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shashankamalladi outlines updated scale limits for Azure Network Security Perimeter, including new hard limits for perimeters, profiles, rule elements, and associated PaaS resources, plus what changes apply to existing configurations through the 10/31/26 transition window.

nelsontam introduces Microsoft’s MARS (Map Autoregressive) model on Microsoft Foundry and explains how it converts satellite imagery into GIS-ready vector map data. The post outlines an end-to-end workflow with Microsoft Planetary Computer Pro, including STAC/COG ingestion requirements and practical deployment settings for running geospatial feature extraction at scale.

TomClaes explains how to govern Azure Log Analytics retention across many workspaces by keeping workspace defaults low, setting per-table interactive retention, and using long-term retention only where needed. The post includes an Azure Policy-based, DeployIfNotExists solution with auditing, drift detection, and automated remediation.

junjieli summarizes the July 2026 releases (1.6.3–1.6.6) of the Foundry Toolkit for VS Code, focusing on a flatter, tabbed workspace, inline tool wiring, deeper agent debugging via event-level inspection, and a new Agent Optimization preview that supports measured compare-and-deploy tuning for hosted agents.

HimanshuYadav explains how to transfer a live GitLab project to a new group without accidentally “resetting” Terraform state or breaking pipelines, focusing on what changes during a namespace move (variables, runners, identity mappings) and how to validate and roll back safely.

Zoran Jovanovic breaks down the main regional distribution patterns for Azure workloads—single region, failover, parallel (active-active), segmented, and portable—framing each as a deliberate reliability and risk decision with clear trade-offs around cost, complexity, RTO/RPO, and service capabilities.

KishoreKumarPattabiraman explains how to choose between building an interactive “skill” and an autonomous “sub-agent” when designing reusable AI capabilities, using practical checks around iteration style, voice fidelity, review gates, risk blast radius, and frequency of reuse.

Abhishek Tiwari explains how Azure Front Door edge actions adds customer-defined logic in the request path while keeping hyperscale performance predictable, enforcing strong tenant isolation with Hyperlight micro-VMs, and building resiliency through bounded latency, fast-fail behavior, and validated fallback mechanisms.

kyleikeda explains an upcoming Azure Cost Management policy change: starting Feb. 1, 2027, reservation exchanges will no longer be available for services that are covered by Azure savings plans. The post lists affected services, clarifies what stays the same, and outlines practical steps to plan ahead.

Chris Noring shows how to build an AI shopping assistant that remembers customer preferences across sessions by plugging a SQL Server-backed history provider into Microsoft Agent Framework, then explains how the same setup can move from local Docker to Azure SQL Database (including Hyperscale) with only a connection-string change.

demiajayi introduces new Cost Management and Pricing toolsets for the Azure Resource Manager MCP server, enabling AI agents (including GitHub Copilot clients) to query costs, forecast spend, manage budgets and alerts, retrieve pricing, and analyze AKS costs directly through Azure’s control plane.

hetvip introduces a community template collection that lets teams keep their existing workflow orchestrator (like Airflow, Temporal, or Argo) while using Azure Container Apps Jobs as the execution layer for containerized tasks, including guidance on identity, RBAC, retries, and fan-out patterns.

gauravbhardwaj explains how GitHub Copilot’s pooled included credits and metered overages work, and how to use user-level budgets and enterprise budgets to cap spend without unexpectedly blocking developers.

Ilana Waitser announces a public preview that lets you share Azure Monitor Logs from a Log Analytics workspace into Microsoft Fabric’s OneLake as Delta Parquet, without duplicating data. The post explains what this enables in Fabric—Power BI reporting, Spark-based analytics and ML, and correlating telemetry with business data for near real-time decisions.

stevenjma announces the general availability of Terraform AzureRM provider 5.0 and explains the key breaking changes, including new defaults for Resource Provider registration, opt-in Azure preflight validation during terraform plan, and removal of deprecated resources and properties.

Aurnov Chattopadhyay recaps the June 2026 Azure Arc Server Forum, including updates on the Arc Server AI Agent, Azure Arc Multicloud Connector previews (GCP connector and EKS cluster enablement), and ESU guidance for Windows Server 2016/2012 and SQL Server 2016 end-of-support timelines.

Lee Stott walks through an end-to-end, production-minded approach for taking a multi-agent system from local development into Microsoft Foundry Agent Service, then distributing it to users in Microsoft Teams. The guide focuses on identity, governance, observability, and repeatable deployment using the FibreOps reference implementation.

pallakatos announces GA support for built-in CIS Benchmark auditing for Linux workloads using Azure Policy with Machine Configuration, including Azure Arc-enabled servers. The post outlines what’s included in GA, which Linux distributions and CIS benchmark versions are supported, and the basic steps to enable continuous compliance reporting in Azure.

Rafia Aqil outlines how to enable Azure Databricks’ Compliance Security Profile (CSP) for HIPAA workloads, including the September 1, 2026 deadline, required prerequisites like Azure VNet encryption and supported VM series, and a rollout approach to validate cluster startup and end-to-end connectivity before production.

stevenbucher introduces a new Compliance Substate property for Azure Policy exemptions, making it possible to see whether exempt resources would be compliant or non-compliant if the exemption were removed. The post shows where to enable the column in the Azure portal and how to query the field at scale using Azure Resource Graph.

