetch/XHR configuration. 4. **Upload Handling**: File API usage, chunk upload implementation, progress tracking, FormData construction, content-type headers. 5. **UI/UX Symptoms**: What the user sees, error messages displayed, loading states. ROUND 1: Provide your independent analysis. Do NOT reference other agents. List your top 3 most likely causes with evidence. Every response MUST be at least 200 words. ROUND 2: You MUST: - Reference NetworkAnalyst and ServerAnalyst BY NAME - State specifically where you AGREE or DISAGREE with their findings - Answer the Coordinator's questions from your perspective - Add NEW cross-layer insights you see from the client perspective - Do NOT just say 'I agree' — provide substantive technical reasoning Be specific, evidence-based, and prioritize findings by likelihood.""", service=service, ) # ═══════════════════════════════════════════════════ # AGENT 2: Network Analyst # ═══════════════════════════════════════════════════ network_agent = ChatCompletionAgent( name="NetworkAnalyst", description="Analyzes problems from the network side: DNS, TCP, TLS, firewalls, load balancers, latency", instructions="""You are ONLY **NetworkAnalyst**. You must NEVER speak as ClientAnalyst, ServerAnalyst, or Coordinator. Every word you write is from NetworkAnalyst's perspective only. You are a senior network infrastructure diagnostics expert. When given a problem statement, analyze it EXCLUSIVELY from the network layer: 1. **DNS & Resolution**: DNS TTL, propagation delays, record misconfigurations. 2. **TCP/IP & Connections**: Connection pooling, keep-alive, TCP window scaling, connection resets, SYN floods. 3. **TLS/SSL**: Certificate issues, handshake failures, protocol version mismatches. 4. **Load Balancers & Proxies**: Sticky sessions, health checks, timeout configs, request body size limits, proxy buffering. 5. **Firewall & WAF**: Rule blocks, rate limiting, request inspection delays, geo-blocking, DDoS protection interference. ROUND 1: Provide your independent analysis. Do NOT reference other agents. List your top 3 most likely causes with evidence. Every response MUST be at least 200 words. ROUND 2: You MUST: - Reference ClientAnalyst and ServerAnalyst BY NAME - State specifically where you AGREE or DISAGREE with their findings - Answer the Coordinator's questions from your perspective - Add NEW cross-layer insights you see from the network perspective - Do NOT just say 'I am ready to proceed' — provide substantive technical analysis Be specific, evidence-based, and prioritize findings by likelihood.""", service=service, ) # ═══════════════════════════════════════════════════ # AGENT 3: Server-Side Analyst # ═══════════════════════════════════════════════════ server_agent = ChatCompletionAgent( name="ServerAnalyst", description="Analyzes problems from the server side: backend app, database, logs, resources, deployments", instructions="""You are ONLY **ServerAnalyst**. You must NEVER speak as ClientAnalyst, NetworkAnalyst, or Coordinator. Every word you write is from ServerAnalyst's perspective only. You are a senior backend and infrastructure diagnostics expert. When given a problem statement, analyze it EXCLUSIVELY from the server side: 1. **Application Server**: Error logs, exception traces, thread pool exhaustion, memory leaks, CPU spikes, garbage collection pauses. 2. **Database**: Slow queries, connection pool saturation, lock contention, deadlocks, replication lag, query plan changes. 3. **Deployment & Config**: Recent deployments, configuration changes, feature flags, environment variable mismatches, rollback candidates. 4. **Resource Limits**: File upload size limits, request body limits, disk space, temporary file cleanup, storage quotas. 5. **External Dependencies**: Upstream API timeouts, third-party service degradation, queue backlogs, cache (Redis/Memcached) issues. ROUND 1: Provide your independent analysis. Do NOT reference other agents. List your top 3 most likely causes with evidence. Every response MUST be at least 200 words. ROUND 2: You MUST: - Reference ClientAnalyst and NetworkAnalyst BY NAME - State specifically where you AGREE or DISAGREE with their findings - Answer the Coordinator's questions from your perspective - Add NEW cross-layer insights you see from the server perspective - Do NOT just say 'I agree' — provide substantive technical reasoning Be specific, evidence-based, and prioritize findings by likelihood.""", service=service, ) # ═══════════════════════════════════════════════════ # AGENT 4: Coordinator # ═══════════════════════════════════════════════════ coordinator_agent = ChatCompletionAgent( name="Coordinator", description="Synthesizes all specialist analyses into a final root cause report with prioritized action plan", instructions="""You are