diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 647eb5a2daa28..f4c3ac970a3ca 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -1104,6 +1104,7 @@ def _aggregate_item_by_item(self, func, *args, **kwargs):
                     # raised in _aggregate_named, handle at higher level
                     #  see test_apply_with_mutated_index
                     raise
+                # otherwise we get here from an AttributeError in _make_wrapper
                 cannot_agg.append(item)
                 continue
 
@@ -1466,7 +1467,8 @@ def _transform_item_by_item(self, obj, wrapper):
                 output[col] = self[col].transform(wrapper)
             except AssertionError:
                 raise
-            except Exception:
+            except TypeError:
+                # e.g. trying to call nanmean with string values
                 pass
             else:
                 inds.append(i)
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 7d1c74e415658..e53e7ffdbf72f 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -641,12 +641,15 @@ def curried(x):
             # if we don't have this method to indicated to aggregate to
             # mark this column as an error
             try:
-                return self._aggregate_item_by_item(name, *args, **kwargs)
+                result = self._aggregate_item_by_item(name, *args, **kwargs)
+                assert self.obj.ndim == 2
+                return result
             except AttributeError:
                 # e.g. SparseArray has no flags attr
                 # FIXME: 'SeriesGroupBy' has no attribute '_aggregate_item_by_item'
                 #  occurs in idxmax() case
                 #  in tests.groupby.test_function.test_non_cython_api
+                assert self.obj.ndim == 1
                 raise ValueError
 
         wrapper.__name__ = name
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 79b51ef57cd37..a34e002e7a144 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -669,13 +669,16 @@ def agg_series(self, obj, func):
         if is_extension_array_dtype(obj.dtype) and obj.dtype.kind != "M":
             # _aggregate_series_fast would raise TypeError when
             #  calling libreduction.Slider
+            # TODO: can we get a performant workaround for EAs backed by ndarray?
             # TODO: is the datetime64tz case supposed to go through here?
             return self._aggregate_series_pure_python(obj, func)
 
+        elif obj.index._has_complex_internals:
+            # MultiIndex; Pre-empt TypeError in _aggregate_series_fast
+            return self._aggregate_series_pure_python(obj, func)
+
         try:
             return self._aggregate_series_fast(obj, func)
-        except AssertionError:
-            raise
         except ValueError as err:
             if "No result." in str(err):
                 # raised in libreduction
@@ -685,12 +688,6 @@ def agg_series(self, obj, func):
                 pass
             else:
                 raise
-        except TypeError as err:
-            if "ndarray" in str(err):
-                # raised in libreduction if obj's values is no ndarray
-                pass
-            else:
-                raise
         return self._aggregate_series_pure_python(obj, func)
 
     def _aggregate_series_fast(self, obj, func):