diff --git a/monai/bundle/__init__.py b/monai/bundle/__init__.py
index fa139bb601..b550dc8c93 100644
--- a/monai/bundle/__init__.py
+++ b/monai/bundle/__init__.py
@@ -24,6 +24,7 @@
     init_bundle,
     load,
     run,
+    run_workflow,
     trt_export,
     verify_metadata,
     verify_net_in_out,
diff --git a/monai/bundle/__main__.py b/monai/bundle/__main__.py
index aa0ed20ef5..e143a5c7ed 100644
--- a/monai/bundle/__main__.py
+++ b/monai/bundle/__main__.py
@@ -11,7 +11,16 @@
 
 from __future__ import annotations
 
-from monai.bundle.scripts import ckpt_export, download, init_bundle, run, trt_export, verify_metadata, verify_net_in_out
+from monai.bundle.scripts import (
+    ckpt_export,
+    download,
+    init_bundle,
+    run,
+    run_workflow,
+    trt_export,
+    verify_metadata,
+    verify_net_in_out,
+)
 
 if __name__ == "__main__":
     from monai.utils import optional_import
diff --git a/monai/bundle/scripts.py b/monai/bundle/scripts.py
index 63898f86e5..29adad970c 100644
--- a/monai/bundle/scripts.py
+++ b/monai/bundle/scripts.py
@@ -18,6 +18,7 @@
 import warnings
 from collections.abc import Mapping, Sequence
 from pathlib import Path
+from pydoc import locate
 from shutil import copyfile
 from textwrap import dedent
 from typing import Any, Callable
@@ -30,7 +31,7 @@
 from monai.bundle.config_item import ConfigComponent
 from monai.bundle.config_parser import ConfigParser
 from monai.bundle.utils import DEFAULT_INFERENCE, DEFAULT_METADATA
-from monai.bundle.workflows import ConfigWorkflow
+from monai.bundle.workflows import BundleWorkflow, ConfigWorkflow
 from monai.config import IgniteInfo, PathLike
 from monai.data import load_net_with_metadata, save_net_with_metadata
 from monai.networks import convert_to_torchscript, convert_to_trt, copy_model_state, get_state_dict, save_state
@@ -635,7 +636,7 @@ def run(
         args_file: a JSON or YAML file to provide default values for `runner_id`, `meta_file`,
             `config_file`, `logging`, and override pairs. so that the command line inputs can be simplified.
         override: id-value pairs to override or add the corresponding config content.
-            e.g. ``--net#input_chns 42``.
+            e.g. ``--net#input_chns 42``, ``--net %/data/other.json#net_arg``.
 
     """
 
@@ -678,6 +679,53 @@ def run(
     workflow.finalize()
 
