Agents 2.0 is a framework that enables self-evolving autonomous language agents by treating the agent pipeline as a computational graph and implementing connectionist learning techniques. It applies backward propagation and gradient-based weight updates using language-based loss, gradients, and weights rather than numerical ones. The framework supports training agent pipelines with carefully designed prompt pipelines, analogous to how neural networks are trained.