ONLY **Coordinator**. You must NEVER speak as ClientAnalyst, NetworkAnalyst, or ServerAnalyst. You synthesize — you do NOT do domain-specific analysis. You are the lead engineer who synthesizes the team's findings. ═══ ROUND 1 BEHAVIOR (your first turn, message 4) ═══ Keep this SHORT — maximum 300 words. - Note 2-3 KEY PATTERNS across the three analyses - Identify where specialists AGREE (high-confidence) - Identify where they CONTRADICT (needs resolution) - Ask 2-3 SPECIFIC QUESTIONS for Round 2 Round 1 MUST NOT: assign tasks, create action plans, write reports, or tell agents what to take lead on. Observation + questions ONLY. ═══ ROUND 2 BEHAVIOR (your final turn, message 8) ═══ Keep this FOCUSED — maximum 800 words. Produce a structured report: 1. **Root Cause** (1 paragraph): The #1 most likely cause with causal chain across layers. Reference specific findings from each specialist. 2. **Confidence** (short list): - HIGH: Areas where all 3 agreed - MEDIUM: Areas where 2 of 3 agreed - LOW: Disagreements needing investigation 3. **Action Plan** (numbered, max 6 items): For each: - What to do (specific) - Owner (Client/Network/Server team) - Time estimate 4. **Quick Wins vs Long-term** (2 short lists) Do NOT repeat what specialists already said verbatim. Synthesize, don't echo.""", service=service, ) # ═══════════════════════════════════════════════════ # All 4 agents — order = RoundRobin order # ═══════════════════════════════════════════════════ agents = [client_agent, network_agent, server_agent, coordinator_agent] print(f"{len(agents)} agents created:") for i, a in enumerate(agents, 1): print(f" {i}. {a.name}: {a.description[:60]}...") print(f"\nRoundRobin order: {' → '.join(a.name for a in agents)}") Cell 4: Run the Analysis from semantic_kernel.agents import GroupChatOrchestration, RoundRobinGroupChatManager from semantic_kernel.agents.runtime import InProcessRuntime from semantic_kernel.contents import ChatMessageContent from IPython.display import display, Markdown # ╔══════════════════════════════════════════════════════════╗ # ║ EDIT YOUR PROBLEM STATEMENT HERE ║ # ╚══════════════════════════════════════════════════════════╝ PROBLEM = """ Users are reporting intermittent 504 Gateway Timeout errors when trying to upload files larger than 10MB through our web application. The issue started after last Friday's deployment and seems worse during peak hours (2-5 PM EST). Some users also report that smaller file uploads work fine but the progress bar freezes at 85% for large files before timing out. """ # ════════════════════════════════════════════════════════════ agent_responses = [] def agent_response_callback(message: ChatMessageContent) -> None: name = message.name or "Unknown" content = message.content or "" agent_responses.append({"agent": name, "content": content}) emoji = { "ClientAnalyst": "🖥️", "NetworkAnalyst": "🌐", "ServerAnalyst": "⚙️", "Coordinator": "🎯" }.get(name, "🔹") round_num = (len(agent_responses) - 1) // len(agents) + 1 display(Markdown( f"---\n### {emoji} {name} (Message {len(agent_responses)}, Round {round_num})\n\n{content}" )) MAX_ROUNDS = 8 # 4 agents × 2 rounds = 8 messages exactly task = f"""## Problem Statement {PROBLEM.strip()} ## Discussion Rules You are in a GROUP DISCUSSION with 4 members. You can see ALL previous messages. There are exactly 2 rounds. ### ROUND 1 (Messages 1-4): Independent Analysis - ClientAnalyst, NetworkAnalyst, ServerAnalyst: Analyze from YOUR domain only. Give your top 3 most likely causes with evidence and reasoning. - Coordinator: Note patterns across the 3 analyses. Ask 2-3 specific questions. Do NOT assign tasks yet. ### ROUND 2 (Messages 5-8): Cross-Examination & Final Report - ClientAnalyst, NetworkAnalyst, ServerAnalyst: You MUST reference the OTHER specialists BY NAME. State where you agree, disagree, or have new insights. Answer the Coordinator's questions. Provide SUBSTANTIVE analysis. - Coordinator: Produce the FINAL structured report: root cause, confidence levels, prioritized action plan with owners and time estimates. IMPORTANT: Each agent speaks as THEMSELVES only. Never impersonate another agent.""" display(Markdown(f"## Problem Statement\n\n{PROBLEM.strip()}")) display(Markdown(f"---\n## Discussion Starting — {len(agents)} agents, {MAX_ROUNDS} rounds\n")) # Build and run orchestration = GroupChatOrchestration( members=agents, manager=RoundRobinGroupChatManager(max_rounds=MAX_ROUNDS), agent_response_callback=agent_response_callback, ) runtime = InProcessRuntime() runtime.start() result = await orchestration.invoke(task=task, runtime=runtime) final_result = await result.get(timeout=300) await runtime.stop_when_idle() display(Markdown(f"---\n## FINAL CONCLUSION\n\n{final_result}")) Cell 5: Statistics and Validation print("═" * 