Category Archives: Azure OpenAI

GitHub Copilot Agent Mode for the Win: I added a new Tool to MCP Server with Single Prompt

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Along with fellow panelists Jason Haley, Veronika Kolesnikova (the three of us run Boston Azure AI), and Udaiappa Ramachandran (he runs Nashua Cloud .NET & DevBoston), I was part of a Boston Azure AI event to discuss highlights from Microsoft’s 2025 Build conference. I knew a couple of the things I wanted to show off were GitHub Copilot Agent mode and hosting Model Context Protocol (MCP) tools in Azure Functions.

What I didn’t realize at first was that these would be the same demo.

I started with a solid sample C#/.NET MCP server ready to be deployed as an Azure Function (one of several languages offered). The sample implemented a couple of tools and my goal was to implement an additional tool that would accept an IP address and return the country where that IP address is registered. The IP to country code mapping functionality if available as part of Azure Maps.

I started to hand-implement it, then… I decided to see how far GitHub Copilot Agent mode would get me. I’ve used it many times before and it can be helpful, but this ask was tricky. One challenge being that there was IaC in the mix: Bicep files to support the azd up deployment, AVM modules, and many code files implementing the feature set. And MCP is still new. And the MCP support within Azure Functions was newer still.

Give GitHub Copilot Agent a Goal

The first step was to give the GitHub Copilot Agent a goal that matches my needs. In my case, I gave Agent mode this prompt:

The .NET project implements a couple of Model Context Protocol (MCP) tools – a couple for snippets and one that says hello. Add a new MCP tool that accepts an IPv4 IP address and returns the country where that IP address is registered. For example, passing in 8.8.8.8, which is Google’s well-known DNS server address, would return “us” because it is based in the USA. To look up the country of registration, use the Azure Maps API.

And here’s what happened – as told through some screenshots from what scrolled by in the Agent chat pane – in a sequence that took around 12 minutes:

I can see some coding progress along the way:

A couple of times the Agent paused to see if I wanted to continue:

It noticed an error and didn’t stop – it just got busy overcoming it:

It routinely asked for permissions before certain actions:

Again, error identification – then overcoming errors, sometimes by getting more up-to-date information:

Second check to make sure I was comfortable with it continuing – this one around 10 minutes after starting work on the goal:

In total 9 files were changed and 11 edit locations were identified:

Deploy to Azure

Using azd up, get it deployed into Azure.

Add MCP Reference to VS Code

Once up and running, then I installed it in VS Code as a new Tool – first click on the wrench/screwdriver:

Then from the pop-up, scroll the the bottom, then choose + Add More Tools…

Then follow the prompts (and see also instructions in the GitHub repo):

Exercise in VS Code

Now that you’ve added the MCP server (running from an Azure Function) into the MCP host (which is VS Code), you can invoke the MCT tool that accepts an IP and returns a country code:

domain-availability-checker% dig A en.kremlin.ru +short
95.173.136.70
95.173.136.72
95.173.136.71
domain-availability-checker%

Using the first of the three returned IP addresses, I ask within the Agent chat area “where is 95.173.136.70 located?” – assuming that the LLM used by the chat parser will recognize the IP address – and the need for a location – and figure out the right MCT tool to invoke:

I give it one-time permission and it does its thing:

Victory!

Check Code Changes into GitHub

Of course, using GitHub Copilot to generate a commit message:

Done!

Connect with Bill and Boston Azure AI

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

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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_pubDownload

Boston Azure AI: Boston Azure is changing calls signs

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I was in the audience at the Microsoft PDC on Nov 3, 2008 where Windows Azure was unveiled on stage by Ray Ozzie in the conference’s opening keynote. At the 16:45 mark he graciously tipped his hat to Jeff Bezos and the AWS team, then announced Windows Azure – a platform with two services: Azure Storage (Blobs, Tables, Queues) and Cloud Services (Web Roles, Worker Roles) – all with the illusion of infinite scale. Later that same day I got hands-on Windows Azure coding experience in a special booth staffed by Microsoft engineers (and it turns out that the impressive engineer helping me was Sriram Krishnan (@sriramk)). I got to test drive those new super-cool Azure services. From my perspective this was the beginning of the conversation about Platform as a Service (PaaS) in the cloud – and the start of horizontal scale as a mainstream architecture pattern. What an event! Around 15 months from this initial announcement, on Feb 1, 2010, Windows Azure reached “GA” (general availability).

