Motivated by upcoming photometric and spectroscopic surveys (Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Manuakea Spectroscopic Explorer), we design the statistical proxies to measure the cadence effects on active galactic nuclei (AGN) variability-observables (time-lags, periodicity, and structure-function (SF)). We constructed a multiple-regression model to statistically identify the cadence-formal error pattern knowing AGN time-lags and periodicity from different surveys. We defined the simple metric for the SFs properties, accounting for the observed SFs deviation relative to those obtained from the homogenously-sampled light curves. We tested the regression models on different observing strategies: the optical dataset of long light-curves of eight AGN with peculiarities and the artificial datasets based on several idealized and LSST-like cadences. The SFs metric is assessed on synthetic datasets. The regression models (for both data types) predict similar cadences for time-lags and oscillation detection, whereas for light curves with low variability ($sim 10%$), cadences for oscillation detection differ. For higher variability ($sim20%$), predicted cadences are larger than for $F_{var}sim 10%$. The predicted cadences are decreasing with redshift. SFs with dense and homogenous cadences are more likely to behave similarly. SFs with oscillatory signals are sensitive to the cadences, possibly impacting LSST-like operation strategy. The proposed proxies can help to select spectroscopic and photometric-surveys cadence strategies, and they will be tested further in larger samples of objects.
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