At a glance
WHAT IT’S REALLY ABOUT
Stripe’s Kai: a governed, context-aware company brain with projects and skills
- Stripe built Kai to scale AI to the entire company with the right governance structures, rather than relying on off-the-shelf tools that don’t match Stripe’s complexity and security needs.
- Kai is context-aware (identity and org chart by default) and can optionally connect to sensitive systems like Drive and Slack with employee-controlled access toggles.
- “Projects” are the central mechanism for enterprise governance, defining intent, tool policies, human-in-the-loop approvals, and model/cost constraints for specific teams or workflows.
- A live demo shows Kai creating and then iterating on a data dashboard using internal skills/tools plus a secure sandbox, highlighting how agents can help non-engineers build “light apps.”
- Stripe attributes strong AI leverage to pre-existing platform investments (DevX and data/analytics foundations) and emphasizes infra hardening to prevent agent-driven failure modes (brute force queries, rogue actions).
IDEAS WORTH REMEMBERING
5 ideasGovernance—not model quality—was the core reason Stripe built Kai.
Stripe didn’t just want “AI access”; it needed enterprise-grade guardrails so employees can use agents safely without thinking about permissions, models, or connectors on every task.
Personalization is opt-in and user-controlled, not an invisible data grab.
Kai starts with basic identity/org-chart context, then can optionally connect to sensitive sources like Drive and Slack. Employees can granularly control and toggle what Kai can see, even session-by-session.
“Projects” are a governance and configuration layer, not just a folder for chats.
Projects act as a shared container for intent + configuration: default models (cost/latency), which skills/tools are in scope, and which actions require confirmation. This enables different safety and capability profiles for different teams (e.g., HR).
Tools + skills + a secure sandbox turn chat into repeatable work execution.
Kai uses tools (capabilities) and skills (packaged workflows over tools) plus a secure cloud sandbox so it can write/run code and manipulate data without running on the user’s laptop or leaking across sessions.
Reliable data agents require a tiered retrieval strategy and resilient data infra.
Stripe’s “Ask Data” approach routes agents through progressively lower-level sources: existing reports/artifacts → blessed analytics layer → data catalog/SQL. This reduces incorrect queries and protects the warehouse from brute-force agent behavior.
WORDS WORTH SAVING
5 quotesAgents are very creative at bringing your infra down.
— Sharadh Krishnamurthy
It turns out that agents just, like, dial up all your failure modes. Like, it just- It just multiplies the amplitude of problems you can get, right?
— Sharadh Krishnamurthy
If you put too much friction in front of people, they're just gonna do unsafe things because that's how humans are, right?
— Sharadh Krishnamurthy
Your data warehouse has to be very resilient to high volume queries because when in doubt, an agent will just brute force it.
— Claire Vo
Double the size of your DevX team. Double the size of your data team.
— Claire Vo
High quality AI-generated summary created from speaker-labeled transcript.
