Business Breakdowns
· David Plon
[00:00:00] S1: So, there's lots of definitions of agentic AI. I think of it essentially as the model has the capability to reason, reflect, take action, reflect on those actions, ultimately in the pursuit of a goal. And there's obviously different levels of scaffolding and constriction around what the model can and can't do. What's really exciting, especially now, is agents have started to work. And interestingly, when we first started Portrait, we tried, and we did actually have an agent that was running using GPT-4, and it was so hard to get to work. And the way you got it to work was you had to be so prescriptive about what the model could and couldn't do. I think our system prompt was 30,000 tokens or something crazy. Really had to limit it because at any given time, the model, if it started going down the wrong direction, it didn't have the ability to self-correct and be thoughtful about what it's learned and how it should adjust its plan. And that required just a ton of prompting. What's cool today – and I think what's happening in software engineering is the leading edge of what's going to eventually happen in investment research and really any other knowledge field – is the models have now become smart enough to do longer-running tasks through using tools and updating its thinking process as it goes. And you're seeing this with things like Claude Code and Codex, where people are leaving these models alone for multiple hours and it is dynamically operating like a software engineer to understand a code base, make changes to it, test things out, see an error, go look at the trace and figure out why there's a problem, make a fix. That type of thinking, it started out really challenging, and that now works. The nice thing about code is it's the perfect environment for these models to figure this type of capability out. Because one, it's all just text files at the end of the day. It's all contained usually within a repo, so all the context is locally available. And it's super verifiable – the code either runs or it doesn't. Investment research is obviously much different. None of those things are true. The context lives in many different forms and very broad, and some is more accessible than others. It's also much more qualitative in terms of output.
· David Plon, Matt Reustle
But the form factor of, "can these models reason iteratively and arrive at complex answers that require on-the-go changes of plans," the answer is yes. And it's just a matter of the engineering work to get those models into the right context, so that they can operate like a junior analyst and, ultimately like a senior analyst doing long-running research. It's definitely still a buzzword, but there's a lot more meat behind that buzzword than there was maybe a year ago. [00:00:00] S0: The point on code – and I often bring up chess – these constrained environments, that are still very complex, but they're constrained by certain things. You tend to see more of a clean iteration cycle and more productivity that comes out of it. And the more complex and adaptive the system, the harder it is to control the evolution. So, it's one that I think will be interesting to see how it all evolves. But this has been fascinating. Thank you for going on some more philosophical tangents in addition to the very tangible use cases. It's been a pleasure, so thank you for sharing the knowledge with us. [00:00:00] S1: Thank you. It's been really fun.