Browse Azure Videos (113)

John Savill reviews this week's Azure updates, covering Container Apps logging, confidential VMs, network security, storage quotas, and cross-tenant backup restore. The Halloween edition also covers Azure AI Foundry model cost management, Azure MCP Server 3.0, new model topics, and GitHub Universe.

Microsoft Developer explores model selection using the Microsoft Foundry model catalog. By swapping only the model deployment and running the same evaluation on both options, the episode compares results alongside measured cost and latency to show when a smaller model is suitable for production and when it is not.

Microsoft Developer shows how to build a prompt agent in Microsoft Foundry using a model, instructions, and MCP tools, without managing containers or servers. The video explains when custom orchestration or a different framework calls for a hosted agent, while keeping name-based invocation consistent for callers.

Michael MacKenzie explains how IoT, industrial AI, and physical AI connect machine data to decisions and actions. He outlines the roles of Azure IoT Hub, Azure IoT Operations, and Azure Device Registry, including operational inference and the policy, access, and certificate controls that bridge IT and operational technology.

Karthik Uthaman explains how Azure Front Door engineers distinguish legitimate traffic surges from DDoS attacks. Using whiteboard examples, he explores resource-based defenses, traffic contributors in multi-tenant services, and how threat intelligence and JA4 client fingerprints provide signals beyond per-IP rate limiting while helping protect legitimate users.

Visual Studio Code demonstrates a PostgreSQL performance troubleshooting workflow, tracing an application slowdown from initial signals to database workload and query evidence. The episode covers Azure Advisor guidance, controlled actions, result verification, and a Database Agent experience that brings issue context and recommendations together while retaining review and approval.

Visual Studio Code demonstrates how to bring PostgreSQL workflows into the editor using Microsoft’s PostgreSQL extension. Learn to install the extension, connect to a PostgreSQL database on Azure with Microsoft Entra ID, save connection profiles, and explore database objects without switching tools.

Microsoft Developer highlights three updates: .NET 11 is ready for serious production validation, Python 3.15 defaults to a native Arm64 runtime on Windows on ARM, and browser automation in Microsoft Foundry is generally available. The video outlines what these changes mean for developers.

Microsoft Developer demonstrates how to protect an AI agent from malicious user input and instructions hidden in tool responses. The walkthrough configures Microsoft Foundry guardrails and Prompt Shields, replays both attacks, and uses traces to show where protections intervene before the agent acts.

John Savill reviews this week's Azure and AI updates, including networking, backup, monitoring, storage, and identity changes. Topics include Traffic Manager integration with Azure DNS, keyless TLS, Cosmos DB key rotation, Azure Databricks private networking, and Copilot Studio agent identity.

Visual Studio Code introduces a four-part video series focused on doing more PostgreSQL work directly inside VS Code, including connecting to databases, writing and optimizing SQL, using GitHub Copilot to understand database context, and troubleshooting performance, with Azure mentioned as the underlying platform for modern PostgreSQL workloads.

GitHub demonstrates a hands-on workflow for going from a GitHub issue prompt to a deployed AI agent inside the GitHub Copilot app, using Azure AI Foundry tooling to provision resources, deploy a model, validate grounding to reduce hallucinations, and ship a secure proxy plus a website chat widget.

Microsoft Developer introduces the idea of “adaptive” software that can reason and take actions, positioning it as a shift beyond apps that only respond to user input.

Christoph Berlin explains how an AI agent can speed up root cause analysis by correlating data and reducing the dependency on tracking down subject-matter experts, helping engineers start investigations with better initial context.

Microsoft Developer walks through how developers (and AI coding agents) can build applications with Azure SQL, including a demo flow that starts from an Azure SQL DB container and moves into Azure SQL Database’s free tier, plus an overview of Microsoft SQL agent skills and plugin concepts.

Marko Hotti and Patty Chow show how to build an Azure Cosmos DB app using AI-assisted development, starting from a blank folder and a product spec to generate a working travel planner. They cover data modeling, container design, queries, and local testing using the Cosmos DB Linux emulator and GitHub Copilot in VS Code.

Marko Hotti and Madeline Egan explain how to move an AI agent from a demo to a production-ready system using Azure Cosmos DB, focusing on observability and evaluation practices like tracing, regression testing, and before/after metrics to improve retrieval quality, latency, token usage, memory efficiency, and RU consumption.

Marko Hotti and Madeline Egan show how to build AI agents that keep durable, shared memory across sessions using Azure Cosmos DB, so a multi-agent travel planner can remember user preferences, share context between agents, and continue work after handoffs instead of restarting from scratch.

Marko Hotti and Patty Chow demonstrate how to build a smarter AI travel planner on Azure Cosmos DB by combining operational app data with vector embeddings and search features, so the app can answer nuanced queries while still respecting real inventory and booking workflows.

Microsoft Developer shows how to use Agent Optimizer in Azure AI Foundry to improve an agent when prompt tweaks aren’t reliably fixing behavior. The video walks through building an evaluation dataset, scoring a baseline, generating candidate instruction sets, and validating improvements (quality, latency, and safety) before shipping a new agent version.

