diff --git a/docs/source/conf.py b/docs/source/conf.py
index f0fe26428..33e4e6fbd 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -46,6 +46,8 @@
 #
 needs_sphinx = "1.6"
 
+user_agent = "Mozilla/5.0 (X11; Linux x86_64; rv:25.0) Gecko/20100101 Firefox/25.0 github.com/pytorch/torchx"
+
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
diff --git a/torchx/pipelines/kfp/adapter.py b/torchx/pipelines/kfp/adapter.py
index 42a6a3184..1b1e4457c 100644
--- a/torchx/pipelines/kfp/adapter.py
+++ b/torchx/pipelines/kfp/adapter.py
@@ -97,7 +97,7 @@ def component_from_app(
     component_from_app takes in a TorchX component/AppDef and returns a KFP
     ContainerOp factory. This is equivalent to the
     `kfp.components.load_component_from_*
-    <https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_text>`_
+    <https://kubeflow-pipelines.readthedocs.io/en/1.8.22/source/kfp.components.html#kfp.components.load_component_from_text>`_
     methods.
 
     Args:
diff --git a/torchx/schedulers/aws_batch_scheduler.py b/torchx/schedulers/aws_batch_scheduler.py
index 9699b8ac2..534f2bf04 100644
--- a/torchx/schedulers/aws_batch_scheduler.py
+++ b/torchx/schedulers/aws_batch_scheduler.py
@@ -378,7 +378,7 @@ class AWSBatchScheduler(DockerWorkspaceMixin, Scheduler[AWSBatchOpts]):
     See :py:func:`torchx.specs.parse_mounts` for more info.
 
     For other filesystems such as FSx you can mount them onto the host and bind
-    mount them into your job: https://aws.amazon.com/premiumsupport/knowledge-center/batch-fsx-lustre-file-system-mount/
+    mount them into your job: https://repost.aws/knowledge-center/batch-fsx-lustre-file-system-mount
 
     For Elastic Fabric Adapter (EFA) you'll need to use a device mount to mount
     them into the container: https://docs.aws.amazon.com/batch/latest/userguide/efa.html