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Running Commands Across VM Scale Set Instances Without RDP/SSH Using Azure CLI Run Command
If you’ve ever managed an Azure Virtual Machine Scale Set (VMSS), you’ve likely run into this situation: You need to validate something across all nodes, such as: Checking a configuration value Retrieving logs Applying a registry change Confirming runtime settings Running a quick diagnostic command And then you realize: You’re not dealing with two or three machines you’re dealing with 40… 80… or even hundreds of instances. The Traditional Approach (and Its Limitations) Historically, administrators would: Open RDP connections to Windows nodes SSH into Linux nodes Execute commands manually on each instance While this may work for a small number of machines, in real‑world environments such as: Azure Batch (user‑managed pools) Azure Service Fabric (classic clusters) VMSS‑based application tiers This approach quickly becomes: Operationally inefficient Time‑consuming Sometimes impossible Especially when: RDP or SSH ports are blocked Network Security Groups restrict inbound connectivity Administrative credentials are unavailable Network configuration issues prevent guest access Azure Run Command To address this, Azure provides a built‑in capability to execute commands inside virtual machines through the Azure control plane, without requiring direct guest OS connectivity. This feature is called Run Command. You can review the official documentation here: Run scripts in a Linux VM in Azure using action Run Commands - Azure Virtual Machines | Microsoft Learn Run scripts in a Windows VM in Azure using action Run Commands - Azure Virtual Machines | Microsoft Learn Run Command uses the Azure VM Agent installed on the virtual machine to execute PowerShell or shell scripts directly inside the guest OS. Because execution happens via the Azure control plane, you can run commands even when: RDP or SSH ports are blocked NSGs restrict inbound access Administrative user configuration is broken In fact, Run Command is specifically designed to troubleshoot and remediate virtual machines that cannot be accessed through standard remote access methods. Prerequisites & Restrictions. Before using Run Command, ensure the following: VM Agent installed and in Ready state Outbound connectivity from the VM to Azure public IPs over TCP 443 to return execution results. If outbound connectivity is blocked, scripts may run successfully but no output will be returned to the caller. Additional limitations include: Output limited to the last 4,096 bytes One script execution at a time per VM Interactive scripts are not supported Maximum execution time of 90 minutes Full list of restrictions and limitations are available here: https://learn.microsoft.com/en-us/azure/virtual-machines/windows/run-command?tabs=portal%2Cpowershellremove#restrictions Required Permissions (RBAC) Executing Run Command requires appropriate Azure RBAC permissions. Action Permission List available Run Commands Microsoft.Compute/locations/runCommands/read Execute Run Command Microsoft.Compute/virtualMachines/runCommand/action The execution permission is included in: Virtual Machine Contributor role (or higher) Users without this permission will be unable to execute remote scripts through Run Command. Azure CLI: az vm vs az vmss When using Azure CLI, you’ll encounter two similar‑looking commands that behave very differently. az vm run-command invoke Used for standalone VMs Also used for Flexible VM Scale Sets Targets VMs by name az vmss run-command invoke Used only for Uniform VM Scale Sets Targets instances by numeric instanceId (0, 1, 2, …) Example: az vmss run-command invoke --instance-id <id> Unlike standalone VM execution, VMSS instances must be referenced using the parameter "--instance-id" to identify which scale set instance will run the script. Important: Uniform vs Flexible VM Scale Sets This distinction is critical when automating Run Command execution. Uniform VM Scale Sets Instances are managed as identical replicas Each instance has a numeric instanceId Supported by az vmss run-command invoke Flexible VM Scale Sets Each instance is a first‑class Azure VM resource Instance identifiers are VM names, not numbers az vmss run-command invoke is not supported Must use az vm run-command invoke per VM To determine which orchestration mode your VMSS uses: az vmss show -g "${RG}" -n "${VMSS}" --query "orchestrationMode" -o tsv Windows vs Linux Targets Choose the appropriate command ID based on the guest OS: Windows VMs → RunPowerShellScript Linux VMs → RunShellScript Example Scenario - Retrieve Hostname From All VMSS Instances The following examples demonstrate how to retrieve the hostname from all VMSS instances using