55) print(" ANALYSIS STATISTICS") print("═" * 55) emojis = {"ClientAnalyst": "🖥️", "NetworkAnalyst": "🌐", "ServerAnalyst": "⚙️", "Coordinator": "🎯"} agent_counts = {} agent_chars = {} for r in agent_responses: agent_counts[r["agent"]] = agent_counts.get(r["agent"], 0) + 1 agent_chars[r["agent"]] = agent_chars.get(r["agent"], 0) + len(r["content"]) for agent, count in agent_counts.items(): em = emojis.get(agent, "🔹") chars = agent_chars.get(agent, 0) avg = chars // count if count else 0 print(f" {em} {agent}: {count} msg(s), ~{chars:,} chars (avg {avg:,}/msg)") print(f"\n Total messages: {len(agent_responses)}") total_chars = sum(len(r['content']) for r in agent_responses) print(f" Total analysis: ~{total_chars:,} characters") # Validation print(f"\n Validation:") import re identity_issues = [] for r in agent_responses: other_agents = [a.name for a in agents if a.name != r["agent"]] for other in other_agents: pattern = rf'(?i)as {re.escape(other)}[,:]?\s+I\b' if re.search(pattern, r["content"][:300]): identity_issues.append(f"{r['agent']} impersonated {other}") if identity_issues: print(f" Identity confusion: {identity_issues}") else: print(f" No identity confusion detected") thin = [r for r in agent_responses if len(r["content"].strip()) < 100] if thin: for t in thin: print(f" Thin response from {t['agent']}") else: print(f" All responses are substantive") Cell 6: Save Report from datetime import datetime timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") filename = f"analysis_report_{timestamp}.md" with open(filename, "w", encoding="utf-8") as f: f.write(f"# Problem Analysis Report\n\n") f.write(f"**Generated:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n") f.write(f"**Agents:** {', '.join(a.name for a in agents)}\n") f.write(f"**Rounds:** {MAX_ROUNDS}\n\n---\n\n") f.write(f"## Problem Statement\n\n{PROBLEM.strip()}\n\n---\n\n") for i, r in enumerate(agent_responses, 1): em = emojis.get(r['agent'], '🔹') round_num = (i - 1) // len(agents) + 1 f.write(f"### {em} {r['agent']} (Message {i}, Round {round_num})\n\n") f.write(f"{r['content']}\n\n---\n\n") f.write(f"## Final Conclusion\n\n{final_result}\n") print(f"Report saved to: {filename}") 7. The Agent Interaction Flow — Round by Round Here's what actually happens during the 8-message orchestration: Round 1: Independent Analysis (Messages 1-4) Msg Agent What They See What They Do 1 ClientAnalyst Problem statement only Analyzes from client perspective: upload chunking, progress bar freezing at 85%, CORS, content-type headers 2 NetworkAnalyst Problem + ClientAnalyst's analysis Gives INDEPENDENT analysis despite seeing msg 1: load balancer timeouts, proxy body size limits, TCP window scaling 3 ServerAnalyst Problem + msgs 1-2 Gives INDEPENDENT analysis: recent deployment regression, request body parser, thread pool exhaustion, disk space 4 Coordinator Problem + msgs 1-3 Observes patterns: "All three mention timeout configuration. ClientAnalyst and NetworkAnalyst both point to body size. Question: Was the deployment a backend-only change or did it include infra?" Round 2: Cross-Examination (Messages 5-8) Msg Agent What They Do 5 ClientAnalyst "I agree with NetworkAnalyst that the load balancer timeout is likely a factor — the 85% freeze point matches the 30-second LB timeout for a 10MB upload on our average upload speed. However, I disagree with ServerAnalyst about thread pool exhaustion because the UI shows a clean 504, not a connection reset." 6 NetworkAnalyst "ServerAnalyst's point about the recent deployment is critical — if the new request parser is 3x slower, that would push uploads past the LB timeout. I can confirm the LB has a 30s idle timeout. The fix is both: increase LB timeout AND optimize the parser." 7 ServerAnalyst "Responding to Coordinator's question: The deployment was backend-only — a new multipart parser using streaming instead of buffered reads. ClientAnalyst is correct that the 504 is from the LB, not the app. The app itself returns 200 after 45 seconds, but the LB kills the connection at 30." 8 Coordinator Produces final structured report with root cause: "The backend deployment introduced a slower multipart parser (45s vs 15s for 10MB). The load balancer's 30s timeout kills the connection at ~85% progress. Fix: immediate — increase LB timeout to 120s. Short-term — optimize parser. Long-term — implement chunked uploads with progress resumption." Notice: The Round 2 analysis is dramatically better than Round 1. Agents reference each other by name, build on each other's findings, and the Coordinator can synthesize a cross-layer causal chain that no single agent could have produced. I made a small adjustment to the issue with Azure Web Apps. Please find the details below from testing carried out using Google Gemini: 8. Bugs I Found & Fixed — Lessons Learned Building this demo taught me several important lessons about multi-agent systems: Bug 1: Agents Speaking Only Once Symptom: Only 4 messages instead of 8. Root cause: The agents list was missing the Coordinator. It was defined in a separate cell and wasn't included in the members list. Fix: All 4 agents must be in the same list passed to GroupChatOrchestration. Bug 2: NetworkAnalyst Says "I'm Ready to Proceed" Symptom: NetworkAnalyst's Round 2 response was just "I'm ready to proceed with the analysis" — no actual content. Root cause: The Coordinator's Round 1 message was assigning tasks ("NetworkAnalyst, please check the load balancer config"), and the agent was acknowledging the assignment instead of analyzing. Fix: Added explicit constraint to Coordinator: "Round 1 MUST NOT assign tasks — observation + questions ONLY." Bug 3: ServerAnalyst Says "As NetworkAnalyst, I..." Symptom: ServerAnalyst's response started with "As NetworkAnalyst, I believe..." Root cause: LLM identity bleeding. When agents share ChatHistory, the LLM sometimes loses track of which agent it's currently playing. This is especially common with Gemini. Fix: Identity anchoring at the very top of every agent's instructions: "You are ONLY ServerAnalyst. You must NEVER speak as ClientAnalyst, NetworkAnalyst, or Coordinator." Bug 4: Gemini Gives Thin/Empty Responses Symptom: Some agents responded with just one sentence or "I concur." Root cause: Gemini 2.5 Flash is more concise than GPT-4o by default. Without explicit length requirements, it takes shortcuts. Fix: Added "Every response MUST be at least 200 words" and "Answer the Coordinator's questions" to every specialist's instructions. Bug 5: Coordinator's Report is 18K Characters Symptom: The Coordinator's Round 2 response was absurdly long — repeating everything every specialist said. Fix: Added word limits: "Round 1 max 300 words, Round 2 max 800 words" and "Synthesize, don't echo." Bug 6: MAX_ROUNDS Math Symptom: With MAX_ROUNDS=9, ClientAnalyst spoke a 3rd time after the Coordinator's final report — breaking the clean 2-round structure. Fix: MAX_ROUNDS must equal (number of agents × number of rounds). For 4 agents × 2 rounds = 8. 9. Running with Different AI Providers The beauty of SK's Strategy Pattern is that you change ONE LINE to switch providers. Everything else — agents, orchestration, callbacks, validation — stays identical. Gemini setup: from semantic_kernel.connectors.ai.google.google_ai import GoogleAIChatCompletion service = GoogleAIChatCompletion( gemini_model_id="gemini-2.5-flash", api_key=os.getenv("GOOGLE_AI_API_KEY"), ) OpenAI Setup from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion service = OpenAIChatCompletion( ai_model_id="gpt-4o", api_key=os.getenv("OPEN_AI_API_KEY"), ) 10. What to Build Next Add Plugins to Agents Give agents real tools — not just LLM reasoning - looks exciting right ;) class NetworkDiagnosticPlugin: (description="Pings a host and returns latency") def ping(self, host: str) -> str: result = subprocess.run(["ping", "-c", "3", host], capture_output=True, text=True) return result.stdout class LogSearchPlugin: (description="Searches server logs for error patterns") def search_logs(self, pattern: str, hours: int = 1) -> str: # Query your log aggregator (Splunk, ELK, Azure Monitor) return query_logs(pattern, hours) Add Filters for Governance Intercept every agent call for PII redaction and audit logging: .filter(filter_type=FilterTypes.FUNCTION_INVOCATION) async def audit_filter(context, next): print(f"[AUDIT] {context.function.name} called by agent") await next(context) print(f"[AUDIT] {context.function.name} returned") Try Different Orchestration Patterns Replace GroupChat with Sequential for a pipeline approach: # Instead of debate, each agent builds on the previous orchestration = SequentialOrchestration( members=[client_agent, network_agent, server_agent, coordinator_agent] ) Or Concurrent for parallel analysis: # All specialists analyze simultaneously, Coordinator aggregates orchestration = ConcurrentOrchestration( members=[client_agent, network_agent, server_agent] ) Deploy to Azure Move from InProcessRuntime to Azure Container Apps for production scaling. The agent code doesn't change — only the runtime. Summary The key insight from building this demo: multi-agent systems produce better results than single agents not because each agent is smarter, but because the debate structure forces cross-domain thinking that a single prompt can never achieve. The Coordinator's final report consistently identifies causal chains that span client, network, and server layers — exactly the kind of insight that production incident response teams need. Semantic Kernel makes this possible with clean separation of concerns: agents define WHAT to analyze, orchestration defines HOW they interact, the manager defines WHO speaks when, the runtime handles WHERE it executes, and callbacks let you OBSERVE everything. Each piece is independently swappable — that's the power of SK from Microsoft. Resources: GitHub: github.com/microsoft/semantic-kernel Docs: learn.microsoft.com/semantic-kernel Orchestration Patterns: learn.microsoft.com/semantic-kernel/frameworks/agent/agent-orchestration Discord: aka.ms/sk/discord Disclaimer: The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you found it helpful and informative for building AI agents with SK (Semantic Kernel) 😀How to Learn Microsoft Azure in 2020
