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This closes out the series' capstone: the multi-agent customer support system built across Parts 6-9, now hardened with evaluation, cost governance, and observability so it can actually run in production with an on-call rotation behind it, not just in a demo environment. Continuous evaluation pipeline Evaluation: measuring quality continuously, not just at launch A one-time eval before launch tells you nothing about drift once real traffic — and real edge cases — start hitting the system. Set up a continuous evaluation pipeline using a G-Eval-style approach, where a separate model scores production outputs against explicit criteria: eval_criteria = { " correctness " : " Does the response accurately reflect the order/refund status retrieved from the tools? " , " escalation_appropriateness " : " If the case was ambiguous or high-risk, did the agent escalate to a human rather than resolving it alone? " , " tone " : " Is the response professional and appropriately empathetic given the customer ' s stated frustration level? " , } def geval_score ( response , context , criterion_name , criterion_description , eval_model_client ): prompt = f """ Evaluate the following response against this criterion: { criterion_description } Context: { context } Response: { response } Score from 1-5 and give one sentence of reasoning. Return JSON: {{ " score " : int, " reasoning " : str}} """ result = eval_model_client . complete ( prompt ) return json . loads ( result ) def run_continuous_eval ( sample_of_production_traffic ): scores = { crit : [] for crit in eval_criteria } for interaction in sample_of_production_traffic : for crit_name , crit_desc in eval_criteria . items (): result = geval_score ( interaction . response , interaction . context , crit_name , crit_desc , eval_model_client ) scores [ crit_name ]. append ( result [ " score " ]) return { crit : sum ( vals ) / len ( vals ) for crit , vals in scores . items ()} Sample a percentage of real production traffic daily (not just s

Microsoft has announced the limited public preview of Copilot Autofix for GitHub Advanced Security for Azure DevOps, extending AI-powered vulnerability remediation to teams using Azure Repos. By Craig Risi

I recently built a multi-agent customer support system on Azure AI Foundry and NVIDIA NIM. First time doing anything like this. Made four predictions upfront about what would happen. Three of them were wrong. Here is what I actually learned. 1. "Tokens" is not a unit of cost It is a unit of work. The price per unit of work varies by 5-10x depending on which model did the work. I was tracking total token count across both the small 9B model and the large 49B model as if they cost the same. They do not. Total tokens went up in the optimized version. Cost in dollars probably went down. I was measuring the wrong thing the whole time. 2. A verbatim hash cache on natural language traffic deflects ~0% of queries I predicted 25-40% cache deflection. The actual number was 0%. Every query in my test set was a unique string, so the hash-based cache never had a single chance to fire. A verbatim cache is not a simpler version of a semantic cache. It is a different thing entirely. If your workload is natural language, build semantic similarity caching from day one, not as an upgrade later. 3. configure_azure_monitor() does not capture OpenAI SDK calls by default You need to install and initialize opentelemetry-instrumentation-httpx explicitly: pip install opentelemetry-instrumentation-httpx==0.61b0 from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor HTTPXClientInstrumentor().instrument() Without this, your App Insights Logs will show customMetric and performanceCounter entries (CPU, memory) but nothing about what your agent actually did. 4. Pin your OpenTelemetry versions or everything breaks Installing opentelemetry-instrumentation-httpx without version pinning pulled in opentelemetry-api 1.42.1. But azure-monitor-opentelemetry-exporter needs opentelemetry-api==1.40. The conflict is silent until things start misbehaving. Pin everything to the 0.61b0 / 1.40.0 line: pip install \ "opentelemetry-api==1.40.0" \ "opentelemetry-instrumentation==0.61b0" \ "opentelem

