Agentic engineering raises output without lowering the quality bar

Transcript

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

1. 15:45

   Amazing, okay. So last year you coined the term vibe coding, and today we're in a world that feels a little bit more serious, more organic engineering. What do you think is the difference between the two, and what would you actually call what we're in today? Yeah, so I would say VibeCoding is about raising the floor for everyone in terms of what they can do in software. So the floor rises, everyone can VibeCode anything, and that's amazing, incredible.

2. 16:07

   But then I would say agentic engineering is about preserving the quality bar of what existed before in professional software. So you're not allowed to introduce vulnerabilities due to VibeCoding. You're still responsible for your software, just as before, but can you go faster? And spoiler is, you can, but how do you do that properly? And so to me, agentic engineering, I call it that because I do think it's kind of like an engineering discipline.

3. 16:30

   You have these agents, which are these like spiky entities. They're a bit fallible, a little bit stochastic, but they are extremely powerful. And it's how do you coordinate them to go faster without sacrificing your quality bar? And doing that well and correctly is the realm of agentic engineering. So I kind of see them as different. Like one is about maybe raising the floor and the other is about extrapolating.

4. 16:54

   And what I'm seeing, I think, is there is a very high ceiling on agentic engineer capability. And, you know, people used to talk about the 10x engineer previously. I think that this is magnified a lot more. 10x is not the speed up you gain. And I think it does seem to me like people who are very good at this peak a lot more than 10x from my perspective right now. I really like that framing. One thing that when Sam Altman came to AI Ascent last year, one memorable thing he said was that people of different generations use ChatGPT differently.

5. 17:28

   So if you're in your 30s, you use it as. as a Google search replacement. But if you're in your teens, ChatGPT is your gateway to the internet. What is the parallel here in coding today? If we were to watch two people code using OpenClaw, CloudCode, CodeX, one you'd consider mediocre at it, and one you would consider fully AI native, how would you describe the difference? I mean, I think it's just trying to get the most out of the tools that are available, utilizing all of their features, investing into your own kind of setup.

6. 18:00

   So just like previously, all the engineers are used to basically getting the most out of the tools you use. Either it's Vim or VS Code, or now it's, you know, ClothCode or Codex or so on. So just investing into your setup and utilizing a lot of the, you know, tools that are available to you. And I think it just kind of looks like that. I do think that maybe related thought is a lot of people are maybe hiring for this, right?

7. 18:29

   because they want to hire strong agentic engineers. I do think that what I'm seeing is that the, you know, most people have still not refactored their hiring process for agentic engineer capability, right? Like, if you're giving out puzzles to solve, then this is still the old paradigm. I would say that hiring has to look like, give me a really big project and see someone implement that big project.

8. 18:53

   Like, let's write, say, a Twitter clone for agents and then make it really good, make it really secure, and then have some agents simulate some activity on this Twitter. And then I'm going to use 10 codecs, 5.4x high, to try to break your website that you deployed. And they're going to try to basically break it, and they should not be able to break it. And so maybe it looks like that, right? And so, yeah, watching people in that setting and building bigger projects and utilizing the tooling