“I'd say like probably 25% of the remaining usage is what you could call like a simple agent. Which is probably, you know, a prompt plus some tools. At least one or perhaps the only tool is a rag type of tool, and it is kind of like an enhanced, you know, chatbot or whatever that interacts with someone. Then I'd say probably the remaining 25% are what I would say are like advanced agents, which are things that maybe run for a long period of time or have a loop or, you know, do something more than that sort of simple but effective paradigm.”
quote is from the automated transcript, cleaned for reading:
filler sounds and stutters are removed, nothing is rephrased. names can be misheard
(the analysis reads context, assessments check outside sources). how →
More from Ankur Goyal
Opinion
Goyal: Running LLM workloads at scale is impractical outside OpenAI
“It's just not practical outside of OpenAI to run use cases at scale in a lot of cases. Like, you can do it, but it requires quite a bit of work. And
Because OpenAI is so good at making their models so available, I think they get a lot of credit for the science…”
Ankur GoyalOct 11, 2024▶ 1:27:25Production AI Engineering starts with Evals
PredictionNot checkable as stated
Goyal: Agent control flow and graph routing will move into models
“It feels very clear to me that this type of logic is going to be built into the model. Anytime there is control flow complexity or uncertainty complexity, I think the history of AI has been to push more and more into the model.”
Ankur GoyalOct 11, 2024▶ 1:33:10Production AI Engineering starts with Evals
PredictionNot checkable as stated
Goyal: OpenAI o1 will make agentic frameworks obsolete
“And I think O-one is going to do that to agentic frameworks as well. Hey, I think To me, it seems very unlikely that the, you know, you and me sort of like sipping an espresso and thinking about how, like, different personified roles of people should interact …”
Ankur GoyalOct 11, 2024▶ 1:34:00Production AI Engineering starts with Evals
Opinion
Goyal: Open-source inference providers are far less reliable than OpenAI
“They are nowhere near as reliable as, I mean, every single time I use any of those products and run a benchmark, I find a bug, text the CEO, and they fix something. It's nowhere near where OpenAI is.”
Ankur GoyalOct 11, 2024▶ 1:37:24Production AI Engineering starts with Evals
Insight
Goyal: Publishing Public Benchmarks Is Marketing, Not Product Improvement
“It's just that the value proposition of publishing an eval is completely orthogonal to the value proposition of building evals in service of building a good product. I think the purpose of publishing benchmarks is marketing, and it's good marketing.”
Goyal: Providing eval criteria and examples is more effective than writing specs
“In many ways coming to the table of product building with representative examples and criteria that articulate what good versus bad is for a use case is just a more precise and usable form of product management than writing a spec.”
This entire site, over 200 episodes transcribed, diarized, checked and made playable,
runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the
moments worth sharing, cuts them, captions them, and reframes them for every feed.
We use essential cookies to make the site work. With your permission we
also use analytics cookies (Google Analytics and Mixpanel) to understand
usage and improve StarZero. See our Cookie Policy.