Azure CLI and Bash. Flexible VMSS, Bash (Azure CLI) RG="<ResourceGroup>" VMSS="<VMSSName>" SUBSCRIPTION_ID="<SubscriptionID>" az account set --subscription "${SUBSCRIPTION_ID}" VM_NAMES=$(az vmss list-instances \ -g "${RG}" \ -n "${VMSS}" \ --query "[].name" \ -o tsv) for VM in $VM_NAMES; do echo "Running on VM: $VM" az vm run-command invoke \ -g "${RG}" \ -n "$VM" \ --command-id RunShellScript \ --scripts "hostname" \ --query "value[0].message" \ -o tsv done Uniform VMSS, Bash (Azure CLI) RG="<ResourceGroup>" VMSS="<VMSSName>" SUBSCRIPTION_ID="<SubscriptionID>" az account set --subscription "${SUBSCRIPTION_ID}" INSTANCE_IDS=$(az vmss list-instances -g "${RG}" -n "${VMSS}" --query "[].instanceId" -o tsv) for ID in $INSTANCE_IDS; do echo "Running on instanceId: $ID" az vmss run-command invoke \ -g "${RG}" \ -n "${VMSS}" \ --instance-id "$ID" \ --command-id RunShellScript \ --scripts "hostname" \ --query "value[0].message" \ -o tsv done Summary Azure Run Command provides a scalable method to: Execute diagnostics Apply configuration changes Collect logs Validate runtime settings …across VMSS instances without requiring RDP or SSH connectivity. This significantly simplifies operational workflows in large‑scale compute environments such as: Azure Batch (user‑managed pools) Azure Service Fabric classic clusters VMSS‑based application tiers
[Architecture Pattern] Scaling Sync-over-Async Edge Gateways by Bypassing Service Bus Sessions
Hi everyone, I wanted to share an architectural pattern and an open-source implementation we recently built to solve a major scaling bottleneck at the edge: bridging legacy synchronous HTTP clients to long-running asynchronous AI workers. The Problem: Stateful Bottlenecks at the Edge When dealing with slow AI generation tasks (e.g., 45+ seconds), standard REST APIs will drop the connection resulting in 504 Gateway Timeouts. The standard integration pattern here is Sync-over-Async. The Gateway accepts the HTTP request, drops a message onto Azure Service Bus, waits for the worker to reply, and maps the reply back to the open HTTP connection. However, the default approach is to use Service Bus Sessions for request-reply correlation. At scale, this introduces severe limitations: 1. Stateful Gateways: The Gateway pod must request an exclusive lock on the session. It becomes tightly coupled to that specific request. 2. Horizontal Elasticity is Broken: If a reply arrives, it must go to the specific pod holding the lock. Other idle pods cannot assist. 3. Hard Limits: A traffic spike easily exhausts the namespace concurrent session limits (especially on the Standard tier). The Solution: Stateless Filtered Topics To achieve true horizontal scale, the API Gateway layer must be 100% stateless. We bypassed Sessions entirely by pushing the routing logic down to the broker using a Filtered Topic Pattern. How it works: 1. The Gateway injects a CorrelationId property (e.g., Instance-A-Req-1) into the outbound request. 2. Instead of locking a session, the Gateway spins up a lightweight, dynamic subscription on a shared Reply Topic with a SQL Filter: CorrelationId = 'Instance-A-Req-1'. 3. The AI worker processes the task and drops the reply onto the shared topic with the same property. 4. The Azure Service Bus broker evaluates the SQL filter and pushes the message directly to the correct Gateway pod. No session locks. No implicit instance affinity. Complete horizontal scalability. If a pod crashes, its temporary subscription simply drops—preventing locked poison messages. Open Source Implementation Implementing dynamic Service Bus Administration clients and receiver lifecycles is complex, so I abstracted this pattern into a Spring Boot starter for the community. It handles all the dynamic subscription and routing logic under the hood, allowing developers to execute highly scalable Sync-over-Async flows with a single line of code returning a CompletableFuture. GitHub Repository: https://github.com/ShivamSaluja/sentinel-servicebus-starter Full Technical Write-up: https://dev.to/shivamsaluja/sync-over-async-bypassing-azure-service-bus-session-limits-for-ai-workloads-269d I would love to hear from other architects in this hub. Have you run into similar session exhaustion limits when building Edge API Gateways? Have you adopted similar stateless broker-side routing, or do you rely on sticky sessions at your load balancers?Proyecto Escolar Tecnológico
Estamos haciendo un trabajo de investigación sobre las nuevas tecnologías aplicadas a la gestión empresarial ya que estamos desarrollando un proyecto de software para el sector de odontología y me gustaría preguntarle a los expertos: ¿Qué tecnologías se consideran "el estándar de oro" o esenciales para aplicar en 2026, y que ustedes ya han utilizado?.
