Your agent is blindfolded

Talk transcript

1. 0:00

   [upbeat music] Um, hi everyone.

2. 0:16

   So I'm Johan, uh, from Poolside. Uh, if you haven't heard of us, so we are one of the handful of companies, uh, that are making their own, uh, foundational model from scratch, uh, their own, uh, LLM, uh, and coding agents.

3. 0:31

   Uh, check us out if you're, if you're not aware of some cool stuff and, and you should hear more soon. Um, but what I want to talk to about today, uh, is this: you have people seeing AI and using AI and getting vastly different experiences.

4. 0:48

   If you're on Reddit, if you're on Twitter, you're going to see people that say, "Oh, yeah, I'm, I'm never, like, touching code anymore. The AI is the, is doing everything for me.

5. 0:56

   It's fantastic." Uh, and others that say, "What are you talking about? I'm trying it in my production app. Uh, it produces absolute garbage. Uh, what are you guys working on to-do apps?

6. 1:07

   Uh, what are you doing?" And there is multiple ways to try to, to understand what's going on there. One way is to say, "Oh, this guy is a shill from OpenAI trying to sell you AI," uh, or, "This one is an empty that doesn't care about anything, and he's just, like, lying.

7. 1:23

   He, he didn't even try it." Another, um, is to say, well, maybe someone is working on a greenfield app, and that's nice and easy. Uh, whereas someone else is working on brownfield, on a legacy application.

8. 1:36

   That's complicated and agents are not there yet. But personally, I think that doesn't quite hold up. Um, we've seen people using AI in legacy applications with good success. I have myself, so at least on my own experience, I know, uh, that can work.

9. 1:54

   So what's the difference? What's, uh, the difference really between, um, brownfield and greenfield? The difference is that with greenfield, the agent's intuition is correct. The agent's writing the code and expect, you know, if I write the components here, if I write the service, it's gonna work.

10. 2:12

    I, I, I think that's gonna be fine. Uh, and he's right 'cause it has very good intuition. Uh, on brownfield, however, uh, there be dragons. Uh, you're gonna have things that the agent is not expecting.

11. 2:24

    Um, maybe, you know, like dead-ends code that's not used anymore. Uh, thing that it's not aware of in, in different part of the code that it hasn't even looked at.

12. 2:33

    And that's where the big difference between those two is the feedback loop. And everybody has sort of like somewhat talked about it in, in, in the background of the talks we've seen over the past three days, um, but I think that's the difference with getting this result.

13. 2:48

    So you've, you've got your agent that says, "Yeah, I've implemented the new overflow, and it's all working perfectly." What the agent means really is, well, to the best of the capa- of my capabilities, to the best of what you have given me, that sounds like it should work.

14. 3:05

    Maybe the agent was able to verify its work, maybe it wasn't. But as far as it knows, it's working. If you're a skeptic of AI, you're gonna see that first quote, see that it's indeed not working and just says, you know, "I'm a liar, I'm incompetent," or like, "You cannot trust the AI."

15. 3:25

    Uh, and that's, I think, where this cleavage in between those two type of users. Like, the first category is going to see some things. "Oh, yeah, actually, you know what?

16. 3:34

    It's not working, agent. Uh, can you try again? Check those logs or whatever." Uh, whereas those ones are just going to give up. But I think we can make this still better.

17. 3:44

    Um, at Poolside, uh, I've created a little CLI tool called Spoolside. Yeah. Um, might be good at programming and not good at naming things. Um, that basically allows it, uh, to test our applications effectively.

18. 3:59

    Some of the stuff you've already seen, uh, in things like JSTAC, for instance, being able to take screenshots of the applications, uh, being able to take a snapshot, uh, that is to say like a very token-compressed, uh, version of, of what's going on, on a webpage and use it.

19. 4:14

    But our application is not a webpage. It's an extension within VS Code. So already, like, it takes an extra step to get there. Um, but with that, our AI can interface with it just like it would with a normal webpage.

20. 4:29

    But we take it further. Uh, we have thing to extract logs from different services, from the backend, from the frontend, ways to restart different services. Uh, high-level commands. Can you access a specific menu?

21. 4:42

    Can you go to this page? Can you send a message to the agent, wait for it to reply, send another message, upload an image and do that, and it can stack things, uh, like somewhat efficiently, um, a bit like we've talked with, uh, coding tools, uh, this morning.

