AI has speed up code generation, but the rest of the software development lifecycle has not necessarily kept pace. At DraftKings, as we rolled out AI tooling across our engineering org, we saw pressure move to PR review, testing, integration, and release confidence.
This talk shares where those bottlenecks emerged and takes a closer look at DraftCode, our code review agent. Its first version took a diff and returned generic feedback. That wasn’t useful enough. We then worked with the teams responsible for compliance, security, and platform standards to make their rules available to the agent at the global, tool, team, and service levels. I’ll walk through one concrete example, a security rule that flags hardcoded secrets, show how DraftCode applies it, and how reviewers went from catching these issues by hand to having them flagged automatically.
We also tried to build a complete AI-assisted SDLC loop. That approach saw limited adoption because many teams already had tools in place. That experience changed where we put our platform effort: on problems individual engineering teams could not readily solve themselves.
MLOps World | GenAI Summit 2026 is a two days of case studies, workshops, and expo on taking AI/ML and agentic systems into production – at the Etter-Harbin Alumni photo – full-bleed hero or browse files.
