Browse Azure Blogs (19)

Pieter Vandenheede breaks down four practical BizTalk migration paths—do nothing (short-term), coexistence, domain-by-domain migration, and big-bang replacement—and provides a decision matrix to help teams choose an approach that fits their constraints and risk tolerance.

Pieter Vandenheede explains how to decide whether to keep BizTalk after Microsoft confirmed BizTalk Server 2020 is the final release, and what conditions make “staying” a deliberate (and limited) choice. He also outlines practical signals that it’s time to move and frames the shift from “if we migrate” to “how we migrate safely.”

Thomas Maurer explains how Adaptive Apps and the open-source Radius control plane help teams engineer, test, and prove application portability across Azure, Azure Local, and other Kubernetes environments by separating app intent from environment-specific implementations.

Pieter Vandenheede breaks down Microsoft’s BizTalk Server lifecycle update, what stays supported through 2028/2030, and why “support isn’t strategy” for long-term integration platforms. He outlines practical next steps: inventorying your integration landscape, planning timelines, and evaluating Azure Integration Services (including Logic Apps) as the direction Microsoft is investing in.

Thomas Maurer introduces the new digital sovereignty adoption guidance in the Microsoft Cloud Adoption Framework, explaining the sovereign cloud continuum and a practical three-phase path (planning, architecture/governance, and operations) for building and running controlled Azure workloads across public, private, and partner cloud models.

Thomas Maurer shares takeaways from a discussion at the HPE Customer Innovation Center on Azure Local and Microsoft’s Sovereign Private Cloud vision, focusing on how organizations can meet data residency, compliance, and operational control requirements while still using Azure-consistent capabilities in hybrid and edge environments.

John Edward lays out a practical checklist for taking an AI agent from demo to production using Microsoft Copilot and Azure AI Foundry, focusing on architecture choices, grounding with enterprise data, controlled tool permissions, evaluation, monitoring, and cost/latency controls.

John Naguib lays out a practical architecture for building self-improving AI agents on Azure, focusing on controlled feedback loops (evaluate, learn, test, approve) rather than uncontrolled self-modification. The guide covers agent runtime, tool calling, RAG, memory/experience stores, evaluation, observability, and governance for production use.

John Edward explains how to apply FinOps practices to high-volume Azure AI workloads, focusing on the real cost drivers behind model usage, compute, and request patterns. The article lays out practical tactics—visibility, right-sizing, scaling, batching, caching, and guardrails—to keep AI spend predictable without sacrificing latency or reliability.

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.