22. 4:57

    Um, and that's pretty useful 'cause then the agent is able to test what it's doing. If it's working on a bug, it can actually reproduce the bug before it starts working on that, on that.

23. 5:10

    The agents are very eager, "Yeah, I, I know what's going on. You just need a margin there. You just need to go and add this code." But until it reproduces the bug, uh, uh, I don't trust you.

24. 5:21

    And that's the big thing. It's maybe without that, the agent is able to still have good intuition and fix the issues, but I don't trust it, and then I'm gonna have to go and verify it myself, and I'm wasting time, and I cannot then take that agent and start running it overnight, for instance.

25. 5:37

    Uh, this is a first step to trust. And the point of this is not, um, Spoolside. It's not something you're gonna find on GitHub to use for yourself. It's to build your own.

26. 5:48

    I think as engineers- Um, that's on your role. Uh, we are going to have to focus less on the product and more on trying to make the AI work on the product.

27. 6:02

    How can we make it easy for it? Uh, that can be those tools, that can be improving the code base so that it's easier to work on, that can be improving knowledge bases.

28. 6:12

    Uh, you can implement this as a CLI, as a skill, as an MCP. In, in my case, it's a CLI 'cause I like things simple. But, like, there are many different variations of it, and I think it's going to be different from people to people and problem to problem.

29. 6:24

    But yeah, I think in, uh, 2025, we had product engineers, uh, that were, you know, like, very focused on doing everything with the product. But now that AI is getting quite good, you want to focus more on making sure that that velocity is not a trap, that you're not going to multiply errors, uh, or compound errors, that

30. 6:48

    you're going to actually verify what you're doing, making it easy for you to, to verify as well with, like, presenting the work that the AI done and everything around that.

31. 6:58

    And so I think we're all going to become AIX engineers, uh, essentially. And yeah, it's a bit like when you're in an airline, uh, and they say, "Oh, you put your mask on yourself before you feed...

32. 7:12

    You, you put your, your mask on, on your children." It's the same with AI. You need to put the mask on the AI. You need to make sure that it's self-served before you try to work on features.

33. 7:20

    Even if it slows you down right now, it's an investment, uh, that pays off as soon as you start multiplying agents, uh, and running things over time. And that's me done with two minutes to spares.

34. 7:33

    So thank you very much. Uh, any question from anybody?

35. 7:38

    Yes.

36. 7:39

    Thanks for the talk. Um, when you're thinking about what to put in the CLI, it sounds like you have a bunch of base primitives. For example, screenshots, logs, et cetera.

37. 7:48

    Um, where do you draw the line? Or what's your mental model for building those mechanical primitives versus putting together, like, checking in, for example, unit tests or integration tests?

38. 8:00

    Like, how ephemeral are these things relative to actually, at limit, like, checking them in and always running them?

39. 8:07

    I, I, I think they're quite ephemeral in the sense... A- and that, I guess, is a personal preference, but I do feel like automated test can be sometimes a bit too rigid and hard to predict and hard to, like, work over time.

40. 8:21

    So I like something that mimics the way I would test it, like a human would go and test the application. The way, uh, I find those is, um, in the first stage where I work with the AI, um, I draw.

41. 8:34

    Even though, you know, like, I can see that the button is a bit m- to the left, and I want to tell the AI that, what I want is the AI to realize that by itself.

42. 8:43

    So whenever I encounter that sort of problem, I take a step back and try to think on how to make the AI realize the problem by itself. Uh, another thing is retroactive loop.

43. 8:53

    Uh, after you've done that many times, look past over your logs, ask an AI, "Oh, did you notice any issues? Any, like, um, um, any sync?" Or you're doing sleep, for instance.

44. 9:03

    Calling sleep (50) everywhere is a thing that, like, there is something that should be wait for whatever, like a comment that could be there. Uh, is there anything that you keep doing over and over, like running storybooks?

45. 9:16

    Um, and another thing is really think about, like, your own product. If you're making, say, a, a game in Unity, like, uh, do you want an ASCII representation of y- of your 3D world in, uh, for, for your AI?

46. 9:30

    If you're making something with lots of permissions, uh, different logins that your AI can take very easily. It's, it's really on your role to think about all that. That's what I think anyway.

47. 9:39

    And I think we're it for time. Thank you very much. [outro music]