How to Learn Microsoft Azure in 2020 🎉☁🎓 The year 2019 is almost over, and usually, we take the time to look back at the year and also to find some New Year’s resolutions for the new year. Why not take all that energy and prepare for the cloud computing era and advance your career by learning Microsoft Azure. In this post, I try to give you a quick look at how you can get started to learn Microsoft Azure in 2020. You can read more here: https://www.thomasmaurer.ch/2019/12/how-to-learn-microsoft-azure-in-2020/Creating a Reliable Notification System for Azure Spot VM Evictions (preempt) events
Introduction Azure Spot VMs offer significant cost savings but come with a trade-off: they can be evicted with minimal notice when Azure needs the capacity back or price change. Building a reliable notification system for these evictions is critical for applications that need to respond gracefully to these events. What are Azure Spot VMs? Azure Spot VMs are virtual machines that use spare capacity in Azure data centers, available at significantly discounted prices compared to regular pay-as-you-go VMs. Microsoft offers this unused capacity at discounts of up to 90% off the standard prices, making Spot VMs an extremely cost-effective option for many workloads. However, there's an important caveat: when Azure needs this capacity back for regular pay-as-you-go customers, your Spot VMs can be evicted (reclaimed) with minimal notice - typically just 30 seconds. This eviction mechanism is what allows Microsoft to offer such deep discounts, as we maintain the flexibility to reclaim these resources when needed. https://azure.microsoft.com/en-gb/products/virtual-machines/spot Benefits of Spot VMs Significant cost savings: The most obvious benefit is the substantial discount, which can be up to 90% off standard VM prices. Same VM types and features: Spot VMs provide the same performance, features, and capabilities as regular VMs - the only difference is the eviction possibility. Ideal for interruptible workloads: For workloads that can handle interruptions, such as batch processing jobs, dev/test environments, or stateless applications, Spot VMs offer enormous value. Flexible sizing options: Spot VMs are available in most VM series and regions, giving you access to a wide range of computing options. Scaling opportunities: The cost savings enable you to run larger clusters or more powerful VMs than might be financially feasible with regular VMs. Effective for burst capacity: When you need additional capacity for temporary workloads, Spot VMs can provide it at minimal cost. Great for fault-tolerant applications: Modern cloud-native applications designed with redundancy and resilience can leverage Spot VMs excellently since they're built to handle node failures. Why Not Just Use Azure Resource Events? A common question is: "Why not simply listen for Azure Resource events like ResourceActionSuccess for VM evictions?" While Azure does emit platform events when resources change state through resource group as source for Azure Event Grid topic subscription, there are several critical limitations when relying on these for Spot VM evictions: Timing issues: By the time a ResourceActionSuccess event is generated for a VM eviction, it is possible that the VM is already being evicted. This gives you no time to perform graceful shutdown procedures. Reliability concerns: These events pass through multiple Azure systems before reaching your event handlers, adding potential points of failure and latency. Ambiguous events: Resource action events don't clearly distinguish between a normal VM shutdown and a Spot VM eviction, making it difficult to trigger the right response. For example: I initially attempted to capture Azure Spot VM eviction events by setting up event notifications on an Azure resource group and publishing them to Service Bus. While this configuration successfully captured some Azure Resource events, it ultimately proved unreliable for eviction monitoring. The solution missed several critical eviction events and, more problematically, could not reliably distinguish between intentional