Excited to share my latest open-source project: KubeCost Guardian
After seeing how many DevOps teams struggle with Kubernetes cost visibility on Azure, I built a full-stack cost optimization platform from scratch. 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀: ✅ Real-time AKS cluster monitoring via Azure SDK ✅ Cost breakdown per namespace, node, and pod ✅ AI-powered recommendations generated from actual cluster state ✅ One-click optimization actions ✅ JWT-secured dashboard with full REST API 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: - React 18 + TypeScript + Vite - Tailwind CSS + shadcn/ui + Recharts - Node.js + Express + TypeScript - Azure SDK (@azure/arm-containerservice) - JWT Authentication + Azure Service Principal 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁: Most cost tools show you generic estimates. KubeCost Guardian reads your actual VM size, node count, and cluster configuration to generate recommendations that are specific to your infrastructure not averages. For example, if your cluster has only 2 nodes with no autoscaler enabled, it immediately flags the HA risk and calculates exactly how much you'd save by switching to Spot instances based on your actual VM size. This project is fully open-source and built for the DevOps community. ⭐ GitHub: https://github.com/HlaliMedAmine/kubecost-guardian This project represents hours of hard work, and passion. I decided to make it open-source so everyone can benefit from it 🤝 ,If you find it useful, I’d really appreciate your support . Your support motivates me to keep building and sharing more powerful projects 👌. More exciting ideas are coming soon… stay tuned! 🔥.Pipeline Intelligence is live and open-source real-time Azure DevOps monitoring powered by AI .
Every DevOps team I've worked with had the same problem: Slow pipelines. Zero visibility. No idea where to start. So I stopped complaining and built the solution. So I built something about it. ⚡ Pipeline Intelligence is a full-stack Azure DevOps monitoring dashboard that: ✅ Connects to your real Azure DevOps organization via REST API ✅ Detects bottlenecks across all your pipelines automatically ✅ Calculates exactly how much time your team is wasting per month ✅ Uses Gemini AI to generate prioritized fixes with ready-to-paste YAML solutions ✅ JWT-secured, Docker-ready, and fully open-source Tech Stack: → React 18 + Vite + Tailwind CSS → Node.js + Express + Azure DevOps API v7 → Google Gemini 1.5 Flash → JWT Authentication + Docker 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁? Most tools show you generic estimates. Pipeline Intelligence reads your actual cluster config, node count, and pipeline structure and gives you recommendations specific to your infrastructure. 🎯 This year, I set myself a personal challenge: Build and open-source a series of production-grade tools exclusively focused on Azure services tools that solve real problems for real DevOps teams. This project represents weeks of research, architecture decisions, and late-night debugging sessions. I'm sharing it with the community because I believe great tooling should be accessible to everyone not locked behind enterprise paywalls. If this resonates with you, I have one simple ask: 👉 A like, a comment, or a share takes 3 seconds but it helps this reach the DevOps engineers who need it most. Your support is what keeps me building. ❤️ GitHub: https://github.com/HlaliMedAmine/pipeline-intelligence
Azure VMs host (platform) metrics (not guest metrics) to the log analytics workspace ?
Hi Team, Can some one help me how to send Azure VMs host (platform) metrics (not guest metrics) to the log analytics workspace ? Earlier some years ago I used to do it, by clicking on “Diagnostic Settings”, but now if I go to “Diagnostic Settings” tab its asking me to enable guest level monitoring (guest level metrics I don’t want) and pointing to a Storage Account. I don’t see the option to send the these metrics to Log analytics workspace. I have around 500 azure VMs whose host (platform) metrics (not guest metrics) I want to send it to the log analytics workspace.Building a Production-Ready Azure Lighthouse Deployment Pipeline with EPAC
Recently I worked on an interesting project for an end-to-end Azure Lighthouse implementation. What really stood out to me was the combination of Azure Lighthouse, EPAC, DevOps, and workload identity federation. The deployment model was so compelling that I decided to build and validate the full solution hands-on in my own personal Azure tenants. The result is a detailed article that documents the entire journey, including pipeline design, implementation steps, and the scripts I prepared along the way. You can read the full article here
Azure Key Vault Replication: Why Paired Regions Alone Don’t Guarantee Business Continuity
