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How does JMP's binomial confidence intervals work on 0 of N data?<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />

In JMP, I've got Pass/Fail data that is 0 failures out of N samples. I analyze the data and have JMP generate the confidence intervals, so for example, 0 failures out of 20 samples will identify a lower confidence interval of 83.9% at a 95% confidence level.

Question: When there are zero failures and/or the Upper CI is 1, does that Lower CI value really represent half of my Alpha?

i.e. in my example, is 83.9% actually the 97.5% confidence level? And therefore, 88.1% is the true 95% confidence level (JMP's calculation of the 90% level)

Or is JMP smart/dumb enough to know that when my distribution butts up against 0 or 1, a single sided confidence interval is appropriate?

Thanks!