diff --git a/README.md b/README.md
index f5a045b..e03d6df 100644
--- a/README.md
+++ b/README.md
@@ -9,25 +9,27 @@ High Availability (HA) DAG Utility
 
 ## Overview
 
-This library provides an operator called `HighAvailabilityOperator`, which inherits from `PythonSensor` and does the following:
+This library provides an operator called `HighAvailabilityOperator`, which inherits from `PythonSensor` and runs a user-provided `python_callable`.
+The return value can trigger the following actions:
 
-- runs a user-provided `python_callable` as a sensor
-  - if this returns `"done"`, mark the DAG as passed and finish
-  - if this returns `"running"`, keep checking
-  - if this returns `"failed"`, mark the DAG as failed and re-run
-- if the sensor times out, mark the DAG as passed and re-run
+| Return | Result | Current DAGrun End State |
+| :----- | :----- | :----------------------- |
+| `(Result.PASS, Action.RETRIGGER)` | Retrigger the same DAG to run again          | `pass` |
+| `(Result.PASS, Action.STOP)`      | Finish the DAG, until its next scheduled run | `pass` |
+| `(Result.FAIL, Action.RETRIGGER)` | Retrigger the same DAG to run again          | `fail` |
+| `(Result.FAIL, Action.STOP)`      | Finish the DAG, until its next scheduled run | `fail` |
+| `(*, Action.RETRIGGER)`           | Continue to run the Sensor                   | N/A |
+| `(Result.PASS, Action.RETRIGGER)` | Retrigger the same dag to run again | `pass` |
+| `(Result.PASS, Action.RETRIGGER)` | Retrigger the same dag to run again | `pass` |
+| `(Result.PASS, Action.RETRIGGER)` | Retrigger the same dag to run again | `pass` |
 
-Consider the following DAG:
-
-```python
-from datetime import datetime, timedelta
-from random import choice
+Note: if the sensor times out, the behavior matches `(Result.PASS, Action.RETRIGGER)`.
 
-from airflow import DAG
-from airflow.operators.python import PythonOperator
-from airflow_ha import HighAvailabilityOperator
+### Example - Always On
 
+Consider the following DAG:
 
+```python
 with DAG(
     dag_id="test-high-availability",
     description="Test HA Operator",
@@ -39,20 +41,32 @@ with DAG(
         task_id="ha",
         timeout=30,
         poke_interval=5,
-        python_callable=lambda **kwargs: choice(("done", "failed", "running", ""))
+        python_callable=lambda **kwargs: choice(
+            (
+                (Result.PASS, Action.CONTINUE),
+                (Result.PASS, Action.RETRIGGER),
+                (Result.PASS, Action.STOP),
+                (Result.FAIL, Action.CONTINUE),
+                (Result.FAIL, Action.RETRIGGER),
+                (Result.FAIL, Action.STOP),
+            )
+        ),
     )
     
     pre = PythonOperator(task_id="pre", python_callable=lambda **kwargs: "test")
     pre >> ha
     
-    fail = PythonOperator(task_id="fail", python_callable=lambda **kwargs: "test")
-    ha.failed >> fail
+    retrigger_fail = PythonOperator(task_id="retrigger_fail", python_callable=lambda **kwargs: "test")
+    ha.retrigger_fail >> retrigger_fail
+
+    stop_fail = PythonOperator(task_id="stop_fail", python_callable=lambda **kwargs: "test")
+    ha.stop_fail >> stop_fail
     
-    passed = PythonOperator(task_id="passed", python_callable=lambda **kwargs: "test")
-    ha.passed >> passed
+    retrigger_pass = PythonOperator(task_id="retrigger_pass", python_callable=lambda **kwargs: "test")
+    ha.retrigger_pass >> retrigger_pass
 