As customers modernize toward multi‑region architectures in Azure, one question comes up repeatedly: “If my region goes down, will Azure Key Vault continue to work without disruption?” The short answer: it depends on what you mean by “work.” Azure Key Vault provides strong durability and availability guarantees, but those guarantees are often misunderstood—especially when customers assume paired‑region replication equals full disaster recovery. In reality, Azure Key Vault replication is designed for survivability, not uninterrupted write access or customer‑controlled failover. This post explains: How Azure Key Vault replication actually works (per Microsoft Learn) Why paired‑region failover does not equal business continuity Two reference architectures that implement true multi‑region Key Vault availability, with Terraform How Azure Key Vault Replication Works (Per Microsoft Learn) Azure Key Vault includes multiple layers of Microsoft‑managed redundancy. In‑Region and Zone Resiliency Vault contents are replicated within the region. In regions that support availability zones, Key Vault is zone‑resilient by default. This protects against localized hardware or zone failures. Paired‑Region Replication If a Key Vault is deployed in a region with an Azure‑defined paired region, its contents are asynchronously replicated to that paired region. This replication is automatic and cannot be configured, observed, or tested by customers. Microsoft‑Managed Regional Failover If Microsoft declares a full regional outage, requests are automatically routed to the paired region. After failover, the vault operates in read‑only mode: ✅ Read secrets, keys, and certificates ✅ Perform cryptographic operations ❌ Create, update, rotate, or delete secrets, keys, or certificates This is a critical distinction. Paired‑region replication preserves access — not operational continuity. Why Paired‑Region Replication Is Not Business Continuity From a reliability and DR perspective, several limitations matter: Failover is Microsoft‑initiated, not customer‑controlled No write operations during regional failover No secret rotation or certificate renewal No way to test DR Accidental deletions replicate No point‑in‑time recovery without backups Microsoft Learn explicitly states that critical workloads may require custom multi‑region strategies beyond built‑in replication. For many customers, this means Azure Key Vault becomes a single‑region dependency in an otherwise multi‑region application design. The Multi‑Region Key Vault Pattern The two GitHub repositories below implement a common architectural shift: Multiple independent Key Vaults deployed in separate regions, with customer‑controlled replication and failover. Instead of relying on invisible platform replication, the vaults become first‑class, region‑scoped resources, aligned with application failover. Solution 1: Private, Locked‑Down Multi‑Region Key Vault Replication Repository: 👉 https://github.com/jclem2000/KeyVault-MultiRegion-Replication-Private Architecture Highlights Independent Key Vault per region Private Endpoints only No public network exposure Terraform‑based deployment Controlled replication using Event Based synchronization What This Enables ✅ Full read/write access during regional outages ✅ Continued secret rotation and certificate renewal ✅ Customer‑defined failover and RTO ✅ DR testing and validation ✅ Strong alignment with zero‑trust and regulated environments Trade‑offs Higher operational complexity Requires automation and application awareness of multiple vaults Solution 2: Low‑Cost Public Multi‑Region Key Vault Replication Repository: 👉 https://github.com/jclem2000/KeyVault-MultiRegion-Replication-Public Architecture Highlights Independent Key Vault per region Public endpoints Minimal networking dependencies Terraform‑based Controlled replication using Event Based synchronization Optimized for simplicity and cost What This Enables ✅ Full read/write availability in any region ✅ Clear and testable DR posture ✅ Lower cost than private endpoint designs ✅ Suitable for many non‑regulated workloads Trade‑offs Public exposure (mitigated via firewall rules, RBAC, and conditional access) Not appropriate for all compliance requirements Requires automation and application awareness of multiple vaults Azure Native Replication vs Customer‑Managed Multi‑Region Vaults Capability Azure Paired Region Multi‑Region Vaults Read access during outage ✅ ✅ Write access during outage ❌ ✅ Secret rotation during outage ❌ ✅ Customer‑controlled failover ❌ ✅ DR testing ❌ ✅ Isolation from accidental deletion ❌ ✅ Predictable RTO ❌ ✅ Azure Key Vault’s native replication optimizes for platform durability. The multi‑region pattern optimizes for application continuity. When to Use Each Approach Paired‑Region Replication Is Often Enough When: Secrets are mostly static Read‑only access during outages is acceptable RTO is flexible You prefer Microsoft‑managed recovery Multi‑Region Vaults Are Recommended When: Secrets or certificates rotate frequently Applications must remain writable during outages Deterministic failover is required DR testing is mandatory Regulatory or operational isolation is needed Closing Thoughts Azure Key Vault behaves exactly as documented on Microsoft Learn—but it’s important to be clear about what those guarantees mean. Paired‑region replication protects your data, not your ability to operate. If your application is designed to survive a regional outage, Key Vault must follow the same multi‑region design principles as the application itself. The reference architectures above show how to extend Azure’s native durability model into true operational resilience, without waiting for a platform‑level failover decision.
How on god's green earth do you buy an API key? Bing custom search API.
I just want to give Microsoft money. I want to buy an API key for the Bing custom search. How do I do this? I get to Production and click "Click to issue paid tier key" and I keep getting the same god-awful """ Could not create the marketplace item Oops! Could not create the marketplace item Gallery item is required, no gallery item is provided. """ in the Azure marketplace. I just want to spend money. How do I do that?
