Browse All Azure Content (753)

Johnson Shi, Zoey (Zhuyu) Li, and Huangli Wu announce public preview support for regional endpoints in Azure Container Registry geo-replication, including the new Azure CLI and portal experience, endpoint URL formats, and practical guidance for pinning pushes/pulls and Kubernetes workloads to specific replicas.

shijain13 explains what’s new in the Azure Monitor Health Model (Preview), focusing on expanded discovery options, faster health signal setup, and new aggregation rules that help teams reason about workload health with less alert noise and clearer troubleshooting paths.

Sam Foo explains how Pod CIDR expansion works for Azure CNI Overlay in Azure Kubernetes Service (AKS), and what to consider when planning pod IP ranges for long-lived clusters as they scale.

davidwright, Arnaud Lheureux, and Suzanne Daniels explain why architecture and governance frameworks only help when they actively change delivery decisions. Using Git-Ape as the example, they show how to turn Azure Well-Architected, Azure Policy (including NIST mappings), and CAF guidance into repeatable repo-driven assessments with prioritized findings tied to code and policy.

Mark Russinovich and Ion Stoica discuss how distributed-systems principles are shaping next-generation AI platforms, covering what changes as workloads become agentic, multimodal, and globally distributed, and why open source, security, and governance are now core requirements from training through real-time serving.

Sameer Nori, Pranay Bakre, and Govardhani Babu show how to run and scale LLM inference for agentic, cloud-native apps on Azure using Arm-based Azure Cobalt VMs, including an AKS demo and practical guidance on performance, scaling, and cost trade-offs.

kinfey explains how to run LLM agents that write and execute code without giving them a host-sized blast radius, using a MicroVM sandbox. The post walks through a real pipeline (a daily Mandarin World Cup podcast) built with Microsoft Agent Framework, Azure AI Foundry, and Hyperlight snapshot/restore isolation.

Scott Guthrie shares a fast-paced builder’s view of what matters for developers in the current AI wave, covering how Microsoft is scaling Azure infrastructure and what “AI-ready” systems look like from silicon to software.

Mike Richter shows how Elastic Agent Builder (Elasticsearch 9.4) can help AI agents manage long-running context by using a conversation context store, selective compaction, and dynamically loaded skills, with an emphasis on deploying these patterns in the Microsoft ecosystem on Azure and with Azure AI Foundry models.

Edo Segal demonstrates how to build multimodal AI agents with persistent memory, including a live walkthrough of provisioning Napster as an Azure resource and integrating the agent securely with Azure AI Foundry.

Caroline Matthews demonstrates how long-running coding agents can be built and guided in Azure AI Foundry using Claude Code and Cowork, focusing on practical patterns like evaluation-first workflows, safe autonomous execution, and multi-agent loops that can plan, test, and recover across larger codebases.

Dom Robinson, samkemp, and Inbal Sagiv announce Foundry Local 1.2.0 and preview Foundry Local on Azure Local, focusing on running AI on-device and at the edge with better transcription, broader hardware support, improved cancellation, and simpler acceleration across Windows and Linux.

Srikumar Nair demonstrates how to build context-aware Azure AI Foundry agents by connecting them to Work IQ via MCP, then governing them with Agent 365. The session shows tool invocation at runtime, agent identity, end-to-end observability, and side-by-side evaluations comparing grounded vs. context-blind agents.

Rishabh Saha shares how Microsoft and PepsiCo engineers modernized PepsiCo’s data foundation for agentic applications, using Azure SQL, Cosmos DB, PostgreSQL, and Azure Databricks. The session outlines a practical build path for agentic RAG, including Azure SQL vector indexing and semantic search to speed up repeatable app patterns.

Shawn Henry, Amanda Foster, and Glenn Condron go deep on building and operating multi-agent systems on Microsoft Foundry, focusing on “agent harness” patterns (including Claw) and hosted agents architecture. They cover long-running agents with triggers, state and file access, plus how Copilot SDK and Claude Agent SDK fit into coordinated workflows.

Rochak Mittal, Shobhit Garg, and Adity Agarwal present a Build 2026 breakout on treating resiliency as an agent-first practice, showing how an agentic AI assistant can connect build, operations, troubleshooting, and recovery workflows across repos, dashboards, runbooks, and collaboration tools for Azure workloads.

Dan Hellem and Dave Burnison demonstrate how Azure DevOps and GitHub integrate for hybrid DevOps workflows, including Azure Boards and Azure Pipelines connectivity, migration tooling, and AI-powered capabilities like Copilot assignment, Copilot code reviews, and automated multi-file fixes.

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.

Karthik Vijayan, Colin Helms, imran Sheik Mohamed, and Jayneel Vora show how agentic AI systems can span client, edge, and cloud, with demos covering on-device reasoning, distributed inference, and enterprise-scale multi-agent orchestration on Azure Kubernetes Service.

Mark Russinovich tours recent Azure platform innovations, focusing on performance, networking resilience, container live migration, and confidential computing. The session highlights how these building blocks support modern applications across cloud, on‑premises, and edge environments, with demos of Azure Container Instances and Azure Integrated HSM.

