No, don’t change your model architecture.
Based on the suggestion you’ve received initially, you’ve added a classifier head to your autoencoder (while still keeping the LGBM classifier). This new classifier head in the autoencoder will also output the classification (in form of logits or probabilities) for the samples, so both the classifier head as well as the LGBM model are now creating classification predictions for each sample. Did you compare the accuracy of these two classifiers and if so what accuracy did you see?