We investigate the concept of an asymptotic e-process, which is a doubly-indexed stochastic process $(E_{m,n})_{m,n\in\mathbb{N}}$ that possesses, asymptotically for an approximation index $m\to\infty$, the properties of an e-process along a monitoring time index $n$. This constitutes the first in-depth study of this recently introduced concept, which is relevant in asymptotic sequential anytime-valid inference. Our theory is motivated by practical applications in sequential hypothesis testing, in which e-variables and e-processes can only be constructed approximately from observations due to model misspecification or estimation errors. Technically, asymptotic e-processes satisfy an asymptotic version of Ville's inequality, which bounds excursion probabilities of $(E_{m,n})_{m,n\in\mathbb{N}}$ uniformly over $n$ up to a monitoring time horizon $r_m$. We show the necessity of allowing for finite values of $r_m$, recovering truly anytime-valid guarantees asymptotically if $r_m\to\infty$. We derive various properties of asymptotic e-processes, and study their connections to asymptotic supermartingales. We also investigate general methods for their construction such as calibration, the cumulative product of asymptotic e-variables, and the monitoring an of an e-process that depends on an estimated parameter. The latter construction constitutes a generalization of a recent approach within the context of asymptotic post-hoc inference.