Raxvan is completely right that "traditional" anti aliasing techniques will work in raytracing, including those that use information such as depth to do antialiasing. You could even do temporal anti aliasing in ray tracing for instance.
Julien expanded on Raxvan's 2nd item which was an explanation of super sampling, and showed how you'd actually do that, also mentioning that you can randomize the location of the samples within the pixel but then you are entering signal processing country which is a lot deeper, and it definitely is!
As Julien said, if you want to do $N$ samples per pixel, you can break the pixel up into $N$ evenly distributed sample points (on a grid basically) and average those samples.
If you do that, you can still get aliasing though. It is better than NOT doing it, because you are increasing your sampling rate, so will be able to handle higher frequency data (aka smaller details), but it can still cause aliasing.
If you instead take $N$ random samples within a pixel, you are effectively trading aliasing for noise. Noise is easier on the eyes and looks more natural than aliasing, so is the preferred result usually. I believe it's even provably the ideal situation with higher sample counts but don't have more info on that ):
When you use just "regular" random numbers like you'd get from rand() or std::uniform_int_distribution, that is called "white noise" because it contains all frequencies, like how white light is made up of all other colors (frequencies) of light.
Using white noise to randomize the samples within a pixel has the problem that sometimes your samples will clump together. For instance, if you average 100 samples in a pixel, but they ALL end up being in the upper left corner of the pixel, you aren't going to get ANY information about the other parts of the pixel, so your final resulting pixel color will be missing information about what color it should be.
A better approach is to use something called blue noise which only contains high frequency components (like how blue light is high frequency light).
The benefit of blue noise is that you get even coverage over the pixel, like you get with a uniform sampling grid, but, you still get some randomness, which turns aliasing into noise and gives you a better looking image.
Unfortunately, blue noise can be very costly to compute, and the best methods all seem to be patented (what the heck?!), but one way to do this, invented by pixar (and patented too i think but not 100% sure) is to make an even grid of sample points, then randomly offset each sample point a small amount - like a random amount between plus or minus half the width and height of the sampling grid. This way you get a sort of blue noise sampling for pretty cheap.
Note that this is a form of stratified sampling, and poisson disk sampling is a form of that too, which is also a way of generating blue noise:
If you are interested in going deeper you probably will also want to check out this question and answer!
What is the fundamental reasoning for anti aliasing using multiple random samples within a pixel?
Lastly, this stuff is starting to stray into the realm of monte carlo path tracing which is the common method for doing photorealistic raytracing. if you are interested in learning more about that, give this a read!