VM shutdowns and actual eviction events. This lack of differentiation made automated response handling impossible, as the system couldn't determine whether a VM was being evicted by Azure or simply stopped through normal administrative actions. Azure resource group as an Event Grid source - Azure Event Grid | Microsoft Learn For these reasons, the most reliable approach is to detect eviction events directly from within the VM using the Azure Instance Metadata Service (IMDS) Scheduled Events API, which is specifically designed to provide advance notice of impending VM state changes. This blog post will guide you through implementing a solution that: Detects Spot VM eviction events from within the VM Formats these events properly Sends them to an Azure Event Grid custom topic Sets up proper event handling downstream Understanding Spot VM Eviction Notices Spot VMs receive eviction notifications approximately 30 seconds before being reclaimed. These notifications are delivered through the Azure Instance Metadata Service (IMDS) Scheduled Events API - an endpoint available from within the VM at http://169.254.169.254/metadata/scheduledevents. When a Spot VM is about to be evicted, a "Preempt" event appears in the Scheduled Events data. Your application needs to poll this endpoint regularly to detect these events in time to take action. https://learn.microsoft.com/en-us/azure/virtual-machines/windows/scheduled-events Solution overview Our solution consists of below components: A custom Event Grid topic to receive and distribute the events - optional if you wish to handle on own from VM A monitoring script running inside the Spot VM - actual script to poll events running on VM Logic to format and send events from the VM to Event Grid Event subscribers that take action when evictions occur A) Setting Up the Event Grid Custom Topic First, create an Event Grid custom topic that will serve as the distribution mechanism for your eviction events - this can be optional if you plan to take actions from VM only like gracefully shutting down any existing processes. You can use below documentation to create custom event grid topic: Custom topics in Azure Event Grid - Azure Event Grid | Microsoft Learn B) Creating a Windows-Based Eviction Monitor For Windows Spot VMs, we'll use below PowerShell to poll preempt events & send it to custom event grid. Create a script file named SpotMonitor.ps1: Powershell script : SpotMonitor.ps1 # Configuration variables - replace with your values $EventGridTopicEndpoint = "https://<EG topic name>.westeurope-1.eventgrid.azure.net/api/events" $EventGridKey = "<EG key>" $CheckInterval = 3 # seconds between checks - feel free to modify as per your requirement $LogFile = "C:\Logs\spot-monitor.log" # Create log directory if it doesn't exist if (-not (Test-Path (Split-Path $LogFile))) { New-Item -ItemType Directory -Path (Split-Path $LogFile) -Force } function Write-Log { param ([string]$Message) $timestamp = Get-Date -Format "yyyy-MM-dd HH:mm:ss" "$timestamp - $Message" | Out-File -FilePath $LogFile -Append } Write-Log "Starting Spot VM eviction monitor..." while ($true) { try { # Get the VM's metadata including scheduled events $headers = @{"Metadata" = "true"} $scheduledEvents = Invoke-RestMethod -Uri "http://169.254.169.254/metadata/scheduledevents?api-version=2020-07-01" -Headers $headers # Check if there are any events if ($scheduledEvents.Events -and $scheduledEvents.Events.Count -gt 0) { Write-Log "Found $($scheduledEvents.Events.Count) scheduled events" # Get VM metadata for context $vmName = Invoke-RestMethod -Uri "http://169.254.169.254/metadata/instance/compute/name?api-version=2020-09-01&format=text" -Headers $headers $resourceGroup = Invoke-RestMethod -Uri "http://169.254.169.254/metadata/instance/compute/resourceGroupName?api-version=2020-09-01&format=text" -Headers $headers $subscription = Invoke-RestMethod -Uri "http://169.254.169.254/metadata/instance/compute/subscriptionId?api-version=2020-09-01&format=text" -Headers $headers # Process each event foreach ($event in $scheduledEvents.Events) { if ($event.EventType -eq "Preempt") { Write-Log "ALERT: Spot VM preemption detected!" # Extract event details $eventId = $event.EventId $notBefore = $event.NotBefore Write-Log "VM $vmName will be preempted not before $notBefore" # Create Event Grid event as an array (critical for EventGrid schema) $eventGridEvent = @( @{ subject = "/subscriptions/$subscription/resourceGroups/$resourceGroup/providers/Microsoft.Compute/virtualMachines/$vmName" eventType = "SpotVM.Preemption" eventTime = (Get-Date).ToUniversalTime().ToString("o") id = [Guid]::NewGuid().ToString() data = @{ vmName = $vmName resourceGroup = $resourceGroup subscription = $subscription preemptionTime = $notBefore eventId = $eventId