Agentic Loops for Knowledge Workers

**Nathaniel Whittemore** (0:00)
Throughout the summer, one of the hot topics among advanced AI users has been the idea of loops or loop engineering. Simply put, the concept is to think about the way that we interact with AI, not as prompting it and telling it what to do, but to setting up the circumstances where the AI or agent can loop over and over again, working to complete a specific task with a measurable output that it can check itself against, running until that task is complete based on that measurable goal. The first place loops took hold was, of course, in software engineering, where the nature of the tasks is fairly definable and success is pretty clear.
Moving loops into knowledge work domains, where sometimes success is less definable, is more of a challenge, but it's not impossible if you have the right tools to design your knowledge work tasks for this type of agentic work. Today's episode is a webinar with Nufar Gaspar where we do exactly that, and that is coming up right now. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
All right, friends, quick announcements before we dive in. I am traveling currently for Labor Day and my birthday. So if something absolutely crazy has happened and you're wondering why the heck you are getting this agentic loops presentation, that is why. Although obviously if there is something big enough, I will pop back in. For now, let's dive into agentic loops for knowledge workers.

**Nufar Gaspar** (1:21)
Today, we will cover, I believe, some very important topics around loops and graphs, and in general, how to utilize the most advanced techniques for getting agents to work autonomously and as a group. I want to hand it over to Nathaniel to set the table stakes as to why we're here today.

**Nathaniel Whittemore** (1:39)
Awesome. So one of the really interesting dynamics right now is, we're pretty well past the point where people hear you say you're vibe coding or doing something with quad code and assume that you're now, all of a sudden, as a knowledge worker, trying to become a software engineer. It's very clear that we're kind of in the phase of actually figuring out how code and software engineering style processes and these set of tools can make their way into other aspects of work and influence how that work gets done. And just a couple of weeks ago, OpenAI dropped these recent usage statistics which show just how dramatically consumption and use of AI has shifted from the assisted to the agentic. So the chart that's on your screen is from that. You can see around April, May, we flipped from majority AI usage in terms of total tokens consumed being in that kind of ChatGPT assisted paradigm to the agentic paradigm.
The notable thing about this is that alongside this more advanced type of usage, you also see the firms and individuals who are using AI in these new agentic ways are pulling away. The space between them and others, at least in terms of tokens consumed, which is obviously a pretty rough metric, but at least by that metric, they are getting farther apart from the average. What's really difficult, and I'm sure a lot of you have felt, is that knowing how to translate these concepts that originate in software engineering to other types of knowledge work can be a difficult process. It can be abstract, it can involve layers of abstraction.
We wanted to put together this webinar because this idea of using agents in loops and no longer prompting but designing loops and things like that, has been part of the buzzy zeitgeist of AI early adopters for a few months now. But I think it's still been remained abstract what it actually means for knowledge workers. That's the goal of this is to bring everyone into new ways of interacting with AI, and I'm excited to see where we go with it.

**Nufar Gaspar** (3:37)
A loop is basically a job and a graph is an organization. That's a quote actually from you, from the podcast. Keep that in mind and we'll walk you through that. But if I need to give you a TLDR of what we're doing in three sentences. So the first thing is that AI tools, the ones that most of you are using, the agentic tools already use loops to do the work behind the scenes. Once you learn to give them a concrete and verifiable end goal, they will keep working until the job is actually done without you nudging them or without you being frustrated by the mediocre result potentially. That's the big promise, okay? And when one loop and one agent stops being enough, you can always compose loops into teams of agents. And that's the whole idea behind the graph engineering noise and the chatter in social. There is substance around that. And this is very important, all of these ideas were born in software engineering. So if you are coming from this background or have computer scientists working for you or company and so on, the graphs are not news to any of them. And AI engineers have been building with these concepts for a couple of years and over the last few months with a lot of focus on specifically on agents. But almost all the practice, as Nathaniel said, is very focused on coding. And we will try to show you the pros and cons, the pitfalls and how best to leverage that to other types of work, namely knowledge work.