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Microsoft now has several ways to build AI agents.

A while back I wrote about building an AI agent server with AG-UI and the Microsoft Agent Framework.

In this post, I want to talk about the Python backend I built for an AG-UI demo project.

Introduction Imagine automating the creation of professional presentations through a coordinated series of intelligent agents.

If you follow me on LinkedIn, you know I have talked about agentic workflows versus agents.

Building an MCP server is really easy.

In previous posts, we discussed multi-agent scenarios, how A2A servers work (here and here) and how to deploy the infrastructure to host a multi-agent application on Azure with Azure Container Apps and AI Foundry.

In previous posts, I discussed multi-agent solutions and the potential use of Google’s Agent2Agent protocol (A2A).

In a previous post we created a simple A2A agent that uses synchronous message exchange.

In the previous post, I talked about options to build multi-agent solutions.

When we meet with customers, the topic of a “multi-agent solution” often comes up.

In this post, I will demonstrate how to build an AI agent system where agents can collaborate in different ways.

Artificial Intelligence (AI) agents have garnered significant attention, with numerous posts discussing them on platforms such as LinkedIn and X/Twitter.

We often get the question to build an assistant based on the content of a website.

Source: Microsoft Azure AI Agents Service simplifies building intelligent agents by combining advanced AI models, tools, and technology from Microsoft, OpenAI, and partners like Meta and Cohere.

In October 2024, OpenAI introduced the Realtime API.

If you are a generative AI developer that works with different LLMs, it can be cumbersome to make sure your code works with your LLM of choice.

In a previous post, we looked at creating a Copilot declarative agent.

If you are a Microsoft 365 Copilot user, you have probably seen that the words “agent” and “Copilot agent” are popping up here and there.

In a previous post, I wrote about Diagrid Catalyst.

Many enterprises have systems in place that take documents, possibly handwritten, that contain data that needs to be extracted.

In the previous post, we discussed Microsoft’s Graph RAG implementation.

Whenever we build applications on top of LLMs such as OpenAI’s gpt-4o, we often use the RAG pattern.

This is an AI-generated article based on a video to transcript generator I created to summarise Microsoft Build sessions.

If you have ever created an application that makes calls to Azure OpenAI models, you know there are limits to the amount of calls you can make per minute.

I have written before about Azure OpenAI on your data.

It’s a common request we hear from clients: “We want a chatbot that can interact with our data in SharePoint!”

A while ago, I wrote about creating your first Prompt Flow in Visual Studio Code.

In a previous post, I discussed the Microsoft Bot Framework SDK that provides a fast track to deploying intelligent bots with the help of the Assistants API.

This post is part of a series of blog posts about the Azure OpenAI Assistants API.

In two previous blog posts, I wrote an introduction to the Azure OpenAI Assistants API and how to work with custom functions.

Introduction In a previous blog post, I wrote an introduction about the Azure OpenAI Assistants API.

Introduction A while ago, I looked at the OpenAI Assistants API.

Update: this post used an older version of Prompt Flow at the time.

In this blog post, we will create a flow with Prompt Flow in Visual Studio Code.

In this post, we will take a look at building an Azure AI Search index with a custom skill.

With the gpt-4-vision-preview model available at OpenAI, it was time to build something with it.

The vector search capability of Azure AI Search became generally available mid November 2023.

A while ago, OpenAI introduced GPTs.

If you have ever tried to build an AI assistant, you know that is not a simple task.

In a previous post, we looked at the basics of deploying a multi-container app that uses Dapr with Radius.

Microsoft recently announced Radius.

When we build LLM applications, there is always a recurring question: “What vector store will we use?”.

In the previous post, we looked at using Azure OpenAI Add your data from the Azure OpenAI Chat Playground.

In a previous post, I talked about the Add your data feature in the Azure OpenAI Chat playground.

In a previous post, we looked at Azure Add your data (preview) that can be configured from the Azure OpenAI Chat playground.

When I talk to customers about Azure OpenAI, I am often asked how to build a chatbot that uses your own data in the simplest way possible while still allowing for some customization.

Introduction If you are a developer who wants to build AI-first apps with natural language processing and large language models, you might be interested in Semantic Kernel (SK), a lightweight and open-source SDK that aims to simplify the integration of AI…

In a previous blog post, we discussed two Python programs, upload_vectors.py and search_vectors.py.

In this blog post, we’ll show you a different approach to searching through a large database of blog posts.