  We discuss how to efficiently and reliably estimate the level of agreement
and disagreement on parameter determinations from different experiments, fully
taking into account non-Gaussianities in the parameter posteriors. We develop
two families of scalable algorithms that allow us to perform this type of
calculations in increasing number of dimensions and for different levels of
tensions. One family of algorithms rely on kernel density estimates of
posterior distributions while the other relies on machine learning modeling of
the posterior distribution with normalizing flows. We showcase their
effectiveness and accuracy with a set of benchmark examples and find both
methods agree with each other and the true tension within $0.5\sigma$ in
difficult cases and generally to $0.2\sigma$ or better. This allows us to study
the level of internal agreement between different measurements of the
clustering of cosmological structures from the Dark Energy Survey and their
agreement with measurements of the Cosmic Microwave Background from the Planck
satellite.
