Videos so9l_MwS2yg
Your Attention Is the Bottleneck, Not Your Agents — Zack Proser, WorkOS
Scene timeline
68 shot(s).
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What was stored
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Provenance
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done | — | 2026-08-10 14:54 | 2m 05s |
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done | — | 2026-08-10 14:59 | 14s |
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done | — | 2026-08-10 19:51 | 6s |
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Transcript
264 cues· 5,125 words· 26,790 chars
- 0:14 Hey, everyone.
- 0:15 I'm Zach.
- 0:15 I work at Work OS.
- 0:16 Thanks for coming.
- 0:17 Work OS is provides drop in API's that allow you to take your software and go upmarket and sell larger deals to enterprises.
- 0:26 But what I'm going to talk about now is sort of the way that I'm finding to try and maintain balance with all the insane new tools that we're getting every day.
- 0:35 So show of hands if anyone is AI coding with agents lately and feels a little bit like this and despite getting more done than ever before, you're completely fried at the end of the day and adrenaline dumping constantly.
- 0:48 So this has been my experience and I've noticed that some of the worst of it is the context switching was always super expensive for me and now it's worse than ever before.
- 0:56 So I think a lot of us are feeling this way
- 0:59 Like the tools are like insanely powerful and our skills are more in demand than ever before and yet we're like exhausted by 11 a.m.
- 1:07 So I'll make this concrete with a recent story.
- 1:10 I was working, I'm on the applied AI team at WorkOS.
- 1:12 I was building a Slack bot that kind of democratized uniform blogging for everybody so that anybody that, even if they've never written a blog post before, can come into a Slack channel, make a simple request, and get a uniform blog post that does everything the correct way, right?
- 1:27 And there was a bug that one of my colleagues reported that said, hey, we need to use sentence case, and the sentence case pass right now is mangling some of our acronyms like SCIM and SSO.
- 1:37 So normally, I'd be living in that kind of window hell that I just showed you.
- 1:41 And instead, this time, I made a very minor change that ended up being super impactful.
- 1:46 So I gave Cloud Code, which is my current preferred aperture for working, the ability to read and write to Slack as well.
- 1:53 And it already had my linear ticket access.
- 1:55 And so I told it, you need to fix this.
- 1:58 And then you also need to verify your own work.
- 2:00 And don't stop until you've done that.
- 2:03 And so roughly, this is what it looked like.
- 2:04 If my terminals on the left, I ran Claude, and I said, fix the sentence case enforcer.
- 2:08 It's mangling acronyms.
- 2:10 And so it went and did that, and because it had an MCP connection to Slack, it fired it into this blog post channel, and then the blog bot that it's working on, it's sitting in that code base in my working directory, picked it up and ran all the way through and got to the step that was relevant, and then Claude verified the outcome and then said, okay, now I have definitively fixed this bug.
- 2:33 So when I came back to it, I came back to a completed loop that had been fixed, and I didn't have to tell it, hey, this part's still broken.
- 2:42 So that felt incredible, and I think that that's an important part of what we're going to all build into our toolkit, and we already are.
- 2:49 But it also scared me because I realized that there's no ceiling to this, right?
- 2:56 So the tools are nuclear now and our nervous system is still relatively ancient.
- 2:59 And so the thing that I'm thinking about recently is how do I find my own kind of developer balance in this world, right?
- 3:06 So why not just stack that same process?
- 3:09 Why not use that harness and just fix 150 bugs every day at work?
- 3:13 Well, the agents can kind of do that, especially if you give them enough context, if you give them the right verification criteria, if you give them the tools to verify their own work.
- 3:22 But at the end of the day, I can't actually sit on top of all of that and make sure that the quality is there.
- 3:28 and also show up the next day for like another eight hour work session and not be completely destroyed.
- 3:34 So what I think I'm finding, and I think a lot of other folks I've been talking to recently are finding, is that the agents are not the bottleneck now, and I think that's going to increasingly be the case, but we are.
- 3:44 So agents can scale infinitely, especially now that they're made available as of last night via Cloud API.
- 3:50 You can give them verification criteria and the tools that they need in order to match that criteria.
- 3:55 But our attention is still in meat space, if you will, and it still degrades under load.
- 4:00 It's still the hard constraint, essentially.
- 4:03 And this is something that's not just me and not just you, everyone that just raised their hand at the beginning of this talk feeling it.
- 4:09 As Simon Wilson said recently, like last week, that he fires up four parallel agents and he's wiped out by 11 a.m. And so he suggests that all of us finding our own individual balance and sort of our personal limits is now something that we all need to do on our own.
- 4:24 I'm definitely seeing this also on the Applied AI team.
- 4:26 So here's a couple of tips and tricks or things that I've used recently and found success with, and so I'll share them.
- 4:33 Some of them I'm sure you've already seen.
- 4:35 So we essentially need to bring the human developer into balance with this new way of working, because now that we have these hypercharged tools, it's faster than ever to burn yourself out, especially if you just scale linearly in terms of what you're taking on at work and what you're outputting.
- 4:51 And so the way that I'm sort of breaking it down in my mind is that agents are going to do better in terms of infinitely scaling, looping infinitely until they have reached the criteria you've given them.
- 5:02 But we still have judgment, taste, knowing that something is actually solved, knowing that the criterion is actually met in terms of human needs and business needs.
- 5:12 And so this is kind of an early breakdown of the stack that I'm seeing.
- 5:15 So the first I'm calling signal layers, for lack of a better word.
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Chapters
- 0:00 Introduction and the problem of AI developer burnout
- 1:08 A concrete example of using AI agents for bug fixing
- 3:36 The bottleneck: human attention vs. infinite agent scalability
- 5:13 The proposed stack for sustainable AI development
- 6:03 The Signal Layer: Managing notifications and context switching
- 7:04 Voice-first flows for coding efficiency
- 8:15 The Shower Principle and Remote Control for agents
- 11:13 Safety, verification, and testing gates
- 12:05 The new, balanced developer workflow
- 13:22 System self-improvement using conversation history
- 15:31 Holistic well-being (Oura ring integration)
- 17:35 Q&A: Addressing skill development early in a career
- 20:07 Q&A: Managing JSONL history and long-term conversation logs
- 21:30 Q&A: Night shift/background agent execution
- 22:17 Q&A: Voice interaction and audio feedback
- 23:39 Q&A: Handling complex, multi-stack features