Azure

Determining how Azure AI Foundry Agents makes decisions is important for troubleshooting and debugging purposes. However, it can get a little complicated when our agents perform complex workflows. Our agents could perform numerous executions, making it difficult to track decisions made by all or them, or some agents may invoke tools, that invoke other tools, which invoke more tools! (And so on and so forth). Tracing our agents helps us see the inputs and outputs involved in a particular agent run, as well as the order in which those agents were invoked. In this blog post, I’ll talk about how tracing agents works, how we can do some simple tracing using the Azure AI Foundry Agents playground, and how we can implement tracing in our pro-code agents using OpenTelemetry. ...

As the technology advances, Generative AI models are becoming powerful enough to operate autonomously to automate tasks. This is improvement on being able to perform simple tasks in “chat” like applications. This allows us to build AI Agents, which are applications that can use generative AI models with contextual data to automate tasks based on user input and the context that they can perceive. In this article, I’ll talk about how we can build AI Agents using Azure AI Agent service. ...

In this article, I’m going to show you how to deploy an AKS Automatic Cluster within your own custom virtual network using Bicep. An Azure Subscription A code editor - I’m using Visual Studio Code! The AZ CLI kubectl A bash shell (VS Code has an integrated terminal, Windows terminal is also pretty neat) If you don’t know what AKS Automatic is, we’ll cover that before we start. We’ll then work through the Bicep code that we need to provision a cluster with our own virtual network. ...

In this article, I’m going to show you how to build an Azure Kubernetes Service lab environment with Bicep. This sample was inspired by this AKS Lab provided by the AKS team! [!NOTE] If you want to see a live demo of this instead, check it out on my YouTube channel! We’re going to build the lab environment step by step using Bicep. Wherever possible, we will avoid creating resources using the AZ CLI, and instead take an opinionated approach to defining our infrastructure declaratively with Bicep instead. We will still need to use the AZ CLI to do some tasks, such as deploying our resources. ...

In order to control traffic flow within our Azure virtual networks, we can use custom routes, and configure the routes to direct traffic through a network virtual appliance. Azure automatically creates a route table for each subnet in our virtual networks, and adds system default routes to the table. We can override these default routes with custom routes and more custom routes to route tables. In this article, we’ll learn how routing in Azure works, how we can use custom routes to override the default routes, before implementing an example of custom routing using Bicep. ...