In between the initial announcement in 2008 and the GA date in 2010, Boston Azure was born. On Oct 22, 2009, Boston Azure debuted as the first community group of its kind – the first one dedicated to learning about the Azure platform. As of this writing, it has around 3.500 members according to Meetup.com.

(For a long time after we started hosting events we had people attending other events see our signage and curiously pop their head in to ask “What’s Azure?” When I’d answer “that’s Microsoft’s public cloud platform” they would very often react with a puzzled look and a follow-up question: “Cloud? What’s a cloud?” So yes, those were early days.)

That first event was held at the Microsoft NERD building in Cambridge MA. Mike Werner said a few words, I gave a short talk about cloud benefits and the coming opportunity (and somehow managed to reference “the Internet is … a series of tubes” comment by Alaska Senator Ted Stevens) and Brian Lambert (@softwarenerd) was the featured speaker who talked about queuing patterns in Azure Storage which was part of my journey of getting interested in cloud-relevant patterns (which culminated in me writing a book – Cloud Architecture Patterns – a few years later). Michael Stiefel was Boston Azure’s second-ever speaker.

Heady days from 2008 to 2010!

The times they are a-changin’

“You better start swimmin’ or you’ll sink like a stone, for the times they are a-changin’.” –Bob Dylan (https://www.youtube.com/watch?v=DL_kPNFL3dY and do yourself a favor and check out the movie A Complete Unknown).

A few things have changed since then. The PDC conference is no longer – though content has been subsumed into the Build conference. Windows Azure is now just Azure. There are hundreds of Azure services, not two. And Ray Ozzie is no longer at Microsoft (but has the Blues in the best sense of the word).

And Boston Azure is still at it. We’ve delivered more than 150 free events and still going strong. Now also delivering events virtually since the you-know-what made in-person events so difficult. Back in the early days George Babey and Nazik Huq signed on to help me run things. These days – and for some time now – our leadership team is Jason Haley (@haleyjason), Veronika Kolesnikova (@veronika_dev1), and me.

But technology continues to evolve, and we need to evolve too. Today Artificial Intelligence is playing a role similar to the role played by public cloud platforms back 15 years ago: everything is different so what does that mean? what will come out next? what does this make possible? how can I make use of this? how do I learn this stuff? This is exciting, right??

Changing Call Sign to Boston Azure AI

With no end in sight for AI to slow down, the three of us running Boston Azure – Veronika Kolesnikova (@veronika_dev1), Jason Haley (@haleyjason), and myself (@codingoutloud) – have decided to update our community group for 2025 by changing call signs – Boston Azure is now Boston Azure AI.

We’ve been emphasizing AI topics for a while already. Veronika is a long-time Microsoft MVP for AI, Jason is a long-time Microsoft MVP for Azure who last year was reclassified to the AI category, and myself as a long-time Azure MVP was re-classified last year as Dev Tools (presumably due to giving so many talks on GitHub Copilot, the AI coding assistant, in the prior year), so this emphasis also aligns with where the group’s leadership is spending time. At any rate, this rename should at least help us more clearly communicate to the community what we intend to offer.

Where to Find Boston Azure AI

With the rename, we are retooling some of our properties. Some are new, some are renamed from bostonazure version. You can find us at the following destinations:

  • X/Twitter: 🐦 https://x.com/bostonazureai – renamed, so if you followed before you are still following
  • Bluesky: 🟦 https://bsky.app/profile/bostonazureai.org – yeah, we did the fancy domain version 😉
  • YouTube: 🎥 https://www.youtube.com/@bostonazureai (renamed) – we have more than 50 videos posted
  • GitHub: 🛠️ https://github.com/bostonazureai – created a new GitHub Organization for this and will be migrating over the old content, including the C# + Semantic Kernel + Azure OpenAI hands-on workshop materials shortly (see bottom of this post for more – we are running an event on Jan 31)
  • Meetup: 📅 https://meetup.com/bostonazureai – renamed, so if you were a member before you are still a member
  • Website: 🌐 https://bostonazureai.org – coming soon
  • Email: ✉️ hello@bostonazureai.org – we used a gmail address for the first 15 years, but now we are getting fancy with the bostonazureai.org domain. Hit us up if you want to offer a talk (in person or virtual) or have a suggested topic for us!

And, fittingly, we also have a shiny new logo.