eventType = $event.EventType } dataVersion = "1.0" } ) # Convert to JSON - ensuring it stays as an array $eventGridPayload = ConvertTo-Json -InputObject $eventGridEvent -Depth 10 # Send to Event Grid $eventGridHeaders = @{ "Content-Type" = "application/json" "aeg-sas-key" = $EventGridKey } try { $response = Invoke-RestMethod -Uri $EventGridTopicEndpoint -Method Post -Body $eventGridPayload -Headers $eventGridHeaders Write-Log "Successfully sent event to Event Grid" # Take actions to prepare for shutdown Write-Log "Taking actions to prepare for shutdown..." # Example: Stop services gracefully # Stop-Service -Name "YourServiceName" -Force } catch { Write-Log "Failed to send to Event Grid: $_" } } } } } catch { Write-Log "Error checking for events: $_" } # Wait before checking again Start-Sleep -Seconds $CheckInterval } The script above checks for eviction events every 3 seconds by default. You can adjust this polling frequency by changing the "Check_Interval" variable in the script to better match your specific system requirements and performance considerations. More frequent polling provides faster detection but increases resource usage, while less frequent polling reduces overhead but might slightly delay event detection. B) Running monitor script as a scheduler or service For Windows Spot VMs, we'll use PowerShell to create a monitoring service. Run a script file named SpotMonitor.ps1 created in last step: You can use Windows Task Scheduler to run the script at startup or to run as a service and the logs will looks like this: Logs: 2025-03-19 18:48:27 - Starting Spot VM eviction monitor... 2025-03-19 20:04:33 - Found 1 scheduled events 2025-03-19 20:04:33 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:33 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:33 - Sending payload: [ { "eventTime": "2025-03-19T20:04:33.4655660Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "5d3e6430-dff5-45da-ae90-992e3e342d37", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:33 - Event Grid response: 2025-03-19 20:04:33 - Taking actions to prepare for shutdown... 2025-03-19 20:04:36 - Found 1 scheduled events 2025-03-19 20:04:36 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:36 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:36 - Sending payload: [ { "eventTime": "2025-03-19T20:04:36.6382480Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "b6152429-f4cb-43b9-8c53-b6ceb08946e5", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:36 - Event Grid response: 2025-03-19 20:04:36 - Taking actions to prepare for shutdown... 2025-03-19 20:04:39 - Found 1 scheduled events 2025-03-19 20:04:39 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:39 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:39 - Sending payload: [ { "eventTime": "2025-03-19T20:04:39.7567285Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "e0bde6d0-ae27-4c01-8e69-621e57d70f8d", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:39 - Event Grid response: 2025-03-19 20:04:39 - Taking actions to prepare for shutdown... 2025-03-19 20:04:42 - Found 1 scheduled events 2025-03-19 20:04:42 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:42 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:42 - Sending payload: [ { "eventTime": "2025-03-19T20:04:42.8339675Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "ab7a3b84-bcd8-4651-829e-c57043c54b92", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:42 - Event Grid response: 2025-03-19 20:04:42 - Taking actions to prepare for shutdown... 2025-03-19 20:04:45 - Found 1 scheduled events 2025-03-19 20:04:45 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:45 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:45 - Sending payload: [ { "eventTime": "2025-03-19T20:04:45.9317109Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "eacfae6b-4ea5-426d-8bc2-659320a7baf0", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:45 - Event Grid response: 2025-03-19 20:04:45 - Taking actions to prepare for shutdown... 2025-03-19 20:04:48 - Found 1 scheduled events 2025-03-19 20:04:49 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:49 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:49 - Sending payload: [ { "eventTime": "2025-03-19T20:04:49.0666732Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "b2142ee8-9ecf-441d-846e-c8ed663a949e", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:49 - Event Grid response: 2025-03-19 20:04:49 - Taking actions to prepare for shutdown... 2025-03-19 20:04:52 - Found 1 scheduled events 2025-03-19 20:04:52 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:52 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:52 - Sending payload: [ { "eventTime": "2025-03-19T20:04:52.1310990Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "d9eba318-9773-4e73-a694-dd1c1bf89c10", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:52 - Event Grid response: 2025-03-19 20:04:52 - Taking actions to prepare for shutdown... 