One common pitfall is the misuse of in-place operations, which can break the computation graph and result in incorrect gradients. Another common mistake is forgetting to call `backward()` on a tensor to compute gradients, which can lead to unavailable gradients. Additionally, it's important to be mindful of memory usage when using autograd, as keeping unnecessary intermediate tensors can quickly consume memory resources.