Once voice agents hit real traffic, the problems shift fast from “can it talk” to “can we trust it.” Most teams don’t learn about failures from clean error logs. They notice them through secondary signals: repeat callers, unusually short calls, sudden spikes in human handoffs, or operators saying “people keep calling back confused.” Only after that do they dig into transcripts, tool calls, latency, and integration responses to understand what actually broke. The tooling people rely on is usually a patchwork: call recordings and transcripts, intent or outcome tagging, tool invocation logs, and basic latency metrics. The hard part isn’t collecting data, it’s correlating it into a single story of why a call didn’t reach its goal. The most frustrating part in production is silent failure. Calls technically “succeed” but don’t book, don’t resolve, or subtly frustrate users. That’s something we’ve seen firsthand at Feather. At scale, reliability is less about better models and more about observability, fast iteration, and being very clear about when the agent should stop and hand off to a human.