How to Optimize .NET Apps with Application Insights

If you’re a developer, that means you’ll have real-time access to definitive Microsoft resources, delivered at the right level of detail to help you solve complex problems, write high-quality code, streamline workflows, and build smarter AI agents. If you’re a technology leader, you’ll be able to equip your teams with smarter tools that provide timely, reliable resources, opening up new ways to learn and work more efficiently and effectively.

🚀 Your .NET App Will Be an AI Agent (Whether You Like It or Not) Microsoft isn’t just building Copilot add-ons — they’re reshaping the entire .NET ecosystem around agentic AI. Semantic Kernel → wrap your .NET code as AI plugins Model Context Protocol (MCP) → standard for discovery + invocation Azure Orchestration (Functions, Event Grid, Service Bus) → make it production-grade This isn’t optional. By 2026, if your APIs aren’t agent-ready, they may not even show up in the Microsoft Copilot ecosystem. In my latest blog, I break down: ✅ Before vs After: REST vs Agentic apps ✅ Code & diagrams with SK plugins + planners ✅ Enterprise concerns: security, cost, resilience ✅ A concrete action plan for .NET engineers 👉 Read it here: https://lnkd.in/duqppgkE 💡 Question for you: Will your APIs be Copilot-ready this year — or will your competitor’s show up first? #DotNet #Azure #SemanticKernel #AI #Copilot #Microsoft #Architecture

7 ways to build smart agents with Microsoft Azure... Whether you’re a professional developer, a low-code builder, or a business user, there’s an option that fits your needs to build AI agents. Here are some of the key paths: 1/ Azure AI Foundry Agent Service – A managed service to create, manage, and use agents with enterprise security and flexibility. Learn More: https://lnkd.in/gkTBiQnt 2/ OpenAI Assistants API – Ideal if you’re already using OpenAI models, with seamless support in Azure OpenAI Learn More: https://lnkd.in/gzTDRf9b 3/ Semantic Kernel – A lightweight, open-source SDK for building agents and multi-agent solutions. Learn More : https://lnkd.in/gZ_zwKcg 4/ AutoGen – An open-source framework for rapid agent prototyping, great for research and experimentation. Learn More: https://lnkd.in/g27UfNZE 5/ Microsoft 365 Agents SDK – Build self-hosted agents that work across channels like Slack or Messenger Learn More: https://lnkd.in/gtXN4xd6 6/ Microsoft Copilot Studio – A low-code platform that lets anyone quickly design and deploy agents with a visual interface Discover More: https://lnkd.in/gNNHH3Yg 7/ Copilot Studio Agent Builder in Microsoft 365 Copilot – For business users, this tool makes it simple to create agents by describing what you need—no coding required. Discover More : https://lnkd.in/gvZEYmeN Which one is your favorite ---full control with SDKs or the speed of low-code tools? #AIagent #Microsoft #Azure

💡 A small AI experiment in a .NET project delivered big results Last month I worked with a client who runs a large customer-support operation. The challenge: agents were spending too much time reading long email threads before replying. We decided to try something simple 👇 🔹 Built a lightweight ASP.NET Core API 🔹 Integrated Azure OpenAI to summarise support emails 🔹 Stored the summaries in SQL Server and surfaced them in the agent dashboard The outcome surprised us: ✅ Average handling time dropped by 40% ✅ Agents felt less stressed and more focused on solving the real issue ✅ Zero disruption to existing systems The key learning? You don’t need a massive AI overhaul to create impact. Even a single, well-chosen AI feature inside a .NET app can change productivity overnight. 👉 If you’re a .NET developer wondering where to start with AI, look for a repetitive pain point and automate just that one task. The ROI speaks for itself. 🚀 Have you tried a similar small-scale AI integration in your projects? I’d love to hear your story. #DotNet #Azure #AI #DeveloperCommunity #CaseStudy #FutureOfWork #Microsoft #IndiaTech

