A Failed Call Is a Late Test

Production failures can be converted into regression tests, but relying on this approach alone leaves conversational agents vulnerable because rare, high-cost errors may not appear until many customers have already been affected. Unlike conventional software, agents are stochastic, can fail silently with confident but incorrect responses, and may change behavior because of model, speech, or vendor updates even without a deployment. The discussion argues that testing should begin before launch by generating targeted scenarios from policies, specifications, and business requirements, then use the same written, evidence-based criteria to assess both pre-launch tests and production recordings. It emphasizes that replaying a failed conversation is insufficient unless it is transformed into a durable scenario containing intent, caller conditions, constraints, and verifiable success criteria, while a single successful rerun provides little statistical confidence in a fix. Examples including Air Canada’s chatbot dispute illustrate how policy-grounded testing can detect misleading answers that sentiment monitoring may miss. The proposed two-way testing loop combines specification-led testing, real-channel and repeated execution, production auditing, release gates, and scheduled checks for dependency drift; TestMu AI Voice Agent Testing is presented as a platform designed to support these activities.

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