The pain point here is real: even frontier models stumble on product-specific workflows, and hand-written evals simply don't scale beyond a handful of critical paths. Turning production traces into skills, evals, and fine-tuning data is the classic flywheel thesis, and CopilotKit deserves credit for closing the loop instead of shipping yet another eval dashboard. The open question is data quality — traces mined from real sessions can encode exactly the failure patterns teams are trying to eliminate, so automated eval generation will live or die on filtering. There's also crowded competition from established observability and eval platforms, where the moat is far from obvious. Still, if the loop works as advertised, this could meaningfully lower the cost of making agents actually deployable.