An autoencoder is usually built from an encoder and a decoder. By forcing reconstruction through a smaller or otherwise constrained internal representation, it learns which aspects of the input matter most.
That is why autoencoders are useful for compression, denoising, and representation learning. On this blog, they also act as a bridge to later discussions about embeddings and latent structure.