Agentic Loops for Knowledge Workers

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.

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[upbeat music] All right, friends, quick announcements before we dive in.

First of all, thank you to today's sponsors, KPMG, Blitzy, Harbor, and Hyperagent.

To get an ad-free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts.

To learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai. And if you like Nufar's presentation on this and you want to go deeper into the world of building agents, allow me to recommend our super intelligent executive agent leadership program.

The next cohort is kicking off next week.

It is led by Nufar, and you can find out all about it at training.besuper.ai. Lastly, a note, 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, [chuckles] 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.