RC4 Deprecating by April
I’m reviewing our Seamless SSO setup and noticed that the AzureADSSOAcc account is still using RC4 (encryption type 0x17) from Kerberos event logs. I have a few questions regarding this: Why does AzureADSSOAcc still default to RC4 instead of AES, even when the domain supports AES? With Microsoft disabling RC4 (April updates), will AzureADSSOAcc automatically switch to AES? If it does not switch automatically, what is the recommended way to force it to use AES? Is running Update-AzureADSSOForest (key rotation) sufficient, and does it cause any downtime or impact to Seamless SSO? I want to make sure we transition to AES safely without breaking SSO for users. Any guidance or real-world experience would be appreciated.The March 2026 Innovation Challenge Winners
For this round of the Innovation Challenge the organizations we sponsor helped over 15,000 developers get the skills it takes to build AI solutions on Azure. This program is grounded in Microsoft’s mission and designed to enable a diverse and qualified community of professional developers coming together to tackle big problems. We helped almost 1,000 people earn Microsoft certifications and Applied Skills credentials, and 300 participated in the invitation only March 2026 Innovation Challenge hackathon. Teams represented SHPE, Women in Cloud, Código Facilito, DIO, GenSpark, NASA Space Apps, Project Blue Mountain, and TechBridge. Check out the winning project to meet some of the best AI talent in our community and to get inspired about what we can build together! First place $10,000 Pebble. - AI Cognitive Load Companion Pebble. is named after a worry stone: something small and smooth you reach for when the world feels like too much. It's an AI cognitive support companion that turns overwhelming documents, tasks, and information into calm, structured clarity. Built for neurodivergent minds. Useful for everyone. Second place $5,000 The Living Memory Bridge We believe dementia represents the most extreme form of cognitive overload that exists. It is not just information overload. It is cognitive loss: the gradual erosion of the very tools people use to process the world. Every principle in the brief applies here in its most urgent form: simplified language, adaptive communication, calm and dignity-preserving interactions, personalized memory anchors, and support that meets people exactly where they are. Query to Insight Analytics CRAM CRAM is a natural language healthcare analytics platform built entirely on Azure that lets clinical and administrative staff query a patient database using plain English, no SQL required. Users type a question like "What are the top 10 conditions among diabetic patients?" and get back a written summary, a data table, and an auto-generated chart in seconds. Third place $2,500 ClearStep ClearStep is an action-first AI system designed to reduce decision overload in high-risk or confusing situations. Instead of only detecting risk, it tells users exactly what to do next. The core innovation is architectural: model output is not trusted. Every response is enforced by a validation layer that guarantees structure, corrects model errors, and prevents unsafe or misleading outputs from reaching the user. DataTalk Our platform enables seamless data ingestion from Excel, CSV, SharePoint, and OneDrive, processes it through a two-layer analytical pipeline powered by DuckDB, and orchestrates four specialized AI agents that work as a team: understanding intent, reading data structure, generating and self-correcting SQL, and enforcing security and auditability at every step RAGulator AI Governance Engine Advanced, governed, and traceable RAG (Retrieval-Augmented Generation) system for international trade. RAGulator is a 100% functional solution that unifies the Azure intelligence ecosystem to deliver grounded responses with immutable bibliographic citations.
Uninstalling Remote Desktop client closes users' Windows App connections
We have our users working from Windows App now to meet the 3/27 out of support date. We are beginning to uninstall the Remote Desktop from their laptops and are finding it closes active Windows App connections on uninstall (of Remote Desktop). That is less than ideal. Looking to see if any way around that, but wondered if others had seen the same?Detecting ACI IP Drift and Auto-Updating Private DNS (A + PTR) with Event Grid + Azure Functions
Solution Author Aditya_AzureNinja , Chiragsharma30 Solution Version v1.0 TL;DR Azure Container Instances (ACI) container groups can be recreated/updated over time and may receive new private IPs, which can cause DNS mismatches if forward and reverse records aren’t updated. This post shares an event-driven pattern that detects ACI IP drift and automatically reconciles Private DNS A (forward) and PTR (reverse) records using Event Grid + Azure Functions. Key requirement: Event delivery is at-least-once, so the solution must be idempotent. Problem statement In hub-and-spoke environments using per-spoke Private DNS zones for isolation, ACI workloads created/updated/deleted over time can receive new private IPs. We need to ensure: Forward lookup: aci-name.<spoke-zone> (A record) → current ACI private IP Reverse lookup: IP → aci-name.