 
+def run_workflow(workflow: str | BundleWorkflow | None = None, args_file: str | None = None, **kwargs: Any) -> None:
+    """
+    Specify `bundle workflow` to run monai bundle components and workflows.
+    The workflow should be suclass of `BundleWorkflow` and be available to import.
+    It can be MONAI existing bundle workflows or user customized workflows.
+
+    Typical usage examples:
+
+    .. code-block:: bash
+
+        # Execute this module as a CLI entry with default ConfigWorkflow:
+        python -m monai.bundle run_workflow --meta_file <meta path> --config_file <config path>
+
+        # Set the workflow to other customized BundleWorkflow subclass:
+        python -m monai.bundle run_workflow --workflow CustomizedWorkflow ...
+
+    Args:
+        workflow: specified bundle workflow name, should be a string or class, default to "ConfigWorkflow".
+        args_file: a JSON or YAML file to provide default values for this API.
+            so that the command line inputs can be simplified.
+        kwargs: arguments to instantiate the workflow class.
+
+    """
+
+    _args = _update_args(args=args_file, workflow=workflow, **kwargs)
+    _log_input_summary(tag="run", args=_args)
+    (workflow_name,) = _pop_args(_args, workflow=ConfigWorkflow)  # the default workflow name is "ConfigWorkflow"
+    if isinstance(workflow_name, str):
+        workflow_class, has_built_in = optional_import("monai.bundle", name=str(workflow_name))  # search built-in
+        if not has_built_in:
+            workflow_class = locate(str(workflow_name))  # search dotted path
+        if workflow_class is None:
+            raise ValueError(f"cannot locate specified workflow class: {workflow_name}.")
+    elif issubclass(workflow_name, BundleWorkflow):
+        workflow_class = workflow_name
+    else:
+        raise ValueError(
+            "Argument `workflow` must be a bundle workflow class name"
+            f"or subclass of BundleWorkflow, got: {workflow_name}."
+        )
+
+    workflow_ = workflow_class(**_args)
+    workflow_.initialize()
+    workflow_.run()
+    workflow_.finalize()
+
+
 def verify_metadata(
     meta_file: str | Sequence[str] | None = None,
     filepath: PathLike | None = None,
diff --git a/monai/bundle/workflows.py b/monai/bundle/workflows.py
index ace08b3ec8..82bab73fe2 100644
--- a/monai/bundle/workflows.py
+++ b/monai/bundle/workflows.py
@@ -165,7 +165,7 @@ class ConfigWorkflow(BundleWorkflow):
             other unsupported string will raise a ValueError.
             default to `None` for common workflow.
         override: id-value pairs to override or add the corresponding config content.
-            e.g. ``--net#input_chns 42``.
+            e.g. ``--net#input_chns 42``, ``--net %/data/other.json#net_arg``
 
     """
 
diff --git a/tests/nonconfig_workflow.py b/tests/nonconfig_workflow.py
new file mode 100644
index 0000000000..63562af868
--- /dev/null
+++ b/tests/nonconfig_workflow.py
@@ -0,0 +1,127 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import torch
+
+from monai.bundle import BundleWorkflow
+from monai.data import DataLoader, Dataset
+from monai.engines import SupervisedEvaluator
+from monai.inferers import SlidingWindowInferer
+from monai.networks.nets import UNet
+from monai.transforms import (
+    Activationsd,
+    AsDiscreted,
+    Compose,
+    EnsureChannelFirstd,
+    LoadImaged,
+    SaveImaged,
+    ScaleIntensityd,
+)
+from monai.utils import BundleProperty, set_determinism
+
+
+class NonConfigWorkflow(BundleWorkflow):
+    """
+    Test class simulates the bundle workflow defined by Python script directly.
+
+    """
