Browse Azure Videos (151)

John Savill rounds up a week of Azure platform changes and retirements, spanning compute/storage updates, database and identity improvements, monitoring changes, and several developer-facing AI items including GitHub Copilot Agent Mode in SSMS and Azure AI Foundry agent licensing and model availability.

Bruno Capuano and Tommaso Stocchi walk through building distributed multi-agent applications using .NET Aspire and Microsoft Agent Framework (MAF), focusing on how multiple agents coordinate across services and exchange context. The session connects these patterns to Foundry-oriented scenarios and demonstrates them with a ski resort example.

John Savill gives a fast-paced rundown of key announcements from Microsoft Build 2026, highlighting notable platform updates across Azure, AI, and identity/security topics such as Entra and passkeys.

Dan Wahlin demonstrates an “agentic journey” workflow that takes an app idea through planning, coding, infrastructure creation, and deployment to Azure, using GitHub Copilot CLI and Azure skills to handle tasks like Bicep templates, health probes, and database wiring for an app backed by Azure SQL and Microsoft Foundry.

Henk Boelman live-codes a real-time, voice-first multimodal agent in Azure AI Foundry using the Voice Live API, showing how to combine speech input, model reasoning, and speech output, then connect the agent to external tools via MCP so it can take real actions.

John Savill runs through a Build-special weekly Azure update, covering a wide set of platform announcements across compute, containers, integration, monitoring, databases, Fabric/Databricks, and Azure AI Foundry—plus security-focused items like confidential computing and Purview agent integrations.

Charles Feddersen and Abe Omorogbe explain how AI apps and agents change database design, focusing on reasoning over operational data instead of only transactions. They demo new capabilities across Azure SQL Database, Azure Cosmos DB, and Azure HorizonDB (cloud-native PostgreSQL) to simplify architectures and reduce latency.

Seth Juarez explains how Azure AI Foundry Toolboxes let teams build, discover, and govern tools across multiple AI agents, reducing duplicated integration work around authentication, credentials, and endpoint wiring.

Milos Colic shares how Xoople scaled Python-based AI workloads on Azure using Ray via Anyscale, covering the distributed-systems challenges behind data ingestion, training, and inference, and why the team prioritized delivering outcomes over operating clusters.

Nikisha Reyes-Grange introduces Azure HorizonDB and Rayfin, focusing on how these Azure Data and Microsoft Fabric innovations aim to modernize PostgreSQL operations and simplify building and running data applications, including SQL-level AI functions and hybrid search concepts.

Jeff Hollan and Lee Stott explain how hosted agents in Microsoft Foundry help teams move from local agent prototypes to production-grade AI systems, with a focus on identity, isolation, evaluation, and lifecycle management so developers can deploy secure, scalable agents with clearer operational boundaries.

Vivek Chauhan shares a quick on-the-show-floor update from Microsoft Build 2026 on Fireworks AI, including the scale they run at and how their partnership with Microsoft connects to Azure AI Foundry for governance, security, and reliability.

Adrian Macias discusses how open-source AI development is shifting across local AI PCs and Azure, covering agentic AI, AI-assisted coding, and the practical need for flexible deployment options as teams experiment and scale AI workloads.

Nish Anil, Hazem El-Hammamy, and Jeff Fritz present a Microsoft Build 2026 session on using GitHub Copilot’s modernization capabilities and agentic AI to analyze large legacy codebases, map dependencies, plan upgrades, and refactor safely with governance controls, including examples spanning mainframe and .NET modernization.

Mark Russinovich and Ion Stoica discuss how AI platforms need to evolve for agentic, multimodal, globally distributed workloads, covering infrastructure fundamentals, training and real-time serving architectures, and why open source, security, and governance are becoming core platform requirements.

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.

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.

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.

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 infrastructure and architecture innovations, covering high-performance networking, serverless building blocks, and security capabilities aimed at running modern AI workloads across cloud, on-premises, and edge environments.

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.

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.

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.

Tina Schuchman and Jeff Hollan walk through the end-to-end lifecycle for building production-grade AI agents using Foundry Agent Service and Microsoft Agent Framework, covering local prototyping through hosted deployment, with identity, secure networking, evaluations, and operational lifecycle management, plus how GitHub Copilot fits into the workflow.

Andrew Westgarth and Gaurav Seth explain how to modernize legacy .NET applications using Managed Instance on Azure App Service, focusing on removing migration blockers without code rewrites and moving to a scalable PaaS foundation. They also show how GitHub Copilot can support AI-assisted modernization and MCP-driven API interactions.