AI

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. ...

I’ve lived in Melbourne for almost two years now, and throughout that time I’ve been trying to get my head around Australian rules, or AFL. I live pretty close to where Carlton Football Club train, so I’ve decided to support them (or as the Australians say “Go for them” 🤷). The more I learn about the game, the more I’m realizing that I may have set myself up for a lifetime of pain and dissappointment, but being an Arsenal fan since I was 6, I’m more than prepared for it 😂 ...

In my last post on Semantic Kernel, I talked about how we interact with large language models (LLMs) through plugins that run with NLP prompts. We can also create plugins using native code. We can use our code to integrate with LLMs for calculations, data manipulation or any other functionality that our applications require. In this article, I’ll talk about how we can use native functions in the Semantic Kernel SDK, how we can create native function plugins, and how we can combine native functions with prompts. ...

When we use the Semantic Kernel SDK, we use plugins that act as the building blocks for our AI applications. Plugins essentially define the tasks that the kernel should complete, and the kernel interfaces with large language models and run the plugins we define. Plugins can include native code and natural language prompts, allowing us to use generative AI in our application. Plugins give us the flexibility of defining desired behavior in our application, and we can create custom prompt plugins to fine tune our applications precisely as we need to. ...