My voice agent sounded smart until one phone number was transcribed wrong.

The agent sounded good. Natural voice. Good prompt. Nice handoff logic. CRM update worked. Calendar integration worked. Then it heard one phone number wrong and the whole thing became useless. That’s when I realized voice-agent STT should not be judged like normal transcription. The transcript can be “mostly correct” and still fail the workflow. For voice agents, these words matter more than the rest: phone numbers appointment times dates names email addresses prices addresses order IDs “don’t” “not” “actually” “wait” “no, I meant…” Those are the words that change the action. I’m testing this now with HubSpot fields instead of just transcript accuracy. Example scorecard: did the phone number field match? did the appointment date match? did the agent catch the correction? did it ask for confirmation? did CRM update only after confirmation? did the transcript preserve the negation? Smallest AI Pulse is interesting to me here because I’m not evaluating it as “can it write a nice transcript?” I’m evaluating whether a real-time STT layer can capture workflow-critical entities while the call is still happening. For AI voice agents, I think entity accuracy deserves its own benchmark. Not WER. Not vibes. Did the system capture the fields that matter?