👋 Hi DEV Community! I'm the creator of Cloud Compass , a newsletter dedicated to making cloud computing easier to understand. If you've ever felt overwhelmed by the constant stream of cloud updates, new services, documentation, and buzzwords, you're definitely not alone. I originally published this as the welcome issue of my newsletter, and I'm sharing it here because I hope it helps developers, students, and anyone starting their cloud journey. ☁️ Welcome to Cloud Compass Cloud news, concepts, and insights without the overwhelm. The cloud is moving fast. Let's make sure you're not left behind. Welcome to Cloud Compass — your guide to staying current with cloud computing while building real cloud knowledge, without feeling overwhelmed. Let's be honest. Keeping up with cloud technology is exhausting. Cloud providers release new services, features, and updates almost every week. Between official documentation, blog posts, YouTube videos, LinkedIn posts, and countless tutorials, it's difficult to know: What actually matters? What should you learn first? Where do you even begin? That's exactly why I started Cloud Compass . This isn't a newsletter written by AI or filled with copied announcements. It's written by someone who genuinely enjoys learning cloud computing and wants to make it easier for everyone else—whether you're: A developer trying to stay current A student entering the cloud world for the first time A professional who wants to understand cloud technology without spending hours reading documentation The goal is simple: Show up regularly with something that's actually useful. "I want Cloud Compass to feel less like reading tech news and more like having a conversation with someone who already read everything and saved you the time." Here's what you can expect 📰 Cloud News Roundup The most important updates from across the cloud industry—not just AWS, Azure, and Google Cloud, but the wider cloud ecosystem. No endless lists of links. No unnecessary hype. Just

Rule of thumb : Secrets → Key Vault Configs → App Configuration Use both for secure + flexible configuration management. Azure Key Vault Purpose : Securely store sensitive information such as secrets, keys, and certificates. Use Cases : Store API keys, connection strings, tokens. Manage and rotate TLS/SSL certificates. Protect cryptographic keys used for encryption/decryption. Strengths : Built-in hardware security module (HSM) support. Access policies and RBAC for fine-grained control. Automatic secret rotation with some Azure services. Logging and monitoring via Azure Monitor. Limitations : Not designed for feature flags or configuration settings that change frequently. API calls can add latency if used excessively at runtime. Azure App Configuration Purpose : Centralized application configuration management . Use Cases : Store non-sensitive app settings (feature flags, UI options, app behavior). Versioned configurations and labels (per environment, per region). Enable dynamic configuration refresh in apps. Strengths : Feature flag management built-in. Supports key-value pairs with labels for environment separation. Integration with Azure Functions, App Service, AKS, and more. High availability and global distribution. Limitations : Not designed to store secrets or keys. Does not provide encryption key lifecycle management. When to Use Which Use Key Vault when : Handling secrets (DB passwords, API keys). Managing certificates and encryption keys. Need secure storage with strong access policies. Use App Configuration when : Handling app configs (feature flags, toggle dark mode, regional endpoints). Need dynamic refresh without redeployment. Want environment-based configuration with versioning. How They Work Together In most real-world solutions, you combine both : Use Azure App Configuration for general settings and feature management. Reference Azure Key Vault inside App Configuration for sensitive values. Example: AppConfig:DbConnectionString → points to Key Vaul

Modern cloud deployments involve many tools with different lifecycles, creating a heavy burden on engineers. The Kubernetes ecosystem offers a unified Control Plane approach. Sharing best practices through tech talks and inner-source collaboration can create an engaged community and drive adoption. By Ben Linders

Introduction This is the second part of sharing my journey to create a microservices-based backend system, in the previous part, I introduced the system and its services. In this part I am going to mention the main things that happened with me from then until now. Transitioning to online DBaaS platforms The first main thing was to transition my databases from my local device to accessible over the internet. I used Neon and MongoDB Atlas . Although performance-wise it is better to deploy the database on the same server that the whole system so all services can access the databases without network overhead , This solution is better from a point, that is avoiding consuming the free VM instance that I got to host my main services, as with DBaaS platforms I will not require storage for databases or additional overhead to handle the DBMS on a free small resources-constrained VM. Observability Observability is one of the main concerns in any system, it gives the ability for the system to expose what happens inside it without any need to guess and manually trace the code to determine where the error may happened. I utilized OpenTelemetry , as I found it is most used and accepted tool to log, trace and collect metrics about the system and the traffic. Also, I loved that it is not related to specific framework, that is .NET by the way, I loved the idea that it is standalone tool. But logging and tracing and metrics collection will not be beneficial if we do not see it actually and can achieve monitoring from those telemetry data, so I used Grafana Cloud to export the telemetry data to some place that is accessible online and a tool that can generate dashboards and visualizing for the metrics ( Prometheus ), and UI that can show all the telemetry data easily. Deployment Until this point, we have a system that is fully functional and have consistent docker configuration for its services via Docker Compose, and it database is already there and available. Now this is the point at

