How to catch voice agent regressions before your users do

Voice agents, used in tasks like drive-through orders and patient intake, often face issues when deployed due to the unpredictability of real-world environments, such as noise and accents, which aren't captured in staging tests. To address this, a process using existing logs and AssemblyAI tools has been developed to preemptively identify and solve these issues. This involves a multi-step diagnostic pipeline that retranscribes audio, summarizes interactions, and scores them against a diagnostic rubric to catch problems before users do. By employing Render for task automation, this method effectively surfaces configuration mistakes, such as the misconfiguration of speech turn-taking settings, thus preventing user-reported issues and allowing teams to focus on more complex challenges. This proactive approach offers a scalable solution without the need for a full observability platform, leveraging existing data and a few additional tools to create a self-service diagnostic system.

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