Building with Agents and Owning the Output: Coding Agents, Product Ownership, and the Modern AI/ML Engineering Workflow
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Hi everybody. Welcome to another episode of Making AA Possible. brought to you by the California Institute of Technology, Center for Technology and Management Education, CTME. Caltech CTME, we are a executive education center at the heart of Caltech campus in Pasadena, bringing world-class professors, researchers from research labs and NASA's Jet Propulsion Laboratory, and industry experts like you see here on this panel today. We host different classes from executives, senior technical leadership, managers, and hands-on AI engineers. systems engineering, systems design, and also project management. But today we're taking a step away from the higher level conversations we have about AI and moving more towards technical.
We have seen in especially 2026 as coding agents have been changing the nature of how AI and machine learning projects are being built. But one element we're noticing is engineers who ship them, are they still in control of what they're building? How do you work with a coding agent without essentially losing the plot to what you're building? When it comes to Cloud Code or OpenAI's Codex, what are the workflows and frameworks that really drive your team? So on this episode, I'm sitting down with Caltech CTV instructor, Dr. Mike France, who's director of AI at Predoc, and Dr. John Chung, a special solutions architect at Databricks. Both of these gentlemen hold PhDs in neuroscience from USC. when I spent both of their careers working on some of the hardest parts of getting machine learning into production.
Um, Mike brings a practitioner's view on how to getting machine learning into production, how to deliberately work with coding agents. And John brings a deep expertise in helping enterprise team build and ship ML solutions. So together we're going to, I don't know, what did we say as, as, as Jenny, we're going to delve into what production looks like, the different workflows we have and how a background in neuroscience helps out. So to start it out, let's, let's start with definitions of, in your view, respectively, what is a coding agent? And either of you can take the ball on this.
Yeah, I'm going to take a really high-level view, and I think a coding agent can be broken down into a large language model. You can think of that as like the brain of the agent that decides what to do, combined with tools, which are things that help it interact with the environment, running in a loop. So you can think of it where the LLM might have some state that's a, there's a history of the conversation. It can inspect that given a goal and decide to apply certain tools. So for example, if you are doing a, like, For example, if you're like planning a road trip or something, you might have a Google Maps tool, you might have a weather tool, you might have a book hotels tool, and your agent can decide to interact with each of those in a specific time to really kind of build out your road trip.
And that's a really, you know, the details of those tools, it's code, it's API calls, but The LLM gets descriptions of how and when to use them. And when you run that in the loop, that becomes something that can really take actions in the real world.
John, John, what's your go-to definition for an agent? How do you think about this?
I think Mike had an awesome description. The only thing I'd add is one of the tools is just compared to normal coding assistants where they mostly just auto-complete and just finish lines of code for you, an agent has one of the tools to be able to run and execute code. And that's really critical to be able to test that and to be able to understand whether it works and then to be able to implement that solution too. And so one of the tools is just being able to look at dependencies, to be able to break features into smaller components, to be able to test that code, and then to be able to iterate on top of that. And I think Mike's kind of point of just about LLMs having that specific reasoning capability to understand the code base, to kind of reason and understand the dependencies between all these different blocks is critical to be able to build many of these software that, you know, or what agents do.
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