Million

Million launched their agent verification MVP in under eight weeks with a deliberately minimal feature set: a single verification pipeline that caught the most critical code defects agents produced, integrated with one popular LLM framework. They shipped with basic logging and no dashboard, forcing early users to parse raw verification outputs. This constraint proved valuable—it revealed exactly which verification signals mattered most to developers before building unnecessary infrastructure. The team deliberately excluded sophisticated UI, multi-agent orchestration, and custom rule engines. This ruthless scope limitation meant they could iterate on core verification accuracy weekly rather than quarterly. Early validation came quickly: three pilot customers immediately reported catching agent hallucinations their existing tools missed, and adoption spread through word-of-mouth within the AI engineering community. However, the spartan approach created friction during onboarding. Several prospects abandoned trials because the product felt unpolished, suggesting Million sacrificed some early revenue for technical clarity. The execution strategy ultimately validated their thesis about verification gaps, but revealed that even technical buyers needed minimal UX scaffolding to adopt novel tools.