-    done = PythonOperator(task_id="done", python_callable=lambda **kwargs: "test")
-    ha.done >> done
+    stop_pass = PythonOperator(task_id="stop_pass", python_callable=lambda **kwargs: "test")
+    ha.stop_pass >> stop_pass
 ```
 
 This produces a DAG with the following topology:
@@ -60,12 +74,54 @@ This produces a DAG with the following topology:
 <img src="https://raw.githubusercontent.com/airflow-laminar/airflow-ha/main/docs/src/top.png" />
 
 This DAG exhibits cool behavior.
-If a check fails or the interval elapses, the DAG will re-trigger itself.
-If the check passes, the DAG will finish.
+If the check returns `CONTINUE`, the DAG will continue to run the sensor.
+If the check returns `RETRIGGER` or the interval elapses, the DAG will re-trigger itself and finish.
+If the check returns `STOP`, the DAG will finish and not retrigger itself. 
+If the check returns `PASS`, the current DAG run will end in a successful state.
+If the check returns `FAIL`, the current DAG run will end in a failed state.
+
 This allows the one to build "always-on" DAGs without having individual long blocking tasks.
 
 This library is used to build [airflow-supervisor](https://github.com/airflow-laminar/airflow-supervisor), which uses [supervisor](http://supervisord.org) as a process-monitor while checking and restarting jobs via `airflow-ha`.
 
+### Example - Recursive
+
+You can also use this library to build recursive DAGs - or "Cyclic DAGs", despite the oxymoronic name.
+
+The following code makes a DAG that triggers itself with some decrementing counter, starting with value 3:
+
+```python
+
+with DAG(
+    dag_id="test-ha-counter",
+    description="Test HA Countdown",
+    schedule=timedelta(days=1),
+    start_date=datetime(2024, 1, 1),
+    catchup=False,
+):
+    
+    def _get_count(**kwargs):
+        # The default is 3
+        return kwargs['dag_run'].conf.get('counter', 3) - 1
+
+    get_count = PythonOperator(task_id="get-count", python_callable=_get_count)
+
+    def _keep_counting(**kwargs):
+        count = kwargs["task_instance"].xcom_pull(key="return_value", task_ids="get-count")
+        return (Result.PASS, Action.RETRIGGER) if count > 0 else (Result.PASS, Action.STOP) if count == 0 else (Result.FAIL, Action.STOP)
+
+    keep_counting = HighAvailabilityOperator(
+        task_id="ha",
+        timeout=30,
+        poke_interval=5,
+        python_callable=_keep_counting,
+        pass_trigger_kwargs={"conf": '''{"counter": {{ ti.xcom_pull(key="return_value", task_ids="get-count") }}}'''},
+    )
+
+    get_count >> keep_counting
+```
+<img src="https://raw.githubusercontent.com/airflow-laminar/airflow-ha/main/docs/src/rec.png" />
+
 ## License
 
 This software is licensed under the Apache 2.0 license. See the [LICENSE](LICENSE) file for details.
diff --git a/airflow_ha/__init__.py b/airflow_ha/__init__.py
index 0e37288..3b326f1 100644
--- a/airflow_ha/__init__.py
+++ b/airflow_ha/__init__.py
@@ -1,3 +1,3 @@
 __version__ = "0.1.0"
 
-from .operator import HighAvailabilityOperator
+from .operator import *
diff --git a/airflow_ha/operator.py b/airflow_ha/operator.py
index 395d689..36067c3 100644
--- a/airflow_ha/operator.py
+++ b/airflow_ha/operator.py
@@ -1,4 +1,5 @@
-from typing import Literal
+from enum import Enum
+from typing import Any, Callable, Dict, Optional, Tuple
 
 from airflow.models.operator import Operator
 from airflow.exceptions import AirflowSkipException, AirflowFailException
@@ -6,14 +7,26 @@
 from airflow.operators.trigger_dagrun import TriggerDagRunOperator
 from airflow.sensors.python import PythonSensor
 
-__all__ = ("HighAvailabilityOperator",)
+__all__ = (
+    "HighAvailabilityOperator",
+    "Result",
+    "Action",
+    "CheckResult",
+)
 
 
-CheckResult = Literal[
-    "done",
-    "running",
-    "failed",
-]
+class Result(str, Enum):
+    PASS = "pass"
+    FAIL = "fail"
+
+
+class Action(str, Enum):
+    CONTINUE = "continue"
+    RETRIGGER = "retrigger"
+    STOP = "stop"
+
+
+CheckResult = Tuple[Result, Action]
 
 
 def skip_():
@@ -28,93 +41,90 @@ def pass_():
     pass
 
 
-class HighAvailabilityOperator(PythonSensor):
+class HighAvailabilityOperatorMixin:
     _decide_task: BranchPythonOperator
-
-    _end_fail: Operator
-    _end_pass: Operator
-
-    _loop_pass: Operator
-    _loop_fail: Operator
-
-    _done_task: Operator
-    _end_task: Operator
-    _running_task: Operator
-    _failed_task: Operator
-    _kill_task: Operator
-
-    _cleanup_task: Operator
-    _loop_task: Operator
-    _restart_task: Operator
-
-    def __init__(self, **kwargs) -> None:
+    _fail: Operator
+    _retrigger_fail: Operator
+    _retrigger_pass: Operator
+    _stop_pass: Operator
+    _stop_fail: Operator
+    _sensor_failed_task: Operator
+
+    def __init__(
+        self,
+        python_callable: Callable[..., CheckResult],
+        pass_trigger_kwargs: Optional[Dict[str, Any]] = None,
+        fail_trigger_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> None:
         """The HighAvailabilityOperator is an Airflow Meta-Operator for long-running or "always-on" tasks.
 
         It resembles a BranchPythonOperator with the following predefined set of outcomes:
 
-                       /-> "done"    -> Done -> EndPass
-        check -> decide -> "running" -> Loop -> EndPass
-                       \-> "failed"  -> Loop -> EndFail
-           \-------------> failed    -> Loop -> EndPass
-
-        Given a check, there are four outcomes:
