Making Sense of Agentic AI | ThoughtWorks Birgitta Boeckeler

**Birgitta Boeckeler** (0:00)
And I haven't really ever, in a demo video or for myself, seen a tool really solve the original problem. There's something weird going on with these marketing videos where even the use cases that they choose for the video often don't make sense.

**Ben Lloyd Pearson** (0:23)
Hey, everyone, I'm your host, Ben Lloyd Pearson, Director of Developer Experience at Linear B. And today, I'm delighted to be joined by Birgitta Boeckeler. She's the Global Lead for AI Assisted Software Delivery at ThoughtWorks. Birgitta, thank you so much for joining me today.

**Birgitta Boeckeler** (0:38)
No, thanks for having me, hi, Ben.

**Ben Lloyd Pearson** (0:40)
So we're gonna spend a lot of time talking about agentic AI today because it's been a big part of your research in recent months. And one of the things that I sort of stumbled upon while I was learning about some of the work you've been doing is some of these articles or memos, as you've been calling them, that you've been publishing over on Martin Fowler's website. And I've seen, like, it looks like a series of experiments that you've been doing to test out some of the cutting edge of AI. So maybe let's just start there. Like, tell me about, like, what's going on there and, like, the types of experiments that you're doing and what you're finding out from them.

**Birgitta Boeckeler** (1:18)
Yeah, I would say, I mean, first of all, like, I'm, so I'm a developer by trade, right? Developer, architect, you know, practitioner, right? So I'm not an AI or machine learning expert. And the way that I see my role at the moment is that I'm a domain expert at effective software delivery because I've been doing it for over 20 years. And, you know, and it's not just like coding, but it's also like effective teamwork and stuff like that. Right. And so that I see as my domain expertise. And now I'm trying to apply AI to that domain. Right. So how can we use AI to be better at coding, to be better at software delivery, teamwork and so on. Right. So and but of course to do that, I have to understand the technology below the hood, like to a certain extent to understand what are the possibilities and understand if a tool is claiming a certain thing that it can do, you know, do I think that's like viable? Like, do I want to try that or not? Like, or where do I see this going? Where do I see this potential? Right. And that's why one of the things I was trying, of course, like one of the hot topics right now is like, how do you use agents or like agentic applications to help with software delivery as well, right? So, that was something I was trying. But also a lot of the things that I'm trying is not even me, myself trying to build a tool, but just trying to use the tools out there and seeing if I can come up with an example where it's like as realistic as possible workflow that I would usually see on a team. And just like try to stitch those things together and see if it actually gives me value, right? So another one of the things that I wrote about was, I used like an issue and like an open source tool that is actually like a business application tool and it's like very old code base, so you could call it a legacy code base, right? And I was trying to see, okay, how would I usually try to find out how to implement this issue, this ticket, and can AI help me with that, right? So that's kind of like the stuff I'm doing. So I'm experimenting, but I'm also talking to our team. So I work for a consultancy, right? So we have teams in a lot of different domains, in a lot of different situations, a lot of different tech stacks. So I talk to them and what are you using, what's working, what's not working, then I try things, I tell them about it, I talk to our clients. So that's kind of like my role right now. It's a lot of different things and the firehose of AI, like of change in the space is actually still going. So even with the full-time role, it's very hard to keep on top of everything. So nobody has to feel bad when they feel overwhelmed by what's happening.

**Ben Lloyd Pearson** (3:41)
And I love the approach of, you mentioned how you're sort of focused on figuring out what's viable in this day and age, because I think we talk to a lot of companies right now that are just, like I don't really know that anyone has figured out how to adopt GNI yet. They're all, like everyone is experimenting at this point and trying to just learn, like what are the tools that actually were today? How can we implement them in our organization? And I love the sort of the legacy case that you brought up, because that actually is one that I think has potentially a lot of promise, right? But also a lot of interest because there's just a really high demand for something that can help manage that. So yeah, so what have you learned? Like maybe, do you want to dig into that one a little bit? Like what did you learn about as you were exploring generative AI for like a legacy system? What did you uncover from that?