Building Durable AI Agents

**SPEAKER_1** (0:02)
Welcome to the Practical AI Podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work and create. Our goal is to help make AI technology practical, productive and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place.
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**Daniel Whitenack** (0:41)
Welcome to another episode of the Practical AI podcast. This is Daniel Whitenack. I'm CEO at Prediction Guard, and really excited for today's episode because it fits right in the theme of our show, which is Practical AI, focusing on some things that are actually useful and practical. Have with us today Hamza Tahir, who is co-founder at ZenML.
And they have a new product, a new project, our Kitaru, which is focused on agents and making agents durable, which is super interesting. And Hamza is joining us today. I think you are out at the AI Engineers World's Fair, right?

**Hamza Tahir** (1:24)
Yeah, I am. It's like 7,000 people, all of our crowd gathered in one big hallway. So it's just fantastic to be in San Francisco when the energy is so high.

**Daniel Whitenack** (1:36)
Yeah, that's awesome. Always inspiring and really cool to see also growth in that from Swix and others who have really built up an amazing community over time, friends of the show. So if you haven't checked it out, go ahead and check out what they're doing over there. But yeah, excited to dig in today, Hamza. Maybe just to set the stage.
I know your co-founder of ZenML is some background with that project and product around ML Ops. Now, you're getting into agent, agentic things. I love your perspective on maybe first off kind of the world that you have been inhabiting around ML and ML pipelines. As now we're all thinking about agents and generative AI and all of these things, like what, from your perspective, before we get into agents specifically, what role does the more traditional ML models, training pipelines, et cetera, play in our world moving forward from your perspective?

**Hamza Tahir** (2:48)
Awesome. That's, I think, a great one to start with. Thank you for inviting me on the show. I appreciate the opportunity.
I co-founded ZenML about five years ago. This was really almost at a point where ML Ops was really reaching fever pitch on, there was all sorts of chatter about how to productionize AI and machine learning workloads. And I had done four or five years of that in my previous job, where I was co-founded another company trying to deploy ML models in disparate compute back ends and all over, especially out of Germany where I'm based.
So that led me to having a framework internally that we used, that you could write workflows and DAGs and you could deploy them on these different back ends. And that turned out to be ZenML, we open sourced it, we got a bit of traction at the beginning and a few projects and revenue and we raised and that's been the story so far. And smack dab in the middle of this, from then to today, we had the agent renaissance and it felt a bit funny because in MLOps, it was like DevOps reinventing itself. And with agents, it's like MLOps reinventing itself. And at the end of the day, it comes down to these very basic principles of how to write good software engineering code that runs non-deterministic code in a way that's safe and reliable and re-triable. So I think if anything, even if you throw away every other tool that we ever used in MLOps, the principles and the learnings that we took from productionizing these applications at scale still translate and are being rediscovered even at the AIE WorldSphere. I sometimes hear docs. I'm like, I seem to remember I've heard this talk before in the MLOps conferences. So yeah, I'm happy to chat deeper if you're interested in a particular.

**Daniel Whitenack** (4:44)
It's interesting, like you're talking about the workflows, DAG pipelines, there was very much this phase, at least this is how it occurred to me. I don't know if everyone had this perspective, but there was this phase with generative AI around workflow automation, and there was this very much workflow focus for some time with things like N8N or whatever.
Those tools are still very useful, of course. But there is this DAG focus, and now it seems like people have thought, well, I remember having Jeffrey from News Research on the show, and he's like, well, with their Hermes agent or whatever, it's like, well, I don't want to impose my workflow into this, but there's still a workflow under the hood. There's decisions made, there's a workflow executed. It's just like you're not defining it. And so from the human perspective, you actually don't see that workflow in a visualized DAG, but it exists under there. Is that partially why you think some of these principles carry over or are reinvented in a new way? Because at the end of the day, there is some flow of things being executed, right?