Christoph Berlin explains how agentic AI can speed up root cause analysis by correlating signals across multiple systems, ranking likely causes by confidence, and presenting supporting evidence so engineers can start investigations faster after stoppages or slowdowns.

Anna Hoffman explains how Microsoft SQL spans on-premises, Azure, and Microsoft Fabric, highlighting what’s new in the engine, developer tooling, and migration options. She also demonstrates AI-assisted performance troubleshooting and scalable vector search, and shows how Azure Arc/Azure Local and Foundry-based experiences can support modern data-driven apps.

David Pless explains how Azure SQL Database Hyperscale’s cloud-native architecture lets teams scale compute, storage, and read capacity independently, and how features like serverless compute, elastic pools, named replicas, and snapshot-based backups map to different workload and availability needs.

Borko Novakovic explains how Azure SQL Managed Instance (SQL MI) aims to make it easier to move SQL Server workloads to Azure while modernizing over time, with demos around replication and minimal-downtime migration plus a look at new performance configuration options and update-policy trade-offs.

Pam Lahoud explains how to migrate SQL Server workloads to Azure Virtual Machines while keeping OS and database control, with practical guidance on VM sizing, storage selection, and cost considerations like constrained cores for licensing. She also outlines what to build next using Azure services and related Microsoft data/AI capabilities.

Bob Ward explains what “AI-ready” means in SQL Server 2025, focusing on native vector capabilities, embedding generation, and RAG patterns you can implement directly with T-SQL across local and cloud AI model deployments.

Amit Khandelwal demonstrates an end-to-end migration of a working app from SQL Server to Azure SQL Database Hyperscale, using SSMS assessments and VS Code tooling to evaluate readiness, address blockers, and execute a BACPAC-based move. He also shows GitHub Copilot helping resolve a Service Broker issue and validating the cutover.

Microsoft Developer presents SQL Modernization Day, a set of sessions from the Microsoft SQL team focused on moving SQL Server workloads to Azure without rewriting applications, and then preparing those workloads for building AI-ready apps using modern Azure and Fabric SQL options.

Julia Muiruri demonstrates an end-to-end workflow for building and shipping an AI agent directly from Visual Studio Code, using GitHub Copilot and the Foundry Toolkit extension to pick a model, test the agent against a real product catalog, deploy to Microsoft Foundry, and connect it to a website via a secure proxy.

Takuto Higuchi interviews Sindhura Bandhakavi on why AI agents get expensive at scale, and how to think about “tokenomics” beyond simply cutting tokens. The episode shows how Azure AI Foundry features like Model Router and Agent Optimizer can help balance quality, performance, and cost while keeping agent usage measurable and controlled.

John Savill shares a short Azure Weekly Update for 16 October 2026, covering a handful of platform changes across resiliency, compute, networking, storage, and Azure’s AI hardware/model announcements.

Microsoft Developer presents a Data Exposed session on Fabric Database Hub, focusing on bringing Azure SQL, SQL Server, Cosmos DB, PostgreSQL, and Fabric databases into a single place for discovery, estate-level visibility, governance, and AI-assisted insights to prioritize actions.

Aaron Stark talks with Haishi Bai about Adaptive Applications, a capability-based architectural approach for building portable apps that can run across Azure, sovereign environments, edge, and on-premises. They focus on decoupling application needs (AI, data, identity) from the underlying services to meet compliance, residency, and continuity requirements.

Microsoft Developer demonstrates an end-to-end workflow for building and shipping a hosted AI agent from the terminal using GitHub Copilot CLI and Microsoft Foundry, including planning, resource creation, model selection, evaluation-driven improvements, and a secure Azure Functions proxy to connect the agent to a website chat experience.

Ali Zaman explains Microsoft Discovery and how it coordinates specialized AI agents to help scientists run an evidence-driven research loop, using battery design as the example and focusing on why search and summarization aren’t enough for real R&D workflows.

Microsoft Developer highlights updates across .NET Aspire, Azure Virtual Machines, and SQL Server, focusing on what’s new in Aspire 13.6 for distributed app debugging, Azure VM lifecycle guidance changes, and running SQL Server on Azure Local when cloud-only isn’t an option.

John Savill runs through the Azure Weekly Update for 9th October 2026, covering a set of platform changes and retirements across AKS, App Service, developer environments, networking/WAF, API Management, PostgreSQL, SQL Server, and Fabric/OneLake, plus a security-focused note on Always Encrypted with Intel SGX enclaves.

Microsoft Developer explains how to debug an AI agent that’s slow or confidently wrong by turning on server-side tracing for an Azure AI Foundry project and inspecting each run step-by-step in Application Insights, including model calls, tool calls, tokens, and latency, then querying patterns with KQL.

Anna Hoffman recaps the key Microsoft SQL announcements shared at SQLCon/FabCon Barcelona 2026, covering hybrid and migration updates, Database Hub changes, Hyperscale improvements, SQL DB in Fabric updates, and developer-focused tooling and agent-related updates.

Gareth Bland explains TokenOps: a practical way to control AI agent spend by focusing token budget where it creates real value. He breaks down why multi-turn agents get expensive fast, how cached input changes the cost curve, and which efficiency ratios help teams measure whether they’re getting leverage or just burning tokens.