Chris Noring explains how to investigate unexpected GitHub Copilot Enterprise AI-credit spend and then control it using GitHub Billing guardrails. The post walks through identifying the SKU driving costs, attributing usage to organizations and cost centers, and applying budgets, alerts, and per-user limits without breaking productive workflows.

prateekwrites explains why “cost per token” is a misleading metric for agentic systems, and proposes “cost per accepted task” as the unit that matters. The post maps this idea to an Azure reference architecture using APIM’s GenAI gateway, Azure AI Foundry evaluations, and a policy loop driven by telemetry and alerts.

Nora Zhan introduces “Physical-World Intelligence” and explains how GeoAI combines geospatial data (weather, satellite imagery, sensors, and maps) with enterprise context and AI. The article outlines Microsoft Foundry’s geospatial model catalog, Planetary Computer Pro as a GeoAI data plane, and a GeoAI SDK for production-scale inference workflows.

tonimontez explains how to govern GitHub Copilot’s usage-based spend using GitHub’s native budget controls first, then adds an Azure API Management gateway in front of an Azure AI Foundry (Azure OpenAI) deployment for real-time, token-granular quotas and per-developer telemetry.

vladvino announces the public preview of the AI Gateway tier for Azure API Management, focused on publishing and governing AI models and MCP servers. The post explains the new portal experience, policy-card governance (rate limits, quotas, Content Safety, fallback), and OpenTelemetry token metrics to destinations like Application Insights.

Kumar Kaushal demonstrates a transitional Azure networking pattern for phased migrations where an application is hard-coded to a legacy private IP. The lab uses Private Link Service Direct Connect plus a Private Endpoint and a site-to-site VPN so Azure-hosted tiers can keep calling the same on-premises address during the interim phase.

Lee Stott explains why the Model Context Protocol (MCP) is becoming the standard way AI agents connect to tools and data, then shows minimal runnable MCP server examples in Python and TypeScript plus practical guidance for VS Code hosting, security, and production operations.

Naba Kumar and the MLVC team announce the open-source release of MLVC, a learned video codec that replaces traditional codec primitives with end-to-end neural compression. The post shares bitrate savings versus H.264/H.265, real-time performance targets on commodity NPUs, and what’s included in the GitHub release (models, weights, training scripts, and conversion tooling).

carlottacaste shares a short walkthrough of the Foundry Toolkit for Visual Studio Code and a companion VS Code Learn course, focused on keeping model selection, prompt iteration, and agent development inside VS Code, with links to a full video playlist and a featured setup episode.

AbodeSaafan explains how the Azure Logic Apps team migrated roughly 60,000 per-customer Azure Functions apps from the end-of-life v1/v2 runtime to Functions v4 (isolated worker) under live load, using shadow traffic, strict parity checks, and a progressive rollout with fast rollback and careful retirement of the old fleet.

stevenbucher explains how Azure Policy for Kubernetes can now enforce policies using Kubernetes Validating Admission Policy (VAP) with CEL, via Gatekeeper’s integration. The post contrasts the older webhook/Rego flow with in-process validation, then walks through packaging a CEL constraint template into an Azure Policy definition and rolling it out across AKS clusters.

OfirSarfaty announces a public preview feature for Azure DDoS Protection: custom policies that let teams set protocol-specific detection thresholds and manage per-resource mitigation behavior, while keeping existing Azure Monitor visibility and DDoS telemetry.

junjieli introduces the Foundry Agent Canvas (public preview), a GitHub Copilot App extension that lets developers discover resources, scaffold and configure a Foundry hosted agent, test it locally with an embedded Agent Inspector, and deploy it to Foundry Agent Service using azd-driven workflows.

anandgmenon walks through running Azure Logic Apps Hybrid on a self-managed RKE2 Kubernetes cluster, including how to provide an ingress IP with MetalLB, connect the cluster to Azure Arc, install the Container Apps extension, and fix the RKE2-specific DNS and inotify issues that can break deployments.

brauerblogs shares a reminder to register for Microsoft’s “Path to Production for Agents” webinar series (July 27–28), focused on taking AI agent solutions from experimentation to secure, scalable production with guidance on governance, platform design, AgentOps, and multi-agent architecture patterns.

saniya0307 explains how Azure Local adds iSCSI SAN integration alongside Fibre Channel, enabling hybrid (S2D + SAN) and SAN-only disaggregated deployments so organizations can reuse existing storage arrays, scale compute and storage independently, and modernize virtualization infrastructure with Azure-consistent management.

Kalaivanan explains how to use Azure API Management (APIM) as a control plane for Model Context Protocol (MCP) servers, focusing on enterprise-ready authentication, access control, observability, and governance. The post lays out practical patterns for putting APIM in front of MCP endpoints and using Entra ID, OAuth flows, and API Center for discovery.

Ricardo Duncan describes how CDK Global migrated a business-critical automotive CRM platform to Azure SQL Managed Instance, including the architecture choices, migration approach, and operational practices used to move more than 1,000 databases with minimal downtime.

richpaw describes a reference architecture for running Microsoft Discovery on a Windows VM in Azure and connecting it to an Azure CycleCloud HPC cluster via Azure NetApp Files (NFS) and SSH, so agentic workflows can submit Slurm jobs, read/write POSIX files, and stay inside a private network boundary.

richpaw explains how Microsoft Discovery combines agentic AI with Azure HPC to speed up semiconductor EDA workflows, focusing on closed-loop orchestration across tools, data, and compute so teams can triage regressions, explore physical design tradeoffs, and converge on signoff faster.