Browse DevOps Community (123)

dchelupati explains how to scale Azure SRE Agent from a single team to enterprise-wide use, focusing on access control, guardrails, auditing, multi-agent topologies, and “agent as code” practices for repeatable deployment and governance.

dchelupati shares a practical “start small” adoption guide for Azure SRE Agent, focused on picking a first operational use case, connecting only the required context (telemetry, repos, runbooks), and expanding into repeatable workflows with the right triggers and permissions.

paulyuk shows how the Azure SRE Agent canvas fits into GitHub Copilot (App and Copilot CLI) so on-call engineers can validate an agent’s incident diagnosis with their own telemetry, queries, and access controls, then share a reviewed update back to the team.

vinilv shows how to build a small, two-node kubeadm Kubernetes cluster on Azure using an N-series GPU VM, then validate GPU scheduling with the NVIDIA device plugin and a PyTorch CUDA test Job. The guide focuses on a transparent dev sandbox for debugging GPU workloads rather than a production-ready design.

Jack Tracey explains that Azure Verified Modules (AVM) has moved from a community-led effort into Azure engineering, with dedicated engineers maintaining the specs, framework, and selected modules. The post outlines changes to AVM’s support statement and introduces a tiered module ownership model planned for early 2027.

Wuyi Weng shows how to run Qwen3.8-Flash-Next across two H100 GPUs on an Azure Spot VM, then instrument and troubleshoot it with OpenTelemetry, Application Insights/Azure Monitor, and Grafana. The post focuses on KV-cache behavior under agentic load, dashboard pitfalls, and a vLLM KV-cache FP8 setting that improves concurrency and time-to-first-token.

GisliGudmundsson shares a reference pattern for running Azure AI Foundry workloads inside a secure Azure landing zone, using private endpoints and private DNS zones, disabling local auth in favor of managed identity, and placing Azure API Management in front to enforce token limits and emit token usage metrics.

Rama Bhimanadhuni explains how Microsoft is working with the Open Compute Project (OCP) and major hardware vendors to standardize reliability and manageability for large, multi-vendor AI clusters, focusing on RAS signals, diagnostics, telemetry, and safer firmware update workflows to keep fleet capacity and availability high.

Wuyi Weng shares a practical, numbers-driven guide to self-hosting the Qwen3.8-27B model on an Azure H100 Spot VM, including the exact vLLM serving flags, a Docker Compose deployment, and an end-to-end observability setup with OpenTelemetry, Application Insights, and Grafana to decide when Spot hosting breaks even.

Brittany Pugh lays out a production-minded reference architecture for document-heavy intake on Azure, showing how to put AI behind a governed API gateway, process work asynchronously, and keep an end-to-end audit trail with a single correlation ID across API Management, Service Bus, Functions, and Event Grid.

Alex-wdy explains how Azure CLI 2.91.0 adds preview support for native broker-based authentication on macOS, improving enterprise sign-in security and SSO. The post also covers the Homebrew Formula-to-Cask packaging change and provides step-by-step commands to enable, verify, and roll back the new sign-in flow.

torreyt explains how noise reduction in Azure Deployment Stacks What-If makes change previews more trustworthy by filtering out false diffs caused by provider defaults and read-only/write-only properties, and shows how to run Stacks What-If via Azure CLI and Azure PowerShell.

tejabhutada explains practical ways to sync Jira Product Discovery ideas with Azure DevOps Epics, so approved roadmap items flow to engineering without manual re-entry and delivery status flows back to product. It breaks down what the Jira API can and can’t sync, plus the field, user, and state-mapping work that usually trips teams up.

Wiliam_Rosa explains Azure Databricks Lakeflow Jobs “Multiple Triggers” (beta), showing how to run a single job from several conditions (schedule, file arrival, table/model updates) and how to configure, monitor, and reason about concurrency and idempotency when triggers overlap.

bobmital announces the general availability of Anyscale on Azure, a managed Ray runtime that runs on Azure Kubernetes Service in the customer’s subscription. The post explains the control-plane/data-plane split, Azure-native identity and governance integration, private connectivity options, and the kinds of AI/ML workloads teams can run end-to-end.

jalenmcg announces a public preview for onboarding Amazon EKS and Google GKE clusters into Azure Arc using the Azure Arc Multicloud Connector, focusing on automatic discovery, agent installation, and ongoing sync so multicloud Kubernetes can be governed and monitored from Azure.

Poornima99 announces the general availability of the GitOps with Argo CD extension for Azure Kubernetes Service (AKS), highlighting Microsoft-managed lifecycle, Entra ID SSO, workload identity federation for ACR/Azure DevOps, hardened Azure Linux images, and Azure Managed Prometheus metrics for observability.

shyambasnet shows how to set up proactive monitoring for Azure VPN Gateway Site-to-Site tunnels by using diagnostic logs and an Azure Monitor alert. The post includes the exact KQL query and alert rule settings needed to notify admins as soon as a tunnel enters a Disconnected state.

Alex-wdy explains how the Azure AD Graph (graph.windows.net) retirement impacts older Azure CLI and Azure PowerShell versions, and what to check in developer machines, CI/CD agents, runbooks, and container images to avoid identity automation breaking during the phased shutdown after October 6, 2026.

kinfey breaks down what changes when an AI agent can run tools and create side effects, then shows how to put real execution boundaries around those tools using OpenSandbox on AKS with Kata pod VMs, plus a comparison to Azure Container Apps Sandboxes and Dynamic Sessions.

