AI
How Uno Platform uses .NET, MCP, and AI to build high quality apps
How Uno Platform built two Model Context Protocol servers in C# so AI agents can ground themselves in real documentation and then see and drive a running cross-platform .NET app.
New Azure SQL Foundations video series with GitHub samples
Bob Ward and I recently released a series of videos, read more in the original blog post or go directly to the series on YouTube. The Azure SQL Database Foundations series are four videos that take you from your first Hyperscale database to AI features running against your own operational data. We also included how to assess and migrate (with AI and skills!) to Hyperscale in the first place, and the common optimizations you should consider. Every episode ships with a repo, so you can follow along in your own environment instead of watching someone else’s terminal.
From dotnet run to Foundry Hosted Agent in 3 lines of C#
You built an agent with Microsoft Agent Framework and it works great on your machine. Now what? Turns out deploying it to production takes 1 NuGet package, 3 lines of C#, and 2 commands. Let's do it.
Outgrowing SQL Server Express? Upgrade to Azure SQL Database Free Tier in 3 Steps
If you’ve been building local prototypes, internal tools, web backends, or lightweight services on SQL Server Express, you know the routine: it’s free, familiar, and gets the job done – until you hit hardware limits or spend weekends manual backup scripts and handling OS patches. Learn how to upgrade from SQL Server Express to Azure SQL Database free tier in 3 simple steps. Whether you're running SQL Server 2022 (or older) and hitting the hard 10 GB storage wall, or using SQL Server 2025 and realizing 50 GB of disk space doesn't solve the strict 1.4 GB memory bottleneck, staying on Express limits your applica...
Azure Cosmos DB in the Agentic Era: Data Tools for Developers and AI Agents
Anthropic's 2026 Agentic Coding Trends Report captures an important tension in how developers use AI. Engineers report using AI in roughly 60% of their work, yet say they can fully delegate only 0–20% of their tasks. AI is becoming a constant collaborator, but it still needs setup, supervision, validation, and human judgment. That gap is especially relevant when an agent starts working with a database. Database requests often sound easy. “Show me the failed orders from last week.” “Why is this query expensive?” “Does this container have the field I need?” For a developer, answering those question...
From single call to agents: five new Claude capabilities now available in Microsoft Foundry
Structured outputs, web search, web fetch, MCP connector, and tool search are now available for Claude models hosted on Azure in Microsoft Foundry, turning a model endpoint into a production agent platform.
Beyond Vector Indexes: Azure SQL Brings Optimizer Intelligence to Vector Search
Why production AI retrieval depends on more than vector index performance. It's easy to think of AI retrieval as a vector search problem. For many developers, the first conversation starts with vector index benchmarks: How many queries per second can it handle? What's the latency? What's the recall? How well does it scale? Those metrics are critical, but production AI retrieval rarely consists of vector similarity alone. Equally important is what happens when vector search becomes part of a real application workload, one that includes filters, joins, security policies, ranking logic, and live operational d...
Routing and Failover for Microsoft.Extensions.AI
Route requests across models and providers natively in Microsoft.Extensions.AI with RoutingChatClient, SemanticRoutingChatClient, and FailoverChatClient — new experimental primitives for content-based routing, failover, and custom routing policies.
From Sync APIs to support for the GPT-5 model series and agentic workflows: What’s new in Azure Content Understanding – August 2026
Enterprise content is no longer just something people read. AI apps and agents are only as useful as the information they can understand, yet much of the world’s enterprise knowledge is locked in documents, forms, tables, images, audio, and video. The latest Azure Content Understanding updates help developers turn that content into structured, grounded data with less custom processing. This release brings broader support for the GPT-5 model series, lower token usage, improved confidence scoring, new synchronous APIs for Read and Layout, advanced contextualization for higher-quality extraction, new tax-focused pre...
Azure Content Understanding GPT-5 Series Guide: Model Selection, Grounding Improvements, and Confidence Enhancements
Enterprise content is no longer just something people consume. As organizations increasingly rely on AI to extract and act on information from documents, images, audio, and video, Azure Content Understanding is expanding support for the GPT-5 series and improving grounding and confidence to deliver greater flexibility, efficiency, and quality. This expanded model catalog enables organizations to choose the right level of intelligence for each workload, helping reduce costs for high-volume processing while preserving access to advanced reasoning capabilities where needed. It also provides optimized pipelines tu...