At this year's Microsoft Build 2026, Microsoft unveiled a broad set of enhancements to Azure Kubernetes Service (AKS) aimed at making Kubernetes a first-class platform for AI training, inference, and large-scale cloud-native applications. By Craig Risi

I have written some version of "never hardcode secrets, store them in Key Vault instead" in at least five of my last nine posts on this blog. I never actually stopped to explain what that means in practice. This post fixes that, using the real secrets this very blog depends on: a database connection string, an admin panel password, and a set of GitHub deployment credentials. What Azure Key Vault Actually Is Azure Key Vault is a managed service for storing secrets, encryption keys, and certificates securely in the cloud. Instead of a password sitting in a configuration file or a public GitHub repository where anyone with read access can see it, the password lives in Key Vault - encrypted, access-controlled, and logged every single time it is read. Picture your code as a house with see-through walls - anyone looking at the repository can see everything inside, including any password left lying on the kitchen table. Key Vault is a bank vault a few streets away. Your code does not hold the password directly; it holds a key card that lets it walk over and request the password at the exact moment it is needed. Lose the key card, and you still cannot get into the vault without proper identity verification on top of it. Three Things Key Vault Stores Secrets are plain string values - passwords, connection strings, API keys, tokens. Keys are cryptographic keys used for encrypting and decrypting data, or for signing and verifying it. Certificates are X.509 certificates used for TLS or client authentication between services. Most applications, including this one, primarily use the Secrets feature. An Honest Admission About TechStackBlog's Own Setup This blog does not actually use Key Vault directly today. The database password lives in Azure App Service Configuration. The admin panel password and deployment credentials live in GitHub Secrets. For a single-application personal project, this is a perfectly reasonable and secure setup. Key Vault earns its place once you have multi

Azure Functions shipped a serverless agents runtime in public preview at Build 2026. Agents are defined in .agent.md markdown files with YAML triggers, MCP server access, 1,400+ connectors, and sandboxed execution. The Functions team confirmed to InfoQ that the runtime adds no cold start overhead and no billing premium beyond standard Flex Consumption. By Steef-Jan Wiggers

A couple of years ago, I built a custom flashcard app. I had a huge list of words and sentences in Japanese that I collected in an Excel file. I wanted an app that could easily take them and display them on flashcards. The flashcard app was useful, but the main issue was that I could only use it on my laptop. This meant that when I wasn't home, I had no access to it. I made some updates so that I could deploy it to Azure and now I can use it on the train or at the park. I wanted to share the app and lessons learned during development. What It Does The app is a straightforward spaced repetition flashcard tool. You create collections, fill them with cards (front/back/optional notes), and review them. After each card you rate your recall: Button Meaning Easy Remembered without effort Good Remembered correctly Hard Remembered with difficulty Again Forgot (resets to day 1) Ratings feed the SM-2 algorithm, which is the same algorithm as other popular spaced repetition apps like Anki. Cards that are easy get pushed further and further into the future. Cards that are difficult will come back sooner. After a while, you're just reviewing what you actually need to review. There's also a 45-second timer per card. If it expires before you complete the card, it automatically counts as Again (Resets to day 1). Before the timer, I found it easy to lose focus or open another tab and forget about the current card. This has helped me stay focused for longer and stay on this task. The CSS is specifically designed to be mobile friendly. The Tech Stack Frontend: Blazor WebAssembly (.NET 10) Backend: ASP.NET Core minimal API (.NET 10) Database: Azure SQL (Basic DTU tier) Hosting: Azure Static Web Apps (frontend) + Azure App Service F1 free tier (backend) I mostly use C# at work, so Blazor WASM was a natural fit. The whole app shares models and flows together without jumping between languages. Importing Cards from Excel This Excel import function is one of the main reasons I made this app.

