Klaviyo CEO：全员L3、Dark Factory与AI代理构建系统

Klaviyo co-founder and co-CEO Andrew Bialecki came to SaaStr AI to walk through how a 2,300-person public company builds AI products. Not the vision deck. The actual build system.

Klaviyo won B2B for e-commerce with one insight: instead of showing merchants how many emails they sent and how many got opened, connect to the cart and to Shopify and show them the campaign made $18,372. Merchants still post those screenshots on LinkedIn years later. Changing what the dashboard reported took Klaviyo to roughly 80% share in the Shopify ecosystem, one of the only IPOs in the 2023 cohort, and the most beloved app I’ve come across in years. Talk to e-commerce merchants and they like a lot of tools. They love Klaviyo.

Now all of it has to be rebuilt for agents.

Klaviyo did $370.6M in Q2 ’26, up 26%, with 205,000+ customers and full-year guidance raised to $1.526B to $1.534B. Composer, the marketing agent Andrew describes below, hit 95,000+ users in its first month, about a quarter of them coming back weekly, with credit consumption growing 30% week over week. The first working prototype of that agent was built over a single weekend by other agents.

Andrew’s Top Takeaways

• Every employee has to be “L3” or they don’t survive this era. Klaviyo defined levels of AI autonomy the way self-driving cars are defined. L1 is using AI to search. L2 is spinning up a session and running an agent. L3 is constantly running multiple sessions or a team of agents. Everyone had to be L3 by the end of June, including PMs, designers, sales and marketing. Everyone commits code, from Andrew down to the summer interns.
• Use teams of agents to build your agents. Klaviyo’s internal system, “Dark Factory,” takes a prompt, acts as the PM, writes the specs, decomposes the problem into engineering subsystems, writes contractual API interfaces between them, then runs subagents against each piece. It builds through the weekend and interrupts with questions when requirements are ambiguous.
• The LLM is a great general athlete. The harness is the coaching. Klaviyo treats the base model like an athletic high schooler who could play any sport well. To make Composer great at marketing specifically, they feed it live signal on how consumers across the entire Klaviyo network are responding, and a “coach” agent scores every proposal on predicted engagement and revenue before it ships.
• Agents are power users on day one, and they sit to the right of your best human users. Software has a power law of user sophistication: a few experts, a long tail of novices who have an hour a week. Agents skip the curve. Onboarding matters less. What matters is what the agent asks you to build next.
• Headless is the default now, so your product is infrastructure. Anything a human used to log into should be treated as infrastructure, which means it needs APIs. Klaviyo is building a path to sign up, configure and pay without ever touching the UI, with a dedicated engineer whose only mission is that experience.

1. The L1/L2/L3 Mandate

The framework is borrowed from levels of driving autonomy, and it applies to every function.

• L1: I use it to search.
• L2: I spin up a session and run an agent.
• L3: I’m constantly running multiple sessions or a team of agents, decomposing a problem into pieces, and validating the output.

Klaviyo told 2,300 people to be at L3 by the end of June. Andrew says there was very little pushback, even though the slope is steep. His argument to the team wasn’t about Klaviyo. It was about them: very few people are going to get to L3 in the next year or two, and if you can put a team of agents to work, decompose a problem, and check and validate the output, you’ll be enormously successful in this next era whether you stay at Klaviyo or not.

The average PM at Klaviyo who was writing wireframes and specs pre-AI now has to hit L3. That’s a job redefinition applied to an entire org at once, not a tooling rollout.

2. Dark Factory: Agents That Build Agents

The name comes from lights-out manufacturing. You keep the lights on in a factory because humans are the ones fixing the machines. Automate enough of it and you turn the lights off, because the machines don’t need them.

Klaviyo started building Dark Factory last fall for a specific reason. Their early agent code looked like most agent code looks: prompts built one at a time, stacked on top of each other, an unmaintainable mess.

The loop works like this. You give Dark Factory a prompt, through a standalone repo or through Slack. It acts as the PM and writes out specifications. It decomposes the problem into engineering subsystems. It writes actual contractual API interfaces between those subsystems. Then subagents build against each contract.

For Composer, that decomposition produced an agent for creative and design that pulls from Canva and Figma and your own assets, an agent for orchestration that decides which segment gets what and when, and an agent for analysis that predicts what the campaign will produce.

Andrew’s strongest recommendation from the session: the clear contracts and interfaces between parts of the build are what make it work. They’re also what makes the human code review survivable at the end, because you’re not reading a tangled mess, you’re reading a system that’s laid out.

• It runs for the whole weekend. Klaviyo reviews progress every Friday, and a new idea doesn’t wait for the next sprint. It goes into Dark Factory Friday afternoon and there’s something to look at Monday. The first Composer prototype was one weekend’s run.
• Human-in-the-loop is continuous, not front-loaded. Instead of an all-in-one plan mode where every decision surfaces up front, Dark Factory raises the flag when it hits something uncertain. Across Friday, Saturday and Sunday you get a stream of small questions: your requirements weren’t clear here, specify this harder. Andrew thinks that’s how software gets built going forward, and it maps to how a real product review works with humans.

3. Tom Brady and the Coaching Layer

Andrew’s mental model for the base model: treat it like a very athletic middle schooler or high schooler. Good at a lot of sports. Not yet great at one.

Tom Brady got drafted to play baseball for the Montreal Expos. He might have had a fine career there. What made him great at football was years of coaching and tailoring: film of Joe Montana, and the specific drills you run to be a great quarterback.

The agent equivalent at Klaviyo is two things layered on the harness.

• A proprietary data feed. Composer gets real-time signal on how consumers across all of Klaviyo’s businesses are responding right now. That’s the film room, and a competitor can’t prompt their way to it.
• A coach that scores the work. Every time Composer proposes a campaign, it checks against a coaching agent that returns a numerical score for predicted engagement and revenue, plus feedback on how to tune it. The agent doesn’t ship its first idea.

An agent product built on a general model with no domain-specific feedback loop is a very athletic high schooler with no coaching staff.

4. Your Agents Are Your Most Advanced Users

Normal software has a power law of user sophistication. A few advanced users you put on a customer panel, and a very long tail of novices. Klaviyo has plenty of customers where one person has an hour or two a week for the software and that’s the whole budget, no matter how good the product is.

Agents break that distribution. They start as power users and, in Andrew’s framing, sit to the right of your best humans.

• Onboarding matters less. Agents onboard themselves off good documentation, plus whatever hinting you provide. They go 0 to 60 much faster than any human user.
• Ask your agent what functionality is holding it back. This is the most specific idea in the session. Klaviyo has been running Composer and asking it what it can’t do.