From tool rollouts to GenAI platform products: Evals, cost, and governance - Eugene Sergueev

Platform engineering serves as the essential framework for scaling GenAI, transforming it from isolated tools into a cohesive, measurable product. Rather than merely accelerating individual coding tasks, organizations must redesign their "factory" workflows to integrate AI natively. This shift requires moving beyond simple tool adoption to building three core primitives: evaluation suites that act as regression gates, persistent company memory that provides synthesized context, and AI gateways that manage governance, security, and costs. By focusing on these pillars, teams can eliminate handoff bottlenecks and improve delivery stability. Ultimately, the competitive advantage lies not in the number of AI tools deployed, but in the ability to embed AI into a repeatable, governed, and value-driven operating model that aligns engineering flow with business outcomes.