Azure API Management shipped a Unified Model API that lets clients speak one format while APIM transforms requests to Anthropic, Vertex AI, and other backends. Content safety policies now cover MCP tool calls and Agent-to-Agent payloads alongside LLM traffic. Token metrics expanded to track reasoning, cached, and audio tokens across providers. By Steef-Jan Wiggers

Microsoft used their Build 2026 event to announce new functionality for Microsoft Foundry. Citing Foundry as "the place where AI agents move from experiments to production systems," in a blog post, Nick Brady writes that the release brings “runtime, tools, memory, grounding, models, observability, and governance” that developers need for production agents, rather than just new model endpoints. By Matt Saunders

If you are just getting started with Azure CLI and Bash scripting, this post is for you. I will walk you through how I automated the creation of Azure resource groups for multiple environments using a single Bash script — something that was taking a cloud admin several manual steps every week. This is Project 2 in my TechRush Cloud Engineering bootcamp series. If you want to see where this journey started, you can read my previous post where I tackled deploying a web app across two Azure regions for the first time . That project involved real blockers — quota limits, CLI version mismatches, and a deep dive into Azure Resource Providers. This one went smoother, and I think that is because the previous project was the hard school. The Problem Imagine a cloud administrator who has to create five resource groups every single week, one for each active project: Project-A-RG Project-B-RG Project-C-RG Project-D-RG Project-E-RG Every week. By hand. Management's response was simple: automate it. But here is where the task gets more interesting. Instead of creating one flat resource group per project, the better approach is to create four resource groups per project — one for each environment: Dev Test UAT Production This matters because each environment needs its own access controls, cost tracking, and lifecycle rules. You do not want your Development environment sharing a resource group with Production. Keeping them separate is a real-world cloud best practice, not just a bootcamp exercise. What You Will Need Before running this script, make sure you have the following set up: Azure CLI installed on your local machine. You can follow the official installation guide . An active Azure account . A free account works fine for this. A terminal that runs Bash — Linux, macOS, or WSL on Windows. Understanding the Design The core idea behind this script is parameterization . Instead of hardcoding project names, the script accepts a project name as input and uses it as a prefix for ev

Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of Majorana 2, a topological quantum chip with 1,000x reliability improvement and 20-second qubit lifetimes. Microsoft now targets a scalable quantum computer by 2029, halving its original timeline. By Steef-Jan Wiggers

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents integrate via agent-loop orchestration, Foundry agents, and managed sandbox. Knowledge as a Service provides a fully managed RAG pipeline. By Steef-Jan Wiggers

If you run Aviatrix on Azure, there is a slow-moving infrastructure change happening underneath your gateways right now that is worth paying attention to. Microsoft started rolling out a new generation of network hardware on May 26, 2026, called MANA (Microsoft Azure Network Adapter). For most Azure workloads, the change is invisible. For network virtual appliances (NVAs) like Aviatrix gateways, it is not, and Aviatrix has issued a field notice ( FN-2026-AZ-001 ) telling customers to take action. What is MANA and Why is Microsoft Rolling it Out? For roughly a decade, Azure VMs with Accelerated Networking enabled have used Mellanox-based NICs exposed to the guest as mlx4/5 SR-IOV adapters . SR-IOV (Single Root I/O Virtualization) lets the VM talk to the network card hardware directly, bypassing the hypervisor's virtual switch. This is what gives Accelerated Networking its low-latency, high-throughput characteristics. Microsoft has been quietly building its own in-house networking silicon. MANA is the result: a Microsoft-designed network adapter that replaces the Mellanox hardware Azure has been using on the host side. From an Azure customer's perspective, MANA preserves Accelerated Networking semantics, but the device the guest OS sees is different. The driver is different. The interface name is different. And that is where Aviatrix gateways run into trouble. Why Aviatrix Gateways Are Affected Aviatrix gateways are not generic VMs. They run a custom data plane that binds tightly to the underlying NIC for performance reasons. Specifically, the gateway image expects the Mellanox driver to be present and operational. On MANA hardware, that driver is no longer in play, and the gateway image does not yet include a MANA-aware driver. Per the field notice, the symptom is intermittent performance degradation rather than an outright outage. That makes it harder to detect: throughput drops, latency spikes, or session resets that look like noise can be the early signs of a gate

