diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index 5f753041f41..8edac3c8cf1 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -34,6 +34,7 @@ from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_array_ops
+from tensorflow.python.ops import gen_logging_ops
 from tensorflow.python.ops import gen_resource_variable_ops
 from tensorflow.python.ops import gen_state_ops
 from tensorflow.python.ops import math_ops
@@ -150,10 +151,13 @@ def variable_handle_from_shape_and_dtype(
     # When in eager mode, explicitly ensure so here. When in graph mode, it's
     # ensured by always generating different variable names.
     exists = gen_resource_variable_ops.var_is_initialized_op(handle)
-    if exists:
-      raise ValueError("variable object with name '%s' already created. Use "
-                       "get_variable() if reuse is desired." %
-                       shared_name)
+
+    # We create an assert Op instead of checking right away in order to be
+    # compatible with ASYNC execution mode. Further, since not all devices
+    # support string tensors, we encode the assertion string in the Op name
+    gen_logging_ops._assert(  # pylint: disable=protected-access
+        math_ops.logical_not(exists), [exists], name="EagerVariableNameReuse")
+
     with context.graph_mode(), ops.Graph().as_default() as graph:
       h = gen_resource_variable_ops.var_handle_op(shape=shape, dtype=dtype,
                                                   shared_name=shared_name,
@@ -1781,5 +1785,6 @@ def copy_to_graph_uninitialized(var):
   # pylint: enable=protected-access
   return new_variable
 
+ops.NotDifferentiable("Assert")
 ops.NotDifferentiable("VarIsInitializedOp")
 ops.NotDifferentiable("VariableShape")