A recent study aimed to test the assumption of conditional independence in compositional reliability bounds for multi-agent systems. These bounds are typically obtained by multiplying component reliabilities, a step that relies on the assumption of conditional independence. However, this assumption is often stated but rarely tested. Through an evaluation of 18,000 missions, the researchers found that two instances of one model co-failed on 90.0% of the missions on which either failed (log OR 6.66, 95% CI [6.38, 7.00]; phi 0.916). Furthermore, substituting a different model reduced the association, while substituting a different vendor did not. This error is signed and runs against the operator: positive dependence inflates joint failure above the independence product, so redundancy is over-credited exactly when components share a model. The researchers proved a bootstrap bound on a fitted model's functional loses coverage of the truth as n grows. They provided a finite-sample certificate assuming no dependence structure: a linear program over the joint, over a Bonferroni-Clopper-Pearson box around measured co-execution moments. The certificate is sound, sharp for the information supplied, and monotone in the moment family. Enriching ten moment functionals to fourteen narrows the identified interval by 85.7% and lifts the certified floor from 0.2455 to 0.4116. A companion anytime-valid certificate holds type-I error at 0.0471 under optional stopping. Blogger's Review: This study reveals that the assumption of conditional independence in compositional reliability bounds for multi-agent systems may not always hold, and provides a finite-sample certificate assuming no dependence structure. These findings have significant implications for improving the design and evaluation of multi-agent systems, particularly when components share a model. Future research can further explore the assumption of compositional reliability bounds and the effectiveness of certificates.