+
+    def __init__(self, filename, output_dir):
+        super().__init__(workflow="inference")
+        self.filename = filename
+        self.output_dir = output_dir
+        self._bundle_root = "will override"
+        self._device = torch.device("cpu")
+        self._network_def = None
+        self._inferer = None
+        self._preprocessing = None
+        self._postprocessing = None
+        self._evaluator = None
+
+    def initialize(self):
+        set_determinism(0)
+        if self._preprocessing is None:
+            self._preprocessing = Compose(
+                [LoadImaged(keys="image"), EnsureChannelFirstd(keys="image"), ScaleIntensityd(keys="image")]
+            )
+        dataset = Dataset(data=[{"image": self.filename}], transform=self._preprocessing)
+        dataloader = DataLoader(dataset, batch_size=1, num_workers=4)
+
+        if self._network_def is None:
+            self._network_def = UNet(
+                spatial_dims=3,
+                in_channels=1,
+                out_channels=2,
+                channels=[2, 2, 4, 8, 4],
+                strides=[2, 2, 2, 2],
+                num_res_units=2,
+                norm="batch",
+            )
+        if self._inferer is None:
+            self._inferer = SlidingWindowInferer(roi_size=(64, 64, 32), sw_batch_size=4, overlap=0.25)
+
+        if self._postprocessing is None:
+            self._postprocessing = Compose(
+                [
+                    Activationsd(keys="pred", softmax=True),
+                    AsDiscreted(keys="pred", argmax=True),
+                    SaveImaged(keys="pred", output_dir=self.output_dir, output_postfix="seg"),
+                ]
+            )
+
+        self._evaluator = SupervisedEvaluator(
+            device=self._device,
+            val_data_loader=dataloader,
+            network=self._network_def.to(self._device),
+            inferer=self._inferer,
+            postprocessing=self._postprocessing,
+            amp=False,
+        )
+
+    def run(self):
+        self._evaluator.run()
+
+    def finalize(self):
+        return True
+
+    def _get_property(self, name, property):
+        if name == "bundle_root":
+            return self._bundle_root
+        if name == "device":
+            return self._device
+        if name == "network_def":
+            return self._network_def
+        if name == "inferer":
+            return self._inferer
+        if name == "preprocessing":
+            return self._preprocessing
+        if name == "postprocessing":
+            return self._postprocessing
+        if property[BundleProperty.REQUIRED]:
+            raise ValueError(f"unsupported property '{name}' is required in the bundle properties.")
+
+    def _set_property(self, name, property, value):
+        if name == "bundle_root":
+            self._bundle_root = value
+        elif name == "device":
+            self._device = value
+        elif name == "network_def":
+            self._network_def = value
+        elif name == "inferer":
+            self._inferer = value
+        elif name == "preprocessing":
+            self._preprocessing = value
+        elif name == "postprocessing":
+            self._postprocessing = value
+        elif property[BundleProperty.REQUIRED]:
+            raise ValueError(f"unsupported property '{name}' is required in the bundle properties.")
diff --git a/tests/test_bundle_workflow.py b/tests/test_bundle_workflow.py
index 948c351a1c..33a3f1d959 100644
--- a/tests/test_bundle_workflow.py
+++ b/tests/test_bundle_workflow.py
@@ -22,22 +22,12 @@
 import torch
 from parameterized import parameterized
 
