At a glance
WHAT IT’S REALLY ABOUT
Inside Laurel’s GitHub-based Company OS powering AI-native execution at scale
- Laurel maintains a companywide GitHub repository structured by function (sales, CS, product, etc.) that stores playbooks, “skill files,” and an ontology of work so teams know exactly which AI-enabled procedure to use when.
- The system tackles the “1% vs 99%” adoption gap by turning elite individuals’ workflows into shared, discoverable skills and delivering them in the tools people already use (Slack/email) rather than separate AI interfaces.
- A practical build path is outlined: start with one tedious workflow automation (e.g., Slack feature-request intake/triage), graduate to playbooks mapped into reusable skills and sub-agents, then consolidate into a routed “mega-agent” and shared company context.
- Culture and governance are treated as core infrastructure: leadership-driven expectations, regular company hackathons, transparency channels (e.g., Devin reviewers), ground rules, and a two-track review system balancing speed with strategy/architecture alignment.
- The AI-native org model shifts roles toward leaner teams and “captain” ownership, where PMs and even CSMs can ship front-end and back-end changes (with code review and testing responsibilities), and hiring focuses on screen-shared proof of AI maturity.
IDEAS WORTH REMEMBERING
5 ideasTreat operating knowledge as code: versioned, searchable, and shared.
Laurel stores function-by-function playbooks and skill files in GitHub, making “how we work” auditable and reusable like software. This creates a single source of truth that can be pulled into AI tools as company context.
The real bottleneck is not AI capability—it’s “what to use when.”
JZ frames adoption as a “1% vs 99%” problem: a few power users tinker while most people are unsure which workflow or prompt applies. A curated OS and just-in-time delivery closes that gap by removing choice overload.
Start with one repeated pain, automate the intake, then scale outward.
Their entry-level example is a Slack workflow that standardizes feature-request details, auto-assigns owners, and creates tickets with SLA expectations. This is a low-friction wedge that proves value before attempting a full OS.
Playbooks only matter if they’re executable—convert them into skills and agents.
A 50+ page GTM/CS playbook becomes practical when decomposed into automatable steps (drafting emails, prospect research, RFP responses) implemented as agents. Adoption improves further when a “mega-agent” routes requests to the right sub-agent.
Deliver workflows where people already work; separate AI tools reduce adoption.
Even small interface switching costs (going to an agent builder) can kill usage. Laurel emphasizes Slack/email delivery and Claude skill files embedded in org context so actions are invoked in-flow, not in a separate destination.
WORDS WORTH SAVING
5 quotesYou got these people who are these 1% AI users. They're highly AI-pilled. And then you have the, you know, 90 to 99% of the rest of the organization who isn't sure what to use when.
— Jiaona Zhang
The big learning that we've had is how do you create a wrap, like a, like a mega agent, something like the f- like a, um, a go-to-market agent that can be called by the sales team at any point, by the success team at any point, and then that agent is able to route the ask, the, the need, or the help to whatever one of these sub-agents that is actually useful.
— Jiaona Zhang
I think the first, uh, advice I'd give is transparency is everything.
— Jiaona Zhang
But I, I really don't believe in this, I think a lot of, quote-unquote, "AI-native companies" are just like, "Roadmaps are gone, plannings are gone, everything is gone." Um, and what I say is, well, if everyone's running in different directions, even if you're running incredibly fast, you're not really gonna get anywhere.
— Jiaona Zhang
The fundamentals and the principles have never changed. In fact, they're even more important than ever before. But the tools and the way you operate, and the way you, um, can blast through the bureaucracy and feel empowered, that's radically changed.
— Jiaona Zhang
High quality AI-generated summary created from speaker-labeled transcript.