7 ways to build smart agents with Microsoft Azure... Whether you’re a professional developer, a low-code builder, or a business user, there’s an option that fits your needs to build AI agents. Here are some of the key paths: 1/ Azure AI Foundry Agent Service – A managed service to create, manage, and use agents with enterprise security and flexibility. Learn More: https://lnkd.in/gkTBiQnt 2/ OpenAI Assistants API – Ideal if you’re already using OpenAI models, with seamless support in Azure OpenAI Learn More: https://lnkd.in/gzTDRf9b 3/ Semantic Kernel – A lightweight, open-source SDK for building agents and multi-agent solutions. Learn More : https://lnkd.in/gZ_zwKcg 4/ AutoGen – An open-source framework for rapid agent prototyping, great for research and experimentation. Learn More: https://lnkd.in/g27UfNZE 5/ Microsoft 365 Agents SDK – Build self-hosted agents that work across channels like Slack or Messenger Learn More: https://lnkd.in/gtXN4xd6 6/ Microsoft Copilot Studio – A low-code platform that lets anyone quickly design and deploy agents with a visual interface Discover More: https://lnkd.in/gNNHH3Yg 7/ Copilot Studio Agent Builder in Microsoft 365 Copilot – For business users, this tool makes it simple to create agents by describing what you need—no coding required. Discover More : https://lnkd.in/gvZEYmeN Which one is your favorite ---full control with SDKs or the speed of low-code tools? hashtag #AIagent hashtag #Microsoft hashtag #Azure

⚡ AI workloads demand smarter infrastructure—and now, smarter developer tools. Both Google and Microsoft just dropped big updates: 1️⃣ Google’s Gemini CLI in Zed → AI that lives inside your editor, learning from docs, APIs, and live code. 2️⃣ Microsoft’s Visual Studio update → GPT-5 integration, semantic search, Git history context, and multi-model support from OpenAI, Google, and Anthropic. This isn’t just about productivity—it’s about how data centers and AI backends are being leveraged directly in developer workflows. These copilots rely on powerful compute, fast data retrieval, and secure integration pipelines. The message is clear: the boundary between AI infrastructure and AI application development is disappearing. Data centers aren’t just serving requests—they’re co-building the future of software. How will this impact how enterprises provision AI workloads in 2026? 🏗️

I just completed the "Develop an AI app with the Azure AI Foundry SDK" module on Microsoft Learn, and it was a great hands-on step in moving from deploying models to actually building AI applications. Here are my key takeaways: 1. Azure AI Foundry SDK - Provides a unified way to interact with models, agents, and connections through code (Python, C#, JavaScript, Java). This makes it easy to bring AI into apps without getting locked into a single provider. 2. Connections & Flexibility - You can configure project connections to models, data sources, or tools, allowing you to switch providers or scale without rewriting core logic. 3. Building Chat & Agent Apps - The SDK supports creating chat apps and more advanced agents. Agents can use tools like file uploads, vector stores, and knowledge bases, opening up real use cases beyond simple Q&A. 3. Developer Workflow - The module emphasized best practices like secure authentication (via Azure Identity), using environment configs, handling long-running operations (checking agent status), and cleaning up resources properly. This module gave me a strong foundation in AI app development, beyond just testing models in the portal. I now understand how to embed models into production-ready applications with governance, flexibility, and scalability in mind. Skills you will learn in this module: 1. Generative AI app development. 2. Working with the Azure AI Foundry SDK 3. Building agents & multi-tool workflows 4. API integration & secure deployment practices

🚀 𝗔𝗜 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮 𝗳𝗶𝗿𝘀𝘁-𝗰𝗹𝗮𝘀𝘀 𝗰𝗶𝘁𝗶𝘇𝗲𝗻 𝗶𝗻 .𝗡𝗘𝗧 𝟭𝟬 I just published a new tutorial on 𝗵𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁.𝗘𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻𝘀.𝗔𝗜 — the new abstraction layer that makes it easier than ever to integrate AI providers like 𝗢𝗽𝗲𝗻𝗔𝗜, 𝗔𝘇𝘂𝗿𝗲 𝗢𝗽𝗲𝗻𝗔𝗜, 𝗮𝗻𝗱 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲 into your applications. 🔹 No more juggling multiple SDKs 🔹 Swap providers with just config changes 🔹 Use the same clean abstractions (IChatClient, ITextGenerationClient) everywhere In this article, I walk you through: ✅ Setting up a .NET 10 console app ✅ Adding and configuring multiple AI connectors ✅ Writing provider-agnostic code with Dependency Injection ✅ The theory behind why Microsoft.Extensions.AI matters (extensibility, testing, future-proofing) 👉 Full tutorial here: https://lnkd.in/dAjYjbdC This is the 𝗜𝗟𝗼𝗴𝗴𝗲𝗿 moment for AI in .NET. If you’re building AI-powered apps in C#, this is something you’ll want to keep an eye on. What provider would you like to see in future examples — Anthropic, Mistral, or Ollama? #dotnet #csharp #ai #openai #azure #huggingface #developers

𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗗𝗼𝗻𝗲 𝗥𝗶𝗴𝗵𝘁 Ever wished you could just write code—without worrying about servers, infrastructure, or scale? Azure Functions does exactly that… and more.  A serverless, event-driven compute platform from Microsoft Azure that lets you focus on writing code while Azure handles infra, scaling, and billing. * 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:  * Reduces operational overhead  * Scales on demand  * You only pay for what runs * 𝗖𝗼𝗺𝗺𝗼𝗻 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀:  * 𝗙𝗶𝗹𝗲 𝘂𝗽𝗹𝗼𝗮𝗱𝘀 → Automatically process images when they land in blob storage  * 𝗜𝗼𝗧 & 𝗲𝘃𝗲𝗻𝘁 𝘀𝘁𝗿𝗲𝗮𝗺𝘀 → Transform sensor data in real-time.  * 𝗤𝘂𝗲𝘂𝗲-𝗯𝗮𝘀𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 → Decode messages and kick off AI inference.  * 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲𝗱 𝗷𝗼𝗯𝘀 → Clean up logs or send reports nightly.  * 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜𝘀 → Build fast, scalable microservices using HTTP triggers.  * 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 → Chain business logic with Durable Functions.  * 𝗗𝗕 𝘁𝗿𝗶𝗴𝗴𝗲𝗿𝘀 → React when a new document is added or updated.  * 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 → Process queues via Azure Queue, Service Bus, or Event Hubs. * 𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Native support for C#, Java, JavaScript, PowerShell, and Python. Plus, custom handlers for Rust, Go, and more. * 𝗟𝗼𝗰𝗮𝗹 𝗱𝗲𝘃 & 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁:  Use Azure Functions Core Tools to build, test, and deploy from your local machine—then publish straight to Azure.  𝗖# 𝗔𝘇𝘂𝗿𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 [FunctionName("HelloFunction")] public static IActionResult Run([HttpTrigger] HttpRequest req)    => new OkObjectResult($"Hello, {req.Query["name"] ?? "Guest"}!"); How are you automating real-time data tasks in your apps—or what would you love to automate if infrastructure weren’t a concern? #AzureFunctions #Serverless #CloudComputing #CSharp #AzureDev #DeveloperLife

🚀 LangChain + Azure OpenAI + MCP = The Future of Intelligent Agents 👨💻 What if your AI agent could: ✔️ Reason step-by-step ✔️ Use internal tools & APIs ✔️ Remember past interactions ✔️ Work securely in Azure with enterprise compliance ✔️ Be built in Python or C#? That’s exactly what I’ve done by integrating LangChain with Azure OpenAI and Microsoft’s MCP Agent Framework. In my latest blog, I break down: 🧠 Real-world use case from enterprise AI 🛠️ Python + C# code (yes, both!) 🔐 How to secure & scale with Azure Functions 📦 Tools, memory, RAG, and custom workflows 📊 Benefits for developers & businesses alike 📍 Whether you're building a virtual assistant, compliance checker, or internal knowledge agent—this is the architecture to adopt. 👉 Read the full blog now: https://lnkd.in/gie2q66P Let’s shape the future of enterprise AI together. ✍️ By Abhishek Kumar | #FirstCrazyDeveloper #LangChain #AzureOpenAI #MCPAgent #EnterpriseAI #AIArchitecture #Python #DotNet #AzureFunctions #GenerativeAI #AutonomousAgents #AbhishekTake #DevCommunity #MicrosoftAI #RAG #MultiAgent #AIEngineer #TechLeadership #AbhishekKumar