<spoke-zone> (PTR record) Two constraints drive this design: Azure Private DNS auto-registration is VM-only and does not create PTR records, so ACI needs explicit A/PTR record management. Reverse DNS is scoped to the VNet (reverse zone must be linked to the querying VNet, otherwise reverse lookup returns NXDOMAIN). Design principle: This solution was designed with the following non‑negotiable engineering goals: Event‑driven DNS updates must be triggered directly from resource lifecycle events, not polling or scheduled jobs. Container creation, restart, and deletion are the only reliable sources of truth for IP changes in ACI. Idempotent Azure Event Grid delivers events with at‑least‑once semantics. The system must safely process duplicate events without creating conflicting DNS records or failing on retries. Stateless The automation must not rely on in‑memory or persisted state to determine correctness. DNS itself is treated as the baseline state, allowing functions to scale, restart, and replay events without drift or dependency on prior executions. Clear failure modes DNS reconciliation failures must be explicit and observable. If DNS updates fail, the function invocation must fail loudly so the issue is visible, alertable, and actionable—never silently ignored. Components Event Grid subscriptions (filtered to ACI container group lifecycle events) Azure Function App (Python) with System Assigned Managed Identity Private DNS forward zone (A records) Private DNS reverse zone (PTR records) Supporting infra (typical): Storage account (function artifacts / operational needs) Application Insights + Log Analytics (observability) Event-driven flow ACI container group is created/updated/deleted. Event Grid emits a lifecycle event (delivery can be repeated). Function is triggered and reads the current ACI private IP. Function reconciles DNS: Upsert A record to current IP Upsert PTR record to FQDN Remove stale PTR(s) for hostname/IP as needed Function logs reconciliation outcome (updated vs no-op). Architecture overview (INFRA) This follows the“Event-driven registration” approach: Event Grid → Azure Function that reconciles DNS on ACI lifecycle events. RBAC at a glance (Managed Identity) Role Scope Purpose Storage Blob Data Owner Function App deployment storage account Access function artifacts and operational blobs (required because shared key access is disabled). Reader Each ACI workload resource group Read container group state and determine the current private IP. Private DNS Zone Contributor Private DNS forward zone(s) Create, update, and delete A records for ACI hostnames. Private DNS Zone Contributor Private DNS reverse zone(s) Create, update, and clean up PTR records for ACI IPs. Monitoring Metrics Publisher (optional) Data Collection Rule (DCR) Upload structured IP‑drift events to Log Analytics via the ingestion API. --- --- Architecture overview (APP) Event‑Driven DNS Reconciliation for Azure Container Instances 1. Event contract: what the function receives Azure Event Grid delivers events using a consistent envelope (Event Grid schema). Each event includes, at a minimum: topic subject id eventType eventTime data dataVersion metadataVersion In Azure Functions, the Event Grid trigger binding is the recommended way to receive these events directly. Why the subject field matters The subject field typically contains the ARM resource ID path of the affected resource. This solution relies on subject to: verify that the event is for an ACI container group (Microsoft.ContainerInstance/containerGroups) extract: subscription ID resource group name container group name Using subject avoids dependence on publisher‑specific payload fields and keeps parsing fast, deterministic, and resilient. 2. Subscription design: filter hard, process little The solution follows a strict runbook pattern: subscribe only to ARM lifecycle events filter aggressively so only ACI container groups are included trigger reconciliation only on meaningful state transitions Recommended Event Grid event types Microsoft.Resources.ResourceWriteSuccess (create / update / stop state changes) Microsoft.Resources.ResourceDeleteSuccess (container group deletion) Microsoft.Resources.ResourceActionSuccess (optional) (restart / start / stop actions, environment‑dependent) This keeps the Function App simple, predictable, and low‑noise. 3. Application design: two functions, one contract The application is intentionally split into authoritative mutation and read‑only validation. Component A — DNS Reconciler (authoritative writer) A thin Python v2 model wrapper: receives the Event Grid event validates this is an ACI container group event parses identifiers from the ARM subject resolves DNS configuration from a JSON mapping (environment variable) delegates DNS mutation to a deterministic worker script DNS changes are not implemented inline in Python. Instead, the function: constructs a controlled set of environment variables invokes a worker script (/bin/bash) via subprocess streams stdout/stderr into function logs treats non‑zero exit codes as hard failures This thin wrapper + deterministic worker pattern isolates DNS correctness logic while keeping the event handler stable and testable. Component B — IP Drift Tracker (stateless observer) The drift tracker is a read‑only, stateless validator designed for correctness monitoring. It: parses identifiers from the event subject exits early on delete events (nothing to validate) reads the live ACI private IP using the Azure SDK reads the current DNS A record baseline compares live vs DNS state and emits drift telemetry Core comparison logic No DNS record exists → emit first_seen DNS record matches live IP → emit no_change DNS record differs from live IP → emit drift_detected (old/new IP) Optionally, drift events can be shipped to Log Analytics using DCR‑based ingestion. 