The Boston Azure AI logo shows Boston skyline within a cloud outline and text Boston Azure AI
Boston Azure AI

Hands-on AI Coding Workshop: C#, .NET 9, and Semantic Kernel on Azure OpenAI

In another evolution, Jason Haley and I are experimenting with offering Boston Azure AI in-person hands-on AI coding workshops during the workday. The community events we’ve historically offered have been only nights and weekends – non-working hours. We’ll see how this works out. We have our second such in-person during-the-workday hands-on coding workshop focused on using C#, .NET 9, and Semantic Kernel on Azure OpenAI coming up on Fri Jan 31, 2025 held in Cambridge MA. You can sign up here. Free.

And we have a weekend event on the schedule to participate in Boston Azure edition Boston Azure AI edition of the Global AI Bootcamp in March. You can sign up here. Free.

Buckle in. Looking forward to an exciting next few years!

🤖 ☁️

Talk: Hello Semantic Kernel and Giving your AI a Goal

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At Virtual Boston Azure tonight Jason Haley and I teamed up to talk about ways Gen AI can be integrated with your existing systems. In the case of existing enterprise software systems, many are written in C# and Java, both languages supported by Semantic Kernel. Semantic Kernel also supports Python, which is a great language, but all things being equal using a language and technology stack already familiar to your team is also attractive. So considering a library like Semantic Kernel is a productive angle when looking across the spectrum of AI tools.

Much of my talk was focused on how to use Semantic Kernel (in C# and .NET 8) to give your AI a goal and have it solve it. The deck I presented and a recording of the talk follow. <I will likely update this post to link to code used in demo and as other artifacts become available>

Hello-SemanticKernel-BostonAzure-12-Nov-2024Download

Workshop: AI Mini-Workshop at Boston Azure

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The March 28 Virtual Boston Azure was headlined by Pamela Fox from Microsoft. She explained all about the RAG pattern which is commonly used for building effective applications based on Large Language Models (“LLMs”) and Generative AI (“GenAI”). Pamela shared many superb insights, including lots of depth, while answering a ton of interesting follow-up questions. Was a fantastic talk. Boston Azure has a YouTube channel at youtube.com/bostonazure where you can find recordings of many past events. Pamela Fox’s talk is available there as the 48th video to be posted to the channel.

After Pamela’s talk around 15 people stuck around to participate in our first ever “AI mini-workshop” hands-on experience. The remainder of this post is about that mini-workshop.

The AI mini-workshop was a facilitated hands-on coding experience with the following goals:

1. Demystify Azure OpenAI

As background, OpenAI’s ChatGPT burst onto the scene in November 2022. That led to an explosion of people learning about AI and associated technologies such as “LLMs” which is the common shorthand for Large Language Models.

The vast majority of people interact with LLMs via chat interfaces such as available from OpenAI’s public version of ChatGPT or via Copilot on Microsoft Bing search. There’s also a more integrated programming experience surfaced through GitHub Copilot for use with VS Code and several other popular IDEs.

But what about programming your own solution that uses an LLM? Microsoft has done a great job of providing an enterprise-grade version of the OpenAI LLM as a set of services known as Azure OpenAI.

The first goal of this AI mini-workshop was to demystify this programming experience.

This was accomplished by giving the mini-workshop participants a working C# or Python program that fit on a page. And there are only around 10 lines of meaningful code needed to interact with the AI service. This is NOT that complex.

Creating a production-grade application has additional requirements, but at its core, it is straight-forward to interact with Azure OpenAI service programmatically.

The hoped for “Aha!” moment was this:

Aha #1! I can do this! I can programmatically interact with the Azure OpenAI LLM. It isn’t that mysterious after all.

Aha #2! This is possible without much code! In the Python and C# solutions shared there were only around 10 lines of core code.

2. Understand Some AI Concepts

Part of the mini-workshop exercise was to recognize a hallucination and fix it through some additional grounding using a very simple form of RAG.

The hope here is for some “Aha!” moments:

Aha #3! Here’s a concrete, understandable example of a hallucination!

Aha #4! And here’s a concrete, simple example use of RAG pattern to better ground the AI so that it no longer hallucinates about today’s date! But do note that other hallucinations remain…

3. Wield Great Power

The ability to program a LLM to generate unique content is something that essentially NO DEVELOPER COULD DO, EVER, before the super-powerful LLMs that were developed at costs of hundreds of millions of dollars and democratized by the Microsoft Azure OpenAI services (as well as by OpenAI themselves).