2025-03-19 20:04:55 - Found 1 scheduled events 2025-03-19 20:04:55 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:55 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:55 - Sending payload: [ { "eventTime": "2025-03-19T20:04:55.2171546Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "c358c433-50f5-496d-8823-c2ffddd03390", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:55 - Event Grid response: 2025-03-19 20:04:55 - Taking actions to prepare for shutdown... 2025-03-19 20:04:58 - Found 1 scheduled events 2025-03-19 20:04:58 - ALERT: Spot VM preemption detected! 2025-03-19 20:04:58 - VM anivmnew will be preempted not before Wed, 19 Mar 2025 20:04:47 GMT 2025-03-19 20:04:58 - Sending payload: [ { "eventTime": "2025-03-19T20:04:58.3040422Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "Wed, 19 Mar 2025 20:04:47 GMT", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "3eacba95-e05f-41dc-b9e7-1593fe2a71e2", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:04:58 - Event Grid response: 2025-03-19 20:04:58 - Taking actions to prepare for shutdown... 2025-03-19 20:05:01 - Found 1 scheduled events 2025-03-19 20:05:01 - ALERT: Spot VM preemption detected! 2025-03-19 20:05:01 - VM anivmnew will be preempted not before 2025-03-19 20:05:01 - Sending payload: [ { "eventTime": "2025-03-19T20:05:01.3842973Z", "data": { "eventId": "DE2EC5FA-AF0A-4D59-85D2-677C66A6BC12", "preemptionTime": "", "eventType": "Preempt", "resourceGroup": "RG-TEST", "subscription": "azure-sub-id", "vmName": "anivmnew" }, "id": "85c058fc-4f2e-49ec-a027-6fcca60f7935", "subject": "/subscriptions/azure-sub-id/resourceGroups/RG-TEST/providers/Microsoft.Compute/virtualMachines/anivmnew", "eventType": "SpotVM.Preemption", "dataVersion": "1.0" } ] 2025-03-19 20:05:01 - Event Grid response: 2025-03-19 20:05:01 - Taking actions to prepare for shutdown... C) Configuring event subscribers Now that your Spot VMs are sending eviction events to Event Grid, set up subscribers to take action when these events occur. For example sending event to service bus queue: Conclusion By implementing this solution, you've created a reliable way to detect and respond to Spot VM evictions. This approach gives your applications precious time to react to evictions, significantly improving reliability while still benefiting from the cost savings of Spot VMs. While Azure does provide resource-level events through system topics, they simply don't provide the reliability, timing, and clarity needed for mission-critical workloads running on Spot VMs. The combination of the Azure Instance Metadata Service Scheduled Events API and custom Event Grid topics creates a powerful pattern for building resilient, event-driven architectures. This approach ensures you're getting the most accurate and timely notifications possible, giving your applications the best chance to gracefully handle Spot VM evictions while enjoying the substantial cost benefits that Spot VMs offer. Disclaimer The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you've found this approach to handling Spot VM evictions helpful
Azure support team not responding to support request
I am posting here because I have not received a response to my support request despite my plan stating that I should hear back within 8 hours. It has now gone a day beyond that limit, and I am still waiting for assistance with this urgent matter. This issue is critical for my operations, and the delay is unacceptable. The ticket/reference number for my original support request was 2410100040000309. And I have created a brand new service request with ID 2412160040010160. I need this addressed immediately.
Unable to install "Microsoft Azure PowerShell with Microsoft Azure SDK" using Web Platform installer
I want to develop a remote event receiver for our sharepoint online list and i am following the steps mentioned in this link @ Remote Event Receivers Creating and Debugging in SharePoint Online , where inside the link they mentioned to install "Microsoft Azure PowerShell with Microsoft Azure SDK" and they used the Web Platform installer. so inside my windows-7 i opened the Web Platform installer >> search for "Microsoft Azure PowerShell" >> but i did not get any item named "Microsoft Azure PowerShell with Microsoft Azure SDK", here are the items i got :- while on the above link, they have added an item named "Microsoft Azure PowerShell with Microsoft Azure SDK", as follow:- so any idea what is going on? why in my case i can not find any item named "Microsoft Azure PowerShell with Microsoft Azure SDK" inside the Web Platform installer Thnaks.