Introduction Website hosting has been greatly improved through the use of cloud computing. This has made it easier for individuals as well as businesses to access, scale, and use hosting services at a reasonable price. One of the easiest and most efficient methods that developers may use to host a static site on Microsoft Azure is the Static Website Hosting option offered by Azure Blob Storage, which allows developers to upload their static files without the need for a traditional web or virtual server (i.e., HTML, CSS, JavaScript, and media files). In this project, I will download a free static website template from Tooplate, customize the downloaded version using Visual Studio Code (VS Code), and then deploy the new version to Azure Blob Storage for public viewing. This will involve editing all content, images, styles, and branding so that the template reflects how I want it to look when it is completed. To develop my own static website using Azure Blob Storage, I will first navigate to Tooplate and browse the templates available for modification (or further development). After deciding which template is most suitable, I will download the ZIP package of the template and extract it onto my local computer's disk drive. Then, I will open the ZIP file's contents using VS Code to customize all aspects of the website prior to publishing. After customizing the template, we will create an Azure storage account, enable Static Website Hosting, upload the customized/static website files to the Azure Blob Storage $web container, and then publish the created static website into the cloud. Project Objectives .Download a free static website template from Tooplate. .Go to VS code and edit the downloaded template from there .Create a Resources Group .Create an Azure Storage Account. .Enable Azure Static Website Hosting. .Upload website files to Azure Blob Storage. .Deploy and access the website through Azure's public endpoint. .Document the deployment process. Prerequisites Before

Create a storage account and configure high availability. Create a storage account for the internal private company documents. In the portal, search for and select Storage accounts . Select + Create . Select the Resource group created in the previous lab. Set the Storage account name to private . Add an identifier to the name to ensure the name is unique. Select Review , and then Create the storage account. Wait for the storage account to deploy, and then select Go to resource . This storage requires high availability if there’s a regional outage. Read access in the secondary region is not required. Configure the appropriate level of redundancy . Explanation A storage account is like a digital locker in the cloud. Resource group is a folder that organizes related services. High availability means your files stay safe even if one region (data center area) has problems Configure Redundancy In the storage account, in the Data management section, select the Redundancy blade . Ensure Geo-redundant storage (GRS) is selected. **Refresh **the page. Review the primary and secondary location information. Save your changes. Explanation : Redundancy means keeping copies of your files in multiple places. GRS ensures your files are copied to another region for safety. Create a storage container, upload a file, and restrict access to the file. Create a private storage container for the corporate data. In the storage account, in the Data storage section, select the Containers blade. Select + Container . Ensure the Name of the container is private . Ensure the Public access level is Private (no anonymous access). As you have time, review the Advanced settings, but take the defaults. It means: don’t change anything in the Advanced settings unless the lab specifically tells you to. Azure already chooses safe, recommended defaults for you. Select Create . Explanation : A container is like a folder inside your storage account. Setting Public access level to Private means nobody can see

Meta Description: Learn how to build AI agents with persistent memory using Azure AI Foundry Memory Service. A complete developer guide covering concepts, memory types, scope, provisioning, and a full Python implementation with the Foundry Hosted Agent Framework. Persistent Agent Memory with Azure AI Foundry: A Complete Developer Guide Table of Contents Introduction What Is Azure AI Foundry Memory? Memory Types Deep Dive Memory Architecture: How It Really Works Access Patterns: Tool vs. Low-Level API Understanding Scope Hands-On: Provisioning a Memory Store Hands-On: Building the Foundry Hosted Memory Agent Running & Deploying the Agent Security Best Practices Quotas, Limits & Regional Availability Conclusion + Next Steps Introduction Imagine you've just shipped a polished AI assistant for your SaaS product. Users log in, ask questions, and get sharp, helpful responses. The launch goes well. Then the complaints start rolling in. "Why does it keep asking me for my name every single session?" "I told it last week that I'm vegetarian — why is it recommending steak again?" "It feels like talking to someone with amnesia." This is the stateless agent problem — one of the most frustrating gaps between the promise of conversational AI and the lived reality of production deployments. Every conversation starts from a blank slate. The agent has no idea who it is talking to, what that person prefers, or what was discussed yesterday, last week, or a month ago. The result is a user experience that feels hollow and repetitive — the opposite of the intelligent, personalized assistant your users were promised. The solution is persistent memory, and Azure AI Foundry Memory is Microsoft's production-grade answer to exactly this problem. Introduced as part of the Azure AI Foundry platform, the Memory Service gives agents the ability to remember facts across sessions, distill long conversation histories into concise summaries, and retrieve the right context at the right moment — all wit