4. DNS Reconciler: execution flow Step 1 — Early filtering Reject any event whose subject does not contain: Microsoft.ContainerInstance/containerGroups. This avoids unnecessary processing and ensures strict contract enforcement. Step 2 — ARM subject parsing The function splits the subject path and extracts: resource group container group name This approach is fast, robust, and avoids publisher‑specific schema dependencies. Step 3 — Zone configuration resolution DNS configuration is resolved from a JSON map stored in an environment variable. If no matching configuration exists for the resource group: the function logs the condition exits without error Why this matters This keeps the solution multi‑environment without duplicating deployments. Only configuration changes — not code — are required. Step 4 — Delegation to worker logic The function constructs a deterministic runtime context and invokes the worker: forward zone name reverse zone name(s) container group name current private IP TTL and execution flags The worker performs reconciliation and exits with explicit success or failure. 5. What “reconciliation” actually means Reconciliation follows clear, idempotent semantics. Create / Update events Upsert A record if record exists and matches current IP → no‑op else → create or overwrite with new IP Upsert PTR record compute PTR name using IP octets and reverse zone alignment create or overwrite PTR to hostname.<forward-zone> Delete events delete the A record for the hostname scan PTR record sets: remove targets matching the hostname delete record set if empty All operations are safe to repeat. 6. Why IP drift tracking is separate DNS reconciliation enforces correctness at event time, but drift can still occur due to: manual DNS edits partial failures delete / recreate race conditions unexpected redeployments or restarts The drift tracker exists as a continuous correctness validator, not as a repair mechanism. This separation keeps responsibilities clear: Reconciler → fixes state Drift tracker → observes and reports state 7. Observability: correctness vs runtime health There is an important distinction: Runtime health container crashes image pull failures restarts platform events (visible in standard ACI / Container logs) DNS correctness A record != live IP missing PTR records stale reverse mappings The IP Drift Tracker provides this correctness layer, which complements — not replaces — runtime monitoring. 8. Engineering constraints that shape the design At‑least‑once delivery → idempotency Event Grid delivery must be treated as at‑least‑once. Every reconciliation action is safe to execute multiple times. Explicit failure behavior If the worker script returns a non‑zero exit code: the function invocation fails the failure is visible and alertable incorrect DNS does not silently persist
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) 😀Recovering our Default Azure Directory
Hello, everyone, relative newcomer to Azure here. I'm dealing with an inherited situation and, to add to the fun, I've just discovered my organization only has a Basic support plan, so no access to Azure technical support. I'm hoping some knowledgeable souls on here are in a charitable mood and will point me in the right direction. We're having problems getting to our DNS subscription because it's locked away behind an Azure directory to which we don't seem to have access, and I'm not quite sure this is completely an Azure problem. I was able to get into this directory around a year ago but I so seldomly access it that I'm not sure when this changed. We have two Azure directories. One is our "regular" directory, named for our organization, and it's linked (not sure of the terminology here) to our domain. Let's call it This.Domain.com. There are no subscriptions in this directory. The other is named "Default Directory" and it's linked to an onmicrosoft domain -- let's call it OldAdminThisDomain.onmicrosoft.com. When I try to switch to this directory I'm prompted to log in, then I'm hit with the MFA prompt. This is normally not a problem but it's like the MFA was set up for a different account with the same email address. By contrast, I can log into both the regular Azure directory and the 365 admin page with no problem -- I type in my email address (let's call it email address removed for privacy reasons), MFA comes up, and I have several authentication methods to choose from: Microsoft's MFA app, SMS, email, YubiKey, phone, etc., and all these options work. When trying to log into the Azure Default Directory, however, the MFA acknowledges only either the Microsoft Authenticator app or Use a Verification Code (which also goes through the Microsoft Authenticator app), and neither option yields any prompt on my phone. I seem to recall I effectively had two different "accounts" that somehow used the same email address but had different MFA setups, but again this was around a year and 3 phones ago so I don't have a solid memory of what was happening. I am also aware that, while this should not be permittable, there have been several cases where multiple Microsoft accounts were somehow created using the same email address. So this is where I am. Ideally we could merge the two Azure directories so that we combine the accessibility of the "regular" directory with the subscription(s?) that are in the Default Directory. Barring that I would have to somehow get the (suspected) two Microsoft accounts based on the email address removed for privacy reasons email address corrected. Any help would be greatly appreciated. Thanks to all in advance
