In last week blog post, we saw how to extend an AI agents intelligence using function tools. In this post, we see how you build AI agents and advertise them as function tools to other agents .  There...

In an earlier blog post, we took an initial look at the Microsoft Agent Framework in terms of the components that form this new open-source SDK. In this post, we focus on a key component that was mentioned - threads. We see how the Microsoft Agent Framework’s helps you manage conversations using threads. Specifically, the following is covered: foundational concepts creating a conversation without a thread creating a conversation with a thread how to serialise a thread how to deserialise a thread agents and multiple threads threading support and implementation options   Demos and code examples are included.  A video demo is also available. ~ Foundational Concepts Before jumping into some examples, a few important points to note when building agents with the agent framework. State  First, agents are stateless by default.  They don’t maintain sate between interactions. To handle the back and forth between the agent and the human (or another agent

In an earlier blog post, we looked at how you can manage conversations using threads with the Microsoft Agent Framework. In this post, we look at how you can extend an AI agents intelligence using fu...

In my earlier blog post, we introduced the Microsoft Agent Framework. We saw how the new open-source SDK from Microsoft blends the best features from Semantic kernel, Auto Gen and the Process Framework. In this blog post we take an initial first look at the Microsoft agent framework in terms of the components that form this new open-source SDK. Specifically, we cover the following: What is an AI agent Available agent types When to use an AI agent When not to use an AI agent Conversations and threads Agent Function tools Agents as function tools Memory and memory types Middleware Background processing Observability Workflows   This blog post is about the main concepts, components and patterns that are core to shipping AI agents using the Agent Framework. Based on prior project experience, I find some of these components and tooling to be similar to others I've used in the past.  For example, ChatHistory in Bot Framework and Semantic Kernel. Thes

Innovation in recent years in the conversational and agent AI space has been wild. Microsoft have recently shipped a new open-source engine that brings together the best bits from Semantic Kernel, AutoGen and the Process Framework. In this blog post, I pull together some of the recent announcements, what it means for you, and weave in some of my own thoughts. We cover: Microsoft Agent Framework Why Agents Need a New Foundation The Paradox of Choice What this means for Semantic Kernel, Process Framework and AutoGen What This Means for Your Current Project How long will Semantic Kernel Be Support for Microsoft’s Official Position Azure AI Foundry Integration Signals to Pay Attention To The AI Timeline and Looking Ahead   An Example of the Agent Framework in Action is also included.  Let’s dig in. ~ Why Agents Need a New Foundation In these last 18 months, there have been many disconnected SDKs, platforms and approaches when it comes to shipping AI age

I am partnering with Pluralsight again to bring you another new course soon. The course is called : Vector Databases & Embeddings for Developers. This course will come in at roughly 50 minutes.   Key learning objectives will centre around: Fundamentals of embeddings and vector databases. How vector databases serve as external memory for LLMs. Using pre-trained models and APIs to create embeddings   The course will be split over 3 modules. A combination of slides, visuals, code and demo applications will rapidly accelerate your understanding of vector embeddings and databases. Code examples and a sample application will help crystalise your understanding of the theory. The course will give you practical knowledge and show you how to leverage embeddings to create intelligent AI agents. Stay tuned for further information. ~ Other Courses In the meantime, you can checkout my 60 minute course, Developing an Artificial Intelligence Strategy for Your Organ

In part 5 of this mini-series, we bring together all main components to form the end-to-end solution for the AI home security system. We also refine and refactor some of the original Python code. To recap, this home security system uses motion detection, image capture, and AI-powered person recognition to distinguish between known persons (family, friends) and potential intruders. It sends alerts when unknown persons are detected. ~ System Components It’s worth a recap of the hardware and software components that form the solution. Hardware The hardware consists of: Raspberry Pi 4 Camera module PIR motion sensor connected to GPIO Bluetooth speaker for local alerts   An old laptop is also used to host a .NET API. Software Some of the files have been renamed to ease readability, the following software components are used in the solution: audioplayer.py - audio alert functions botmessage.py - Telegram messaging functions ClipServer.py - Python server running C

This is the fourth instalment of a miniseries where you see how to build an end-to-end AI home security and cross platform system. To recap, the main requirements for this system are: Detect motion and capture photo Create message with photo attached Send message to defined Telegram bot Detect who is in the photo, if the person is me, then do not invoke the Telegram bot with a message and image   I recommend reading the following before continuing: Part 1 – Introduction, Hardware, Motion Detection and Telegram Bot Part 2 – Creating a Local CLIP Server for Image Vectorisation Part 3 – Create a Local AI Custom Vision API for Training and Matching Images   In part 4, we extend the existing Telegram bot. It will be extended to allow the management of AI image functionality with the API from part 3, from a cell phone. This means you don’t need to be at a computer to train the AI with new data or to test the image recognition capability. ~ The following detai

This is the third instalment of a miniseries where you will learn how to build an end-to-end AI home security and cross platform system. I recommend reading part 1 and part 2 before reading on. To recap, the main requirements for this system are: Detect motion and capture photo Create message with photo attached Send message to defined Telegram bot Detect who is in the photo, if the person is me, then do not invoke the Telegram bot with a message and image   In part 2, we created a locally running CLIP server.  This provided an API endpoint that could accept a single image. The input image was then converted to vectors.  When in vector format, the image data is much easier to run comparisons against. ~ .NET API Capabilities In this blog post, we implement a .NET API.  The .NET API will leverage the core CLIP functionality but will expose 2 endpoints: /api/image/train /api/image/match   The /train endpoint will let us train the system with

This is the second instalment of a miniseries where I am building an end-to-end AI home security and cross platform system. I recommend reading Part 1 before reading on.  To recap, the main requirements for this system are:   Detect motion Capture photo Create message with photo attached Send message to defined Telegram bot Detect who is in the photo, if the person is me, then do not invoke the Telegram bot with a message and image   In this blog post, we implement core functionality that lets us convert images to vectors using OpenAIs free CLIP SDK and ViT-B-32 model. These can then be used to perform real-time similarity checks against new incoming data. ~ What is CLIP CLIP (Contrastive Language–Image Pre-training) is an AI model developed by OpenAI that can understand the relationship between images and text. CLIP makes it easy for you to encode images into high-dimensional vectors (embeddings). With images in vector format, you can then perform:

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