Sharadh Krishnamurthy

AI Summary

→ WHAT IT COVERS Sharadh Krishnamurthy, engineering manager at Stripe, explains how a 1.5-person team built Kai, Stripe's internal AI agent, in two weeks. Now serving 10,000+ employees with under 10 staff, Kai combines personalized context, project-based governance, a shared skills platform, and agent-ready data infrastructure. → KEY INSIGHTS - **Project-Based Governance:** Structure enterprise AI around "projects" rather than individual sessions. Projects control model selection, tool access, and cost limits for specific teams or workflows. An HR team project, for example, can block agents from writing sensitive data to public documents while still allowing other tools — removing the all-or-nothing permission problem. - **Three-Layer Data Agent Triage:** Build data agents to query in priority order: first, a blessed analytics layer with pre-validated metrics; second, existing dashboards and their underlying queries; third, the raw data catalog. This hierarchy prevents agents from defaulting to brute-force warehouse queries, protecting infrastructure while improving answer quality and reducing token spend. - **Agent-Ready Infrastructure First:** Pre-AI investments in developer experience, data catalogs, and resilient query layers (Stripe uses Trino) directly determine how well agents perform. Agents amplify existing infrastructure quality and failure modes equally — a poorly indexed warehouse becomes catastrophically slow under agent load, while a well-tiered catalog enables accurate, efficient querying. - **Skill Platform Over Skill Builder:** Rather than letting employees create isolated automations, build a shared skill library with retrieval, quality evals, and governance. Stripe maintains roughly 50 high-frequency skills used company-wide, plus 100–150 niche skills for specific teams. Automatic platform-generated improvement suggestions keep shared skills accurate as the library scales past 2,000 entries. - **Human-in-the-Loop via Tool Policies:** Assign tool policies at the project level to require human approval before sensitive actions execute — such as calendar writes or external document creation. This targets friction precisely where risk exists rather than applying blanket restrictions, preventing both rogue agent behavior and the user frustration that leads to unsafe workarounds. → NOTABLE MOMENT When describing agent failures during Kai's early rollout, Krishnamurthy noted that agents nearly took down core Stripe systems on multiple occasions. The team caught each incident in time, but the pattern revealed that agents don't introduce new failure modes — they dramatically amplify the ones already present in existing infrastructure. 💼 SPONSORS [{"name": "DX", "url": "https://getdx.com/howiai"}, {"name": "HyperAgent", "url": "https://hyperagent.com/howiai"}] 🏷️ Enterprise AI, AI Governance, Internal Tooling, Data Agents, Skill Management