this is a sharp read, and yeah, endpointing was exactly the stage that bit us. one wrong pause gets inherited by every stage after it, so we stopped chasing a single global silence threshold and started tuning it per use case. time to first audio is the number we watch most too, a short acknowledgement or filler token buys a surprising amount of grace before the caller starts talking again.
barge-in is the part we'd underline twice. it demos fine and then real callers talk over the agent constantly, so a lot of the work was cancelling in-flight audio cleanly and telling a real interruption apart from a back-channel "mhm". thanks for the comment, this is the kind we like getting.
Voice is the setting where latency is not a performance metric but a correctness one, because past a certain delay the human starts talking again and now you have an interruption to resolve rather than a slow reply. The number that decides whether an agent sounds human is usually not total round trip either, it is time to first audio - people forgive a slow sentence far more readily than a gap before anything happens. Worth measuring each hop separately, since in a stack like this the surprise is rarely the model: it is endpointing deciding the caller has finished when they merely paused, and every downstream stage inheriting that mistake. Barge-in handling is the other thing that separates demo from production, and it is much harder than the happy path suggests.