Rebekah Midkiff lays out a reference architecture for governing AI agents across multiple Microsoft Entra tenants, combining Agent 365, Entra Agent ID, Microsoft Foundry, Copilot Studio, Purview, and an Azure API Management AI gateway to enforce identity, policy, tool access, and observability at runtime without collapsing tenant boundaries.

Wiliam_Rosa breaks down Lakeflow in Azure Databricks, explaining how it unifies ingestion, transformation, and orchestration, and why Unity Catalog-based governance and lineage can reduce the “glue work” of multi-tool data engineering stacks.

paulyuk introduces the Azure Functions Hosted Skills canvas inside the GitHub Copilot app, showing how to build and test an event-driven Function App locally, invoke triggers on demand, and debug runs with logs and evidence links. The post also covers deployment to Azure with azd and exposing skills as MCP tools.

DanaCozmei announces a Microsoft and IBM integration that lets IBM Spectrum Symphony orchestrate large-scale HPC and batch workloads on Azure using Azure Compute Fleet, including elastic bursting, policy-driven scaling, and cost/availability choices across VM families and Spot vs pay-as-you-go capacity.

Mason Torres explains how Hotpatch works for Azure Arc-enabled Windows Server 2025 to reduce restart-driven maintenance windows while still keeping servers current with security updates. The post covers eligibility prerequisites (VBS/VSM, Secure Boot, Hyper-V Gen2), enrollment steps in the Azure portal, and how to manage updates after enrollment.

Dalibor Kovacevic explains how Azure SRE Agent plugin marketplaces can now be backed by Azure DevOps Repos, so teams can keep runbooks and tooling in their existing Azure DevOps Git repositories instead of mirroring to GitHub, using managed identity or PAT-based access.

emiller635 compares three practical ways to evaluate and observe AI agents: self-hosted Langfuse on an Azure VM, a Microsoft Foundry hosted agent, and a standalone agent on Azure Container Apps. The post breaks down what each option gives you for tracing, scoring, and dashboards, and what you still have to build and operate yourself.

Wiliam_Rosa breaks down a practical rollout pattern for testing newly released “frontier” AI models without letting costs spike: give immediate experimental access, but isolate spend risk using layered budgets in Azure Databricks Unity AI Gateway, then decide promotion or rollback using benchmarks, user feedback, and OpenTelemetry cost traces.

Wiliam_Rosa explains how Azure Databricks’ Unity Gateway CLI (ug) centralizes authentication, routing, governance, and usage tracking for multiple coding agents (Claude Code, Codex, Gemini, and GitHub Copilot CLI) by reusing Unity Catalog permissions, rate limits, and budgets instead of scattering provider keys across developer machines.

stevenbucher announces the Azure Policy Linter as an open-source CLI and .NET library, aimed at catching common Azure Policy definition mistakes before policies are assigned. The post explains how to install and run it, how rule sets and JSON output work, and highlights several high-impact rules from the catalog.

puneetguptams shares practical configuration guidance for Azure SRE Agent, based on patterns observed in production telemetry. The post focuses on connecting the right data sources, routing incidents to the right specialist via response plans, writing useful skills, deciding when to require approvals, and ensuring the agent has the permissions needed to complete the last step.

jaredfholgate introduces an experimental Bicep CLI feature, `bicep docs generate`, which creates and keeps module documentation in sync with your Bicep code. The post shows how the generator discovers parameters, outputs, types, and examples, how to customize output with Scriban templates, and how to enforce up-to-date docs in CI.

RajyaLaxmiYellajosyula explains how Oracle GoldenGate is now managed as a native Azure experience within Oracle AI Database@Azure, including Azure portal/API lifecycle operations, Azure control-plane governance (RBAC, tags, Activity Log), and generally available observability via Azure Monitor metrics and Azure Log Analytics events.

Karl-WE curates a practical hub of tooling and reference links for teams evaluating or operating Azure Local, covering community GitHub projects, Microsoft-supported utilities for sizing and troubleshooting, and OEM documentation from Dell and HPE for certified hardware deployments.

Jan-Kalis announces the general availability of Azure Container Apps Sandboxes and explains how they provide per-task, hardware-isolated microVMs for running untrusted code and agent workloads. The post covers network egress controls, VNet/private endpoint connectivity, snapshots and lifecycle policies, persistent volumes, and telemetry export options.

Lee Stott walks through Fibey Field Ops, a synthetic field-service assistant built with Microsoft Foundry Hosted Agents, MCP, and Azure Container Apps, focusing on the practical engineering work around tool contracts, identity boundaries, deployment sequencing, and debugging/observability rather than just model choice.

Wiliam_Rosa explains how Azure Databricks (Databricks Runtime 18) lets you change the partition count of a stateful Spark Structured Streaming query after it’s already in production, without abandoning the checkpoint. The post covers how the repartition works, how to apply it, and what to measure to confirm the impact.

Nate_Waters summarizes a wave of Azure Kubernetes Service (AKS) releases aimed at running AI workloads more safely and efficiently, covering isolation features (pod sandboxing, confidential GPU), agent-focused platform components (GitOps, burst scaling, GPU profiling), and capacity improvements for large-scale clusters and hybrid/edge deployments.

Trinidad Salazar explains how Microsoft and Dapple are integrating topology-aware, purpose-built GPU clusters into an Azure-native operating model, using AKS as the control plane so teams can run large-scale training, fine-tuning, and inference while keeping familiar governance and operational workflows.

kinfey explains how Azure KARS (a Kubernetes reference stack) can run “coding-agent” CLIs as governed workloads, focusing on blast-radius control, credential isolation, and consistent policy enforcement across multiple agent runtimes like Copilot CLI, Claude Code, and Codex.