-from monai.bundle import BundleWorkflow, ConfigWorkflow
-from monai.data import DataLoader, Dataset
-from monai.engines import SupervisedEvaluator
+from monai.bundle import ConfigWorkflow
+from monai.data import Dataset
 from monai.inferers import SimpleInferer, SlidingWindowInferer
 from monai.networks.nets import UNet
-from monai.transforms import (
-    Activationsd,
-    AsDiscreted,
-    Compose,
-    EnsureChannelFirstd,
-    LoadImage,
-    LoadImaged,
-    SaveImaged,
-    ScaleIntensityd,
-)
-from monai.utils import BundleProperty, set_determinism
+from monai.transforms import Compose, LoadImage
+from tests.nonconfig_workflow import NonConfigWorkflow
 
 TEST_CASE_1 = [os.path.join(os.path.dirname(__file__), "testing_data", "inference.json")]
 
@@ -46,103 +36,6 @@
 TEST_CASE_3 = [os.path.join(os.path.dirname(__file__), "testing_data", "config_fl_train.json")]
 
 
-class NonConfigWorkflow(BundleWorkflow):
-    """
-    Test class simulates the bundle workflow defined by Python script directly.
-
-    """
-
-    def __init__(self, filename, output_dir):
-        super().__init__(workflow="inference")
-        self.filename = filename
-        self.output_dir = output_dir
-        self._bundle_root = "will override"
-        self._device = torch.device("cpu")
-        self._network_def = None
-        self._inferer = None
-        self._preprocessing = None
-        self._postprocessing = None
-        self._evaluator = None
-
-    def initialize(self):
-        set_determinism(0)
-        if self._preprocessing is None:
-            self._preprocessing = Compose(
-                [LoadImaged(keys="image"), EnsureChannelFirstd(keys="image"), ScaleIntensityd(keys="image")]
-            )
-        dataset = Dataset(data=[{"image": self.filename}], transform=self._preprocessing)
-        dataloader = DataLoader(dataset, batch_size=1, num_workers=4)
-
-        if self._network_def is None:
-            self._network_def = UNet(
-                spatial_dims=3,
-                in_channels=1,
-                out_channels=2,
-                channels=[2, 2, 4, 8, 4],
-                strides=[2, 2, 2, 2],
-                num_res_units=2,
-                norm="batch",
-            )
-        if self._inferer is None:
-            self._inferer = SlidingWindowInferer(roi_size=(64, 64, 32), sw_batch_size=4, overlap=0.25)
-
-        if self._postprocessing is None:
-            self._postprocessing = Compose(
-                [
-                    Activationsd(keys="pred", softmax=True),
-                    AsDiscreted(keys="pred", argmax=True),
-                    SaveImaged(keys="pred", output_dir=self.output_dir, output_postfix="seg"),
-                ]
-            )
-
-        self._evaluator = SupervisedEvaluator(
-            device=self._device,
-            val_data_loader=dataloader,
-            network=self._network_def.to(self._device),
-            inferer=self._inferer,
-            postprocessing=self._postprocessing,
-            amp=False,
-        )
-
-    def run(self):
-        self._evaluator.run()
-
-    def finalize(self):
-        return True
-
-    def _get_property(self, name, property):
-        if name == "bundle_root":
-            return self._bundle_root
-        if name == "device":
-            return self._device
-        if name == "network_def":
-            return self._network_def
-        if name == "inferer":
-            return self._inferer
-        if name == "preprocessing":
-            return self._preprocessing
-        if name == "postprocessing":
-            return self._postprocessing
-        if property[BundleProperty.REQUIRED]:
-            raise ValueError(f"unsupported property '{name}' is required in the bundle properties.")
-
-    def _set_property(self, name, property, value):
-        if name == "bundle_root":
-            self._bundle_root = value
-        elif name == "device":
-            self._device = value
-        elif name == "network_def":
-            self._network_def = value
-        elif name == "inferer":
-            self._inferer = value
-        elif name == "preprocessing":
-            self._preprocessing = value
-        elif name == "postprocessing":
-            self._postprocessing = value
-        elif property[BundleProperty.REQUIRED]:
-            raise ValueError(f"unsupported property '{name}' is required in the bundle properties.")
-
-
 class TestBundleWorkflow(unittest.TestCase):
     def setUp(self):
         self.data_dir = tempfile.mkdtemp()
diff --git a/tests/test_integration_bundle_run.py b/tests/test_integration_bundle_run.py
index 24c0286133..2018ca801e 100644
--- a/tests/test_integration_bundle_run.py
+++ b/tests/test_integration_bundle_run.py
@@ -62,8 +62,10 @@ def test_tiny(self):
                 },
                 f,
             )
-        cmd = ["coverage", "run", "-m", "monai.bundle", "run", "training", "--config_file", config_file]
-        command_line_tests(cmd)
+        cmd = ["coverage", "run", "-m", "monai.bundle"]
+        # test both CLI entry "run" and "run_workflow"
+        command_line_tests(cmd + ["run", "training", "--config_file", config_file])
+        command_line_tests(cmd + ["run_workflow", "--run_id", "training", "--config_file", config_file])
 
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2])
     def test_shape(self, config_file, expected_shape):
@@ -136,6 +138,18 @@ def test_shape(self, config_file, expected_shape):
         # test the saved execution configs
         self.assertTrue(len(glob(f"{tempdir}/config_*.json")), 2)
 
+    def test_customized_workflow(self):
+        expected_shape = (64, 64, 64)
+        test_image = np.random.rand(*expected_shape)
+        filename = os.path.join(self.data_dir, "image.nii")
+        nib.save(nib.Nifti1Image(test_image, np.eye(4)), filename)
+
+        cmd = "-m fire monai.bundle.scripts run_workflow --workflow tests.nonconfig_workflow.NonConfigWorkflow"
+        cmd += f" --filename {filename} --output_dir {self.data_dir}"
+        command_line_tests(["coverage", "run"] + cmd.split(" "))
+        loader = LoadImage(image_only=True)
+        self.assertTupleEqual(loader(os.path.join(self.data_dir, "image", "image_seg.nii.gz")).shape, expected_shape)
+
 
 if __name__ == "__main__":
     unittest.main()