# Repetitive workflows can become agent environments

Markdown copy of https://realalpha.app/s/repetitive-workflows-can-become-agent-environments/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Alexandr Wang](https://realalpha.app/builders/alexandr-wang/) · Alexandr Wang is CEO of Scale AI. · Dropped out of MIT to co-found Scale AI in 2016; Meta invested $14.3B in it in 2025 and hired him to lead Superintelligence Labs.
- Host: [Garry Tan](https://realalpha.app/people/garry-tan/) · President & CEO, Y Combinator · Co-founded Posterous and Initialized Capital, an early Coinbase backer.
- Host: [Harj Taggar](https://realalpha.app/people/harj-taggar/) · Managing partner, Y Combinator · Co-founded Auctomatic with the Collison brothers and later Triplebyte.
- Host: [Diana Hu](https://realalpha.app/people/diana-hu/) · Managing partner, Y Combinator · CTO of Escher Reality; after Niantic bought it, ran AR for Pokémon GO.
- Host: [Jared Friedman](https://realalpha.app/people/jared-friedman/) · Managing partner, Y Combinator · Co-founded Scribd, which YC funded in 2006; has advised 20+ YC unicorns.
- Show: Lightcone
- Episode: [Alexandr Wang: Building Scale AI, Transforming Work With Agents & Competing With China](https://realalpha.app/v/5noIKN8t69U/), Jun 18, 2025
- Moment: 37:32 to 41:05 of the episode (4 min), playing on https://realalpha.app/s/repetitive-workflows-can-become-agent-environments/; the episode from there: https://realalpha.app/v/5noIKN8t69U/?t=2252

## Quote

> “if you can actually, like, you know, turn existing **human-driven workflows** into environments and data for reinforcement learning, then you have this ability to convert these, like, human workflows”
> — [Alexandr Wang](https://realalpha.app/builders/alexandr-wang/) at [38:32](https://realalpha.app/v/5noIKN8t69U/?t=2312) · Lightcone

## Takeaways

1. Look for repetitive workflows that can be captured as tasks.
2. Agents already support hiring, quality control and analysis.
3. A task, its data and a rubric form a useful environment.

## About this segment

Alexandr Wang describes turning repeatable work into environments and data for reinforcement learning, then deploying agents across company processes. A candidate brief is one concrete example of the task, data and rubric involved.

From “Alexandr Wang: Building Scale AI, Transforming Work With Agents & Competing With China”, Jun 18, 2025. [Watch the full episode](https://realalpha.app/v/5noIKN8t69U/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[37:37](https://realalpha.app/v/5noIKN8t69U/?t=2257) Talking about operations, you clearly are living in the future, which is super cool. I'm sure you're running scale with all these agents and tools already to make it very efficient. Could you share some of the things that you're doing internally as a company and agents you're adopting so you can do more with less people? We saw this early because when the model developers were starting to develop agents and starting to develop using reinforcement learning, like actual reasoning models where the models could actually really do end-to-end workflows, we were responsible for producing a lot of the data sets that enabled the agents to get there.

[38:20](https://realalpha.app/v/5noIKN8t69U/?t=2300) And then we saw just how effective that training process is. I think that like the efficacy of reinforcement learning for for agent deployments is like is pretty insane. So then once we realized that, we realized, like, okay, if you can actually, like, you know, turn existing human-driven workflows into environments and data for reinforcement learning, then you have this ability to convert these, like, human workflows, especially ones where you're, like, okay with some level of faultiness and okay with a certain level of reliability.

[38:58](https://realalpha.app/v/5noIKN8t69U/?t=2338) you can convert those into agentic workflows. So there's all sorts of agent workflows that happen in our hiring processes and happen in our quality control processes and happen to sort of just automate away certain data analyses and data processes, as well as various sales reporting. It's sort of embedded at every major org of the company. And the whole thing is just like mindset. that like, can you identify these like very repetitive human workflows and basically like undergo this process where you convert that into data sets that enable you to build automation tools? What do these data sets actually look like? I mean, for browser uses, like, is it an environment? And then, you know, here's a video of a human being going through this process of like filling out this form and decide like, yes, no on this drop down or something. I mean,

[39:51](https://realalpha.app/v/5noIKN8t69U/?t=2391) you know, what's a concrete example just for the audience? One of the processes that we go through is like you'll take a sort of like full packet of a from a candidate and you'll like want to distill that into like you know a brief of some sort that sort of like gives all the salient details about that candidate for like decision by a sort of like broader committee um and these kinds of cases you know broadly speaking like deep research plus plus kind of things are like the lowest hanging fruit it's just sort of like can you take these processes that like more or less look like you know you have to like click around a bunch of places and pull a bunch of pieces of information and then blend them together and then produce some analysis on top of that.

[40:31](https://realalpha.app/v/5noIKN8t69U/?t=2431) Like that process, that fundamental like information-driven sort of like analysis process is the easiest thing to drive via agentic workloads. And the kinds of data you need are just like, you know, we call them kind of environments, but usually it's just like, what is the task? What is the full sort of like data set that's necessary to conduct that task? And what is like the rubric for how you conduct that effectively?

[40:56](https://realalpha.app/v/5noIKN8t69U/?t=2456) Do you need RL and fine-tuning when, like, prompt engineering and meta-prompting seems so good?