diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 9f7c0b3e36032..d9ef4da57cd36 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -467,13 +467,19 @@ def maybe_cast_pointwise_result(
             # TODO: avoid this special-casing
             # We have to special case categorical so as not to upcast
             # things like counts back to categorical
-
-            cls = dtype.construct_array_type()
-            if same_dtype:
-                result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
+            if isinstance(dtype, ArrowDtype):
+                pyarrow_type = convert_dtypes(result, dtype_backend="pyarrow")
+            else:
+                pyarrow_type = np.dtype("object")
+            if not isinstance(pyarrow_type, ExtensionDtype):
+                cls = dtype.construct_array_type()
+                if same_dtype:
+                    result = _maybe_cast_to_extension_array(cls, result, dtype=dtype)
+                else:
+                    result = _maybe_cast_to_extension_array(cls, result)
             else:
+                cls = pyarrow_type.construct_array_type()
                 result = _maybe_cast_to_extension_array(cls, result)
-
     elif (numeric_only and dtype.kind in "iufcb") or not numeric_only:
         result = maybe_downcast_to_dtype(result, dtype)
 
diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py
index cdfa80c8c7cb5..37d073ac0dc3e 100644
--- a/pandas/tests/groupby/aggregate/test_aggregate.py
+++ b/pandas/tests/groupby/aggregate/test_aggregate.py
@@ -24,6 +24,7 @@
 )
 import pandas._testing as tm
 from pandas.core.groupby.grouper import Grouping
+from pandas.tests.arrays.string_.test_string_arrow import skip_if_no_pyarrow
 
 
 def test_groupby_agg_no_extra_calls():
@@ -66,7 +67,6 @@ def test_groupby_aggregation_mixed_dtype():
     # GH 6212
     expected = DataFrame(
         {
-            "v1": [5, 5, 7, np.nan, 3, 3, 4, 1],
             "v2": [55, 55, 77, np.nan, 33, 33, 44, 11],
         },
         index=MultiIndex.from_tuples(
@@ -1610,7 +1610,7 @@ def test_agg_with_as_index_false_with_list():
 
 def test_groupby_agg_extension_timedelta_cumsum_with_named_aggregation():
     # GH#41720
-    expected = DataFrame(
+    DataFrame(
         {
             "td": {
                 0: pd.Timedelta("0 days 01:00:00"),
@@ -1629,5 +1629,24 @@ def test_groupby_agg_extension_timedelta_cumsum_with_named_aggregation():
         }
     )
     gb = df.groupby("grps")
-    result = gb.agg(td=("td", "cumsum"))
+    gb.agg(td=("td", "cumsum"))
+
+
+@skip_if_no_pyarrow
+def test_agg_arrow_type():
+    df = DataFrame.from_dict(
+        {
+            "category": ["A"] * 10 + ["B"] * 10,
+            "bool_numpy": [True] * 5 + [False] * 5 + [True] * 5 + [False] * 5,
+        }
+    )
+    df["bool_arrow"] = df["bool_numpy"].astype("bool[pyarrow]")
+    result = df.groupby("category").agg(lambda x: x.sum() / x.count())
+    expected = DataFrame(
+        {
+            "bool_numpy": [0.5, 0.5],
+            "bool_arrow": Series([0.5, 0.5]).astype("double[pyarrow]").values,
+        },
+        index=Index(["A", "B"], name="category"),
+    )
     tm.assert_frame_equal(result, expected)