Parthasarathy Srinivasan and Rajya Laxmi Yellajosyula demonstrate a multi-cloud, enterprise AI workflow that combines Oracle Database@Azure with Microsoft Fabric, MCP, and GitHub Copilot, covering provisioning, synthetic data creation, ETL from bronze to gold, and an end-to-end fraud detection demo driven by natural-language orchestration.

Pablo Castro presents a Microsoft Build 2026 deep dive into Foundry IQ, Microsoft’s context engineering platform for building agents that can retrieve enterprise knowledge using agentic RAG. The session covers Foundry IQ’s architecture, connecting new knowledge sources, ingestion pipeline customization, retrieval APIs, and performance/evaluation improvements.

Vini Soto and Jan Kalis demonstrate an “agentic content factory” built from multiple agent frameworks, deployed to Azure Container Apps, and wired up with Azure AI Foundry for observability and evaluations, with a focus on secure sandbox execution and controlling outbound access.

Vaidyaraman Sambasivam, Osi Otugo, and Jean Boudier demonstrate an end-to-end flow for taking Hugging Face open-source models from discovery to production inference using Foundry Managed Compute in Azure AI Foundry, focusing on scaling, governance, and avoiding direct GPU management.

William Liang demonstrates how teams use Azure AI Foundry to distill large models into smaller, task-focused language models using supervised fine-tuning, with an emphasis on reducing production latency and cost while maintaining accuracy through structured evaluation.

Vivek Chauhan explains how to move from generic foundation models to production-ready, use case-specific AI by combining Fireworks AI training/inference capabilities with Microsoft (Azure) AI Foundry, focusing on practical patterns to reduce cost and latency and deploy at scale.

Katarina Stanley and Daniel Arrizza explain how Anyscale on Azure uses Ray on Azure Kubernetes Service (AKS) to run distributed AI workloads, from multimodal data pipelines and training/fine-tuning through to deploying models as inference services inside an Azure subscription.

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.

leoyao summarizes the //build 2026 updates to Foundry Toolkit for VS Code, focusing on an end-to-end Hosted Agent workflow (scaffold, run, deploy, observe), richer Toolbox integrations, and new LangGraph samples that cover MCP, human-in-the-loop flows, and production observability.

Sebastian Kohlmeier outlines what’s new in Microsoft Foundry observability at Build 2026, focusing on production-grade tracing, evaluations, and optimization for AI agents across multiple frameworks. The post also introduces ROI tracking for agents, tying operational signals to business value via the Foundry portal and APIs.

Elijah Straight shows how Microsoft IQ can be used to unify enterprise intelligence, starting with a long-running agent demonstrated at Microsoft Build 2026.

Tanaya Yadav demonstrates Frontier Tuning from Microsoft Build 2026, showing how teams can create enterprise AI by tuning models on their own data and workflows, with an emphasis on building organization-specific behavior rather than relying only on generic, off-the-shelf models.

Amanda Foster demonstrates how to integrate AI agents into business workflows and govern them at scale using Microsoft Foundry, as presented at Microsoft Build 2026.

Ram Kakani explains how Oracle Managed Database MCP (Model Context Protocol) remote servers can be used from Microsoft Foundry to build enterprise AI agents that query Oracle AI Database@Azure, including local VS Code workflows, self-hosted Azure deployments, and a fully managed OCI option with identity, networking, and governance controls.

LZhang lays out a practical DevOps loop for Microsoft Foundry Hosted Agents, covering how to move from Terraform-provisioned infrastructure to production delivery with immutable agent versions, evaluation as a release gate, manifest-driven promotion, traffic-split canaries, and per-version observability.

Learn Microsoft AI covers Microsoft Build 2026 announcements for MAI models in Azure AI Foundry, spanning text, image, voice, and speech modalities, and explains how these first-party models are positioned to help developers build more capable AI applications.

mmcrey announces Confidential Live Migration for Intel TDX Confidential VMs in Azure, explaining how Azure can move a running confidential VM to updated infrastructure with limited interruption while protecting VM memory and execution context through attestation, policy checks, and encrypted state transfer.

Connected-Seth outlines new Azure Event Grid Namespace capabilities for IoT and event-driven systems, including MQTT v5 Subscription Identifiers (GA), larger 1MB event payloads (GA, coming soon), and autoscale up/down (preview, coming soon), plus a GA integration for routing Stripe events into Azure services.

Ivan Varnitski announces a public preview feature for Azure Monitor Data Collection Rules that lets you run multi-stage transformations (processors) to filter, aggregate, parse, and reshape logs before they’re ingested into Log Analytics, cutting ingestion volume and cost while improving query-ready data quality.

susaraswat4 shares performance and sizing guidance for Azure Monitor pipeline, including measured Syslog/CEF ingestion throughput into Log Analytics, memory footprint, and how throughput scales with vCPUs and replicas. It also highlights operational behaviors like automatic core usage and TCP backpressure as a signal to scale.