You can smooth without strong assumptions on the functional form of the relationship (you need to assume the relationship is smooth and not too rapidly-varying). The data may go up a little, or up a lot, or stay fairly flat or go down, or oscillate, and a smoother can follow it -- smoothing methods achieve this by being adaptive enough to fit the main trend of the data but not so adaptive that they just interpolate noise.
You can't extrapolate without some pretty strong assumptions because there's no data there to adapt to. The values in this unseen set of values might go up a little, or up a lot, or stay fairly flat or go down, or oscillate ...
... but there's nothing to tell us which it might be (or indeed something else), unless you make suitable assumptions that might give some way of figuring it out and then your out-of-range predictions will rely on those assumptions.
Note that if I had the grey points and any one of the three sets of coloured points a kernel smoother would describe the smooth relationship using all the points very well -- it would fit both halves of the data, and everything would look just great. Now we cut off the coloured part. Which curve might you pick? What about fifty other smooth curves for the second half that I might have used but didn't?
You can modify smoothers in various ways to project, but if you project more than a couple of observations out they'll rely heavily on what, exactly, you do. That really shouldn't be done automatically because you'll need to rely on things like domain knowledge to make sensible choices about constraints (and you'll need some) on what the function can do and how it relates to the trends in the region of x-space that you have.