Nufar Gaspar
speaker
2,488 appearances
11 recordings
1 series
first heard Oct 2025
last heard 3 Sep
Nufar Gaspar’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 11 in all, peaking in Jun 2026 with 5.
Appearances
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.
And I wanna hand it over to Nathaniel to set the table stakes as to why we are here today.
All right.
So 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 like 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 uh loops to do the work behind the scenes.
And 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 in your 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.
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