The hands-on AI mini-workshop required either (a) a functional Python 3 environment, or (b) a functional C#/.NET environment – everything else was provided, including sufficient access to the Azure OpenAI LLM service to complete the mini-workshop.

But in the end with very little coding you can get to the 5th Aha! moment which is:

Aha #5! I have at my command capabilities that have not been possible in all of the history of computers. The magic of LLMs available via Azure OpenAI gives me superpowers that we are only in the very beginning of understanding the ways this can be put to use.

The source code for the AI mini-working is available here. Note that the API key has subsequently been rolled (invalidated), but the code works pretty well otherwise. 🙂

My original thinking was to distribute the keys separately (like this). If this was an in-person workshop I would have kept the configuration values separated from the source, but given the added challenge of doing this with an online distributed audience I decided to simplify the mini workshop by included the configuration values directly in the source code. Looking back, I believe it was a good concession for minimizing obstacles to learning. So I’d do it again next time.

Talk: Boston Code Camp 36 – Meet GitHub Copilot, Your AI Coding Assistant!

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23-Mar-2024

Always great to hang out with the greater Boston tech community. Today I attended and contributed a talk to Boston Code Camp 36 (the 36th edition of this event).

I made the trip with Maura (she gave a talk on blockchain). and we met a lot of cool people and had a great time.

I spoke on GitHub Copilot. Much of my talk was demo and discussion – you have to see this in action (or use it) to appreciate what’s happening. I consider this a glimpse into the future – it will surely become then norm to have an AI assistant when programming.

It is fun have one AI 🤖 (GitHub Copilot) help us program another AI 🤖 (Azure OpenAI). 🤖 🤖 🤖 😀 After Copilot Chat was able to explain that Azure OpenAI did not have any notion of “today” we used Copilot to implement a trivial version of RAG to anchor the prompt to the current day.

We saw how the agents like @workspace can explain a body of code and even help us figure out where to implement a new feature (such as the --joke command line param).

Another demo was to get Copilot to write unit tests for me. The demo gods were not helpful 😱 😱 😱 and I ran into an error. I moved on without fixing it since time was short. I diagnosed it later and it turns out I had double-pasted (classic demo failure!) which caused the problem. We did use /tests to create unit tests, which were initially NUnit test, but then we asked Copilot to recast them as xUnit tests, then to more efficiently create test cases using the InlineData attribute to consolidate similar test cases.We didn’t get to run the tests at the end, but hopefully the power of GitHub Copilot in helping to create unit tests came through.

I also had the opportunity to hang out with some smart soon-to-be graduates from my alma mater – University of Massachusetts at Boston (some of them were Rohini Deshmukh, Master’s in Information Technology, Kunal Sahjwani, Master’s in Information Technology, and Shounak Kulkarni, Master’s in Business Analytics). Great to see our profession is in such capable hands from chatting with these very smart and capable technologists, analysts, and future leaders.

Here is the published talk abstract for the talk I delivered – and though much of the session was demos, the PowerPoint deck is attached after the abstract.

Meet GitHub Copilot, Your AI Coding Assistant

Imagine an assistant who anticipates your needs as you code, handling mundane and time-consuming steps, allowing you to focus on more complex challenges (the fun stuff). Allow me to introduce you to GitHub Copilot.

GitHub Copilot is an AI-powered coding assistant that integrates with your developer IDE adding many powerful productivity features. Backed by the same OpenAI Large Language Model (LLM) behind ChatGPT, it has an uncanny ability to suggest code snippets that you were about to type in. But suggesting code snippets is just the beginning.

In this demo-heavy talk, we’ll show usage basics, distinguish scenarios where it excels vs. some it finds challenging, and point out a few common anti-patterns so you can avoid them.

Since it is still early days, big features are still showing up at a fast clip, so we’ll highlight some recent features and some others just emerging. At the end we’ll squeeze in just a bit of prognosticating about what it might mean for the future of software development.

As you’ll learn in more depth during this session, the promise of GitHub Copilot is to help you be more productive – go faster, stay in flow, build even more robust software. We are not fully there but we are on the way. This imperfect tool is still a game changer.

I believe the rise of AI coding assistants is unstoppable at this point – and that’s a good thing. By the end of this session, you might agree. And maybe you’ll join the revolution early.

boston-codecamp-meet-github-copilot-22-mar-2024_pubDownload