Inside the AI Agent Stacks at SaaStr, Owner.com & Klaviyo | The Deep Dives From SaaStr AI 2026

Inside the AI Agent Stacks at SaaStr, Owner.com & Klaviyo | SaaStr AI Annual 2026 Final Day

Three companies. Three different stages. Three different markets. But all three have done the same thing in 2025 and 2026: rebuilt their internal operations and their customer-facing products around AI agents, not around humans.

SaaStris a sub-10-person B2B media and events company + $200M VC Fund with 21+ agents in production.Owner.comis the $100M ARR “Shopify for restaurants” growing 1X0% a year — where 83% of new customers start their journey by using an AI product.Klaviyois a top public B2B leader at $1.4B+ in revenue rebuilding its product development process from the inside out.

Here’s what each one is actually running. Not the AI marketing slide. The real stack.

SaaStr: Two Humans, One Dog, 20+ Agents

The full SaaStr go-to-market team is Amelia Lerutte (Chief AI Officer), me, David on sponsor sales, and Ginger the dog. The rest of the work is agents.

Most of them started as something boring. None of them were designed as agents on day one. They became agents through 600 to 1,000 commits each, 7 to 8 commits a day, over a few months.

**10K (AI VP of Marketing).**Built on Replit, first commit January 2026, ~1,000 commits, 18K+ lines of code. Started as a dashboard to stop copy-pasting numbers from Marketo and Salesforce into Notion. Now owns daily revenue tracking, forecasting, campaign performance, and pushes three new marketing ideas per day via Slack and email. Yells at us when we fall behind on the ideas.**QBee (AI VP of Customer Success).**Started as a project management tool to replace an antiquated system. Now manages 150+ sponsors with personalized email outreach, asset collection, and real-time risk flagging. Doesn’t even have full Salesforce integration yet and already outperforms 85% of human CSMs at force-ranking sponsor health.**Annie (event producer agent).**Was just sastranual.com on Squarespace. We rebuilt it on Replit in November 2025. Now has 46K+ lines of code, the most commits of any of our agents, runs the parking pass app, the agenda, the attendee newsletters, and active website visitor targeting.**Amelia AI (Qualified inbound).**The most-trained agent in our stack. 2.2M sessions, 442K chats, 614 booked meetings, ~$85K average sponsor ASP this year. Replaces the 3 BDRs we would otherwise need and never could afford.**Agent Force (dead lead revival).**Runs inside Salesforce. Took on most of the Qualified and Momentum context after Salesforce acquired both. Highest open rate of any of our agents because it has the most context.**Ava / Artisan (warm outbound).**Handles slightly-warm B leads. Past attendees, past sponsors, lapsed contacts. Recovered ~$500K of sponsor revenue this year from leads humans wouldn’t touch.**Monaco (cold ICP look-alikes).**Fills its own funnel. Pulls our close-won history, builds look-alike accounts, books meetings without a human ever touching it. Idles the least of any agent we have.

The connective tissue is headless Salesforce. None of these agents would work the way they do if they had to use the Salesforce UI. They use the API directly, in real time, all the time.

Owner.com: Build the Free AI Product, Then Bundle From There

Adam Gild was on this stage three years ago talking about being a Shopify for restaurants. Adam was on this stage Thursday talking about how Owner is about to cross $100M ARR with 83% of new customers starting their journey by using an AI product. The pivot in early 2023 was the company.

The big bets that worked:

**Gradr (free AI restaurant website generator).**Got 2M+ views on X two weeks ago. Costs Owner ~$1 in compute per restaurant. Free for the first three months, then $1/month. A restaurant owner types in their name. Within 5 minutes the agent has crawled their Google Business Profile, all nearby competitors, every review, done an AI photo shoot of every menu item, upscaled all images, generated motion video with Veo 3, and rebuilt the entire site around what customers actually love about the place. 83% of Owner’s new customer pipeline now starts here.**Owen (internal coordination agent).**Will, the genius builder behind much of Owner’s product, was suffocating under coordination work as the engineering team grew. Owen now listens to GitHub, Slack, Notion, Linear, and Google Meet transcripts. Generates real-time status reports on every project and every engineer. When a designer posts a Slack screenshot of a UI bug, Owen calls Claude Code, finds the relevant codebase, and ships the first-draft PR before any human looks at it.**Product Insight Command Center.**Dean, Owner’s CTO, was burning hours per month interviewing support, sales, and CS to figure out what to ship next. Now an agent pulls real-time signal from Salesforce, Intercom, Momentum, and Talkdesk. Categorizes every support ticket and every sales call. Dean can click into “83 tickets related to delivery issues” and see the exact customer quotes within seconds.**AI-PCR pre-call research + eGPV.**Fires the moment a lead is submitted. Runs the Gradr report on the prospect, pulls the nearest most successful Owner customer as social proof, estimates the restaurant’s gross payments volume within $250. Result: 90%+ increase in rep call volume and meaningful close rate lift.**AI-native finance.**Will, Owner’s CFO, and Meera moved their financial model into Claude. When an investor asks about the rule of 40 comparison between Q3 and Q4 with seasonal GPV adjustments, Adam queries the model directly mid-conversation and has an answer in 10 seconds.**Owner Photographer.**Adam built this in 6 hours on a Saturday after a restaurant owner told him she’d spent $2,000 on a commercial food photo shoot and the new menu items looked terrible on her iPhone. Restaurant owner uploads the iPhone photo, picks the style from their original shoot, the agent calls Nano Banana with anti-uncanny-valley prompts, ships the new image in 30 seconds. Now used by hundreds of customers.

The pattern: Adam personally still ships production code. The CEO leads by example, not by sending Slack threats about “10x productivity or you’re out.”

Klaviyo: Agents Building Agents at Public Company Scale

Andrew Bialecki founded Klaviyo 14 years ago to be the CRM for consumer businesses. They IPO’d post-2021, hit $1.4B+ in revenue, and dominate B2B for e-commerce with 200K+ customers and what may have been the most beloved B2B app of the past decade.

The interesting thing isn’t Klaviyo’s customer-facing AI. It’s how they rebuilt their internal product development process.

**Dark Factory.**Klaviyo’s internal pattern for agents building agents. You give it a prompt. It acts as the PM (decomposes the spec), breaks the problem into engineering subsystems, writes hard API contracts between them, then has sub-agents build each piece in parallel. When it hits ambiguity, it raises a flag for human input rather than guessing. Andrew’s prototype for the customer-facing Composer agent took one weekend through Dark Factory and became the actual shipped product.**Composer (the inside agent).**Klaviyo’s customer-facing marketing AI. Helps merchants figure out what to do next and then does it. The reason it works at all is because Klaviyo gave it real-time feedback from how consumers across all 200K merchants actually react to campaigns. Andrew calls this the “coach.” The LLM is an athletic middle schooler. The proprietary training feedback loop is what makes it elite at the specific sport of marketing automation.**Customer agents (the outside agent).**Every Klaviyo merchant gets their own digital representative. The hard part isn’t building it. The hard part is that SMB merchants don’t have time to train agents themselves. So Klaviyo built agents that train the customer agents automatically. The training agent takes representative use cases from each merchant, decomposes them, builds the workflow, identifies what configuration data it need