Browse Azure Blogs (20)

John Edward explains how to build an AI-powered IT support agent on Azure, using Azure AI Foundry with Azure OpenAI Service and (optionally) Azure AI Search for RAG-based answers grounded in internal documentation, plus ideas for automating common help desk actions like ticket creation and password resets.

John Edward outlines practical Azure architecture best practices for enterprise applications, covering the Azure Well-Architected Framework, scalability and high availability patterns, security with Zero Trust, observability, infrastructure as code, CI/CD, cost controls, networking, disaster recovery, and governance.

Thomas Maurer explains what AKS on bare metal is and where it fits for on-premises, edge, and sovereign deployments, focusing on how it keeps the AKS experience while running directly on physical hardware. He also outlines a deployment path using Azure Local SFF with Arc-based management.

John Edward explains how Azure’s “Agentic Agents” can support resilient cloud operations across migration planning, observability, and continuous optimization. The article focuses on turning telemetry into actionable guidance, reducing alert fatigue, improving root-cause analysis, and driving cost, performance, security, and sustainability improvements in Azure environments.

John Edward explains what an Agent Optimizer is in Azure AI Foundry Agent Service and why it matters for building reliable AI agents. The article breaks down how optimization works in practice—evaluating performance, refining instructions, improving workflows, and learning from feedback—to increase accuracy, efficiency, and user satisfaction.

Thomas Maurer explains what Azure Local Small Form Factor (SFF) is and why it matters for edge scenarios, then outlines an end-to-end deployment flow: provisioning a device as an Azure resource, installing the Azure Local OS, registering it with Azure Arc, and running container workloads with Docker and K3s.

John Edward explains what “agentic AI” means in the Microsoft ecosystem, focusing on how goal-driven agents plan tasks, call tools, and maintain memory. The article maps those concepts to Azure AI Foundry, Semantic Kernel, and Microsoft Graph, with concrete enterprise workflow examples.

Tim D'haeyer explains how to replace BizTalk-style code-table mapping during migrations by using an Azure Function that enriches XML documents via XPath-driven rules and SQL lookups, keeping Azure Logic Apps focused on orchestration instead of complex transformation logic.

Thomas Maurer explains Azure Local Simplified Machine Provisioning, a new workflow for provisioning physical Azure Local nodes with minimal on-site work while keeping configuration and control centralized in Azure.

John Edward explains how Retrieval-Augmented Generation (RAG) works and how Azure AI Search fits into a production-ready RAG architecture, covering indexing, semantic and vector search, embeddings, chunking strategies, and practical steps to build a basic retrieval + generation workflow.

Hidde de Smet shows how to add fast local guardrails for Azure Terraform by running fmt, validate, tflint, Trivy, and terraform-docs on every git commit. The post includes a working pre-commit config, Azure-specific lint rules, and an MCP-based workflow to keep generated HCL current and policy-aligned.

John Edward explains how Microsoft Fabric OneLake in Azure acts as a single, organization-wide data lake and why it matters for modern enterprise analytics architecture, including reducing data silos, supporting lakehouse patterns, and improving governance and AI readiness.

Thomas Maurer explains how Azure Local multi-rack deployments extend Azure-consistent management from small clusters to datacenter-scale footprints, including the core architecture (SAN-backed disaggregated design), minimum rack layout, and how operations are handled through familiar Azure tooling like the portal, CLI, and ARM/Bicep.

Thomas Maurer explains how LAPS for Azure Arc extends Windows LAPS so teams can centrally audit and enforce local admin password rotation across Azure VMs and Arc-enabled servers, with Azure Policy-based compliance reporting that works in hybrid and regulated environments.

DevClass rounds up Microsoft Build announcements that matter to developers, including new Windows sandboxing for AI agents (MXC), an Arm-based Surface RTX Spark Dev Box, GitHub Enterprise Local for connected or air-gapped environments, Azure Linux updates, and Microsoft-maintained Coreutils for Windows.

DevClass reports on .NET Aspire 13.4, highlighting the general availability of the TypeScript AppHost and new integrations that broaden Aspire beyond C#-only workflows. The piece also covers deployment targets (including Azure and Kubernetes), the Aspire dashboard’s OpenTelemetry-based observability, and notable Kubernetes-related improvements.

John Edward outlines common enterprise AI agent architecture patterns you can implement with Microsoft Copilot Studio, including single-agent designs, multi-agent orchestration, RAG, human-in-the-loop workflows, and event-driven automation, with notes on integrations, governance, and compliance considerations.

Thomas Maurer shares a conversation with Geoff Ross (Cireson) on using Tikit to bring IT service management practices to Azure operations, with a focus on self-service deployments, standardized service delivery, and governance baked into approval workflows.

John Edward outlines an architecture for a “Daily Stand-Up Agent”: a custom AI copilot that pulls sprint activity from Jira and Azure DevOps, detects blockers, and generates consistent stand-up summaries. The post focuses on connectors, grounding ticket data, conversational reporting, and practical considerations like security and data quality.

Rob Bos shares an overview of his open source projects spanning GitHub and CI/CD tooling, Azure-backed services, security reporting, and local-first AI utilities, with links to each repo and a clear description of what each tool does.