Tag Archives: ChatGPT

Talk: Boston Code Camp 38 – Let’s Build a Goal-Oriented AI Agent Using Semantic Kernel

29-Mar-2025

Today I attended and contributed a talk to the Boston Code Camp 38 (yes, impressively, the 38th edition of this event). I made the trip with Maura (she gave a talk combining Cryptocurrency And Agentic AI) and Kevin (he gave a talk on Top 10 AI Security Risks). and we got to hang out with and chat with so many cool people from the Boston technology community.

The description of my Let’s Build a Goal-Oriented AI Agent Using Semantic Kernel talk follows, followed by a couple of relevant links, then the slide deck.

Imagine an AI not limited to answering individual questions or chatting, but actively working towards a goal you’ve assigned to it.

In this session, we’ll explore the building of an AI Agent – an autonomous actor that can execute tasks and achieve objectives on your behalf.

Along the way we will demystify:

1. 🧠 LLMs – What is a Large Language Model (LLM)
2. 📚 Tokens – What is a token and what are its roles
3. 💡 Embeddings – What are embedding models and vectors and what can they do for us
4. 🎯 Prompts – Beyond the basics
5. ⚙️ Tools – How can these be created and accessed using Semantic Kernel
6. 🤖 Agents – Let’s put all these concepts to work!

The end result will be the core (or perhaps ‘kernel’ 😉) of an AI Agent – your virtual coworker willing to handle tasks on your behalf without. It will be built in C# using the open source, cross-platform Semantic Kernel library.

This talk assumes user-level familiarity with LLMs like ChatGPT or Microsoft Copilot and basic prompting. Anything else will be explained.

(stole photo from Robert Hurlbut)

A couple of prominent links from the talk are:

You can find the Fun with Vectors tool here: https://funwithvectors.com/

You can find the OpenAI Tokenizer tool here: https://platform.openai.com/tokenizer

Download the slides here:

BostonCodeCamp38-EssentialAgency-AGoalAndSomeTools-28-Mar-2025_pub Download

Talk: AI for Application Architects

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Last night I was guest speaker at the Boston .NET Architecture community group. I learned they are now 21-year-olds. That’s a long track record! The audience had some insightful questions, which I always appreciate.

My talk focused on the perspective of the application architect – and not the data scientist, for example – in how the process works and what are some areas I would need to dig into.

Here’s the alternative talk description I offered a few days ago:

Interested in understanding how LLMs are created and how they work internally, including all the in-depth data science and machine learning techniques? If so, then this is not that talk. Rather, this talk steps back to treat the LLM as a black box. And then steps further back to treat the LLM as a part of a cohesive system offered over the internet through an API. It is from that perspective that we begin our exploration.

How exactly does an application make use of LLM services? Is this thing secure? Is it private? Am I operating according to Responsible AI principles? (Oh, and what are Responsible AI principles?) Is it accurate? Is it portable? And of course, when does it stop being a Chatbot and start being an Agent?

These are some of the key types of application architecture considerations we will discuss as we start with “the humble chatbot demo” then turn it into an Agent and then see what it would take to put that into production.

The deck is here:

Boston-.NET-Architecture–Architecting-AI-Applications-18-Dec-2024-published Download

The recording is here: https://youtu.be/UJutO4eFLZg