Feature request: allow setting web client features from direct-launch-url
We use the "direct launch URL" feature of the AVD web client to deep link users to a session desktop (https://learn.microsoft.com/en-us/windows-app/direct-launch-urls?tabs=avd). One of the reason we use the web client is because we use AVD in exam halls on Chromebooks in kiosk-mode. The ChromeOS kiosk-mode only supports websites. Students are faced with a connection dialog in which they can toggle IME and Special Keys. The students have to enable IME, but since these are university-owned devices, they do not know and just click "Connect". We would like to be able to configure these client options automatically. For example, as query parameters in the direct-launch-url. Ideally, we would also skip the "Connect" dialog entirely and just go strait into the session once the direct-launch-url is loaded.Docker Engine v29 on Linux: Why data-root No Longer Prevents OS Disk Growth (and How to Fix It)
Scope Applies to Linux hosts only Does not apply to Windows or Docker Desktop Problem Summary After upgrading to Docker Engine v29 or reimaging Linux nodes with this version, you may observe unexpected growth on the OS disk, even when Docker is configured with a custom data-root pointing to a mounted data disk. This commonly affects cloud environments (VMSS, Azure Batch, self‑managed Linux VMs) where the OS disk is intentionally kept small and container data is expected to reside on a separate data disk. What Changed in Docker Engine v29 (Linux) Starting with Docker Engine 29.0, containerd’s image store becomes the default storage backend on fresh installations. Docker explicitly documents this behavior: “The containerd image store is the default storage backend for Docker Engine 29.0 and later on fresh installations.” Docker containerd image store documentation Key points on Linux: Docker now delegates image and snapshot storage to containerd containerd uses its own content store and snapshotters Docker’s traditional data-root setting no longer controls all container storage Docker Engine v29 was released on 11 November 2025, and this behavior is by design, not a regression. Where Disk Usage Goes on Linux Docker’s daemon documentation clarifies the split: Legacy storage (pre‑v29 or upgraded installs): All data under /var/lib/docker Docker Engine v29 (containerd image store enabled): Images & snapshots → /var/lib/containerd Other Docker data (volumes, configs, metadata) → /var/lib/docker Crucially: “The data-root option does not affect image and container data stored in /var/lib/containerd when using the containerd image store.” Docker daemon data directory documentation This explains why OS disk usage continues to grow even when data-root is set to a data disk. Why the Old Configuration Worked Before On earlier Docker versions, Docker fully managed image and snapshot storage. Configuring: { "data-root": "/mnt/docker-data" } Was sufficient to redirect all container storage off the OS disk. With Docker Engine v29: containerd owns image and snapshot storage data-root only affects Docker‑managed data OS disk growth after upgrades or reimages is expected behavior This aligns fully with Docker’s documented design changes. Linux Workaround: Redirect containerd Storage To restore the intended behavior on Linux, keeping both Docker and containerd storage on the mounted data disk, containerd’s storage path must also be redirected. A practical workaround is to relocate /var/lib/containerd using a symbolic link. Example (Linux) sudo systemctl stop docker.socket docker containerd || true; sudo mkdir -p /mnt/docker-data /mnt/containerd; sudo rm -rf /var/lib/containerd; sudo ln -s /mnt/containerd /var/lib/containerd; echo "{\"data-root\": \"/mnt/docker-data\"}" | sudo tee /etc/docker/daemon.json; sudo systemctl daemon-reload; sudo systemctl start containerd docker' What This Does Stops Docker and containerd Creates container storage directories on the mounted data disk Redirects /var/lib/containerd → /mnt/containerd Keeps Docker’s data-root at /mnt/docker-data Restarts services with a unified storage layout This workaround is effective because it explicitly accounts for containerd‑managed paths introduced in Docker Engine v29, restoring the behavior that existed prior to the change. Key Takeaways Docker Engine v29 introduces a fundamental storage architecture change on Linux data-root alone is no longer sufficient OS disk growth after upgrades or reimages is expected containerd storage must also be redirected The workaround aligns with Docker’s official documentation and design References Docker daemon data directory https://docs.docker.com/engine/daemon/ containerd image store (Docker Engine v29) https://docs.docker.com/engine/storage/containerd/ Docker Engine v29 release notes https://docs.docker.com/engine/release-notes/29/Azure for Students Subscription Renewal
Hello, I'm unable to renew my Azure for Students Subscription as I get the following error message: You are not eligible to renew Azure for Students Sign up for Azure for Students Starter. Unfortunately, I teach ASP.NET and Azure courses at my community college so this is a big problem for me. It appears that at some point my subscription was changed to a free trial and now I'm unable to renew Azure for Students. I have two expired Azure for Students subscriptions as well on my account. I've tried Azure support but they were unable to assist me. Any assistance would be greatly appreciated. Thanks, Joe
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