-        - The tasks finished/exited cleanly, and thus the DAG should terminate cleanly
-        - The tasks finished/exited uncleanly, in which case the DAG should restart
-        - The tasks did not finish, but the end time has been reached anyway, so the DAG should terminate cleanly
-        - The tasks did not finish, but we've reached an interval and should loop and rerun the DAG
-
-        The last case is particularly important when DAGs have a max run time, e.g. on AWS MWAA where DAGs
-        cannot run for longer than 12 hours at a time and so must be "restarted".
-
-        Additionally, there is a "KillTask" to force kill the DAG.
+        check -> decide -> PASS/RETRIGGER
+                        -> PASS/STOP
+                        -> FAIL/RETRIGGER
+                        -> FAIL/STOP
+                        -> */CONTINUE
 
         Any setup should be state-aware (e.g. don't just start a process, check if it is currently started first).
         """
-        python_callable = kwargs.pop("python_callable")
+        pass_trigger_kwargs = pass_trigger_kwargs or {}
+        fail_trigger_kwargs = fail_trigger_kwargs or {}
 
         def _callable_wrapper(**kwargs):
             task_instance = kwargs["task_instance"]
             ret: CheckResult = python_callable(**kwargs)
-            if ret == "done":
-                task_instance.xcom_push(key="return_value", value="done")
-                # finish
-                return True
-            elif ret == "failed":
-                task_instance.xcom_push(key="return_value", value="failed")
-                # finish
-                return True
-            elif ret == "running":
-                task_instance.xcom_push(key="return_value", value="running")
-                # finish
+
+            if not isinstance(ret, tuple) or not len(ret) == 2 or not isinstance(ret[0], Result) or not isinstance(ret[1], Action):
+                # malformed
+                task_instance.xcom_push(key="return_value", value=(Result.FAIL, Action.STOP))
                 return True
-            task_instance.xcom_push(key="return_value", value="")
-            return False
+
+            # push to xcom
+            task_instance.xcom_push(key="return_value", value=ret)
+
+            if ret[1] == Action.CONTINUE:
+                # keep checking
+                return False
+            return True
 
         super().__init__(python_callable=_callable_wrapper, **kwargs)
 
-        self._end_fail = PythonOperator(task_id=f"{self.task_id}-dag-fail", python_callable=fail_, trigger_rule="all_success")
-        self._end_pass = PythonOperator(task_id=f"{self.task_id}-dag-pass", python_callable=pass_, trigger_rule="all_success")
+        # this is needed to ensure the dag fails, since the
+        # retrigger_fail step will pass (to ensure dag retriggers!)
+        self._fail = PythonOperator(task_id=f"{self.task_id}-force-dag-fail", python_callable=fail_, trigger_rule="all_success")
 
-        self._loop_fail = TriggerDagRunOperator(task_id=f"{self.task_id}-loop-fail", trigger_dag_id=self.dag_id, trigger_rule="all_success")
-        self._loop_pass = TriggerDagRunOperator(task_id=f"{self.task_id}-loop-pass", trigger_dag_id=self.dag_id, trigger_rule="one_success")
+        self._retrigger_fail = TriggerDagRunOperator(
+            task_id=f"{self.task_id}-retrigger-fail", **{"trigger_dag_id": self.dag_id, "trigger_rule": "all_success", **fail_trigger_kwargs}
+        )
+        self._retrigger_pass = TriggerDagRunOperator(
+            task_id=f"{self.task_id}-retrigger-pass", **{"trigger_dag_id": self.dag_id, "trigger_rule": "one_success", **pass_trigger_kwargs}
+        )
+
+        self._stop_pass = PythonOperator(task_id=f"{self.task_id}-stop-pass", python_callable=pass_, trigger_rule="all_success")
+        self._stop_fail = PythonOperator(task_id=f"{self.task_id}-stop-fail", python_callable=fail_, trigger_rule="all_success")
 
-        self._done_task = PythonOperator(task_id=f"{self.task_id}-done", python_callable=pass_, trigger_rule="all_success")
-        self._running_task = PythonOperator(task_id=f"{self.task_id}-running", python_callable=pass_, trigger_rule="all_success")
-        self._failed_task = PythonOperator(task_id=f"{self.task_id}-failed", python_callable=pass_, trigger_rule="all_success")
         self._sensor_failed_task = PythonOperator(task_id=f"{self.task_id}-sensor-timeout", python_callable=pass_, trigger_rule="all_failed")
 
         branch_choices = {
-            "done": self._done_task.task_id,
-            "running": self._running_task.task_id,
-            "failed": self._failed_task.task_id,
-            "": self._sensor_failed_task.task_id,
+            (Result.PASS, Action.RETRIGGER): self._retrigger_pass.task_id,
+            (Result.PASS, Action.STOP): self._stop_pass.task_id,
+            (Result.FAIL, Action.RETRIGGER): self._retrigger_fail.task_id,
+            (Result.FAIL, Action.STOP): self._stop_fail.task_id,
         }
 
         def _choose_branch(branch_choices=branch_choices, **kwargs):
             task_instance = kwargs["task_instance"]
             check_program_result = task_instance.xcom_pull(key="return_value", task_ids=self.task_id)
-            ret = branch_choices.get(check_program_result, None)
+            try:
+                result = Result(check_program_result[0])
+                action = Action(check_program_result[1])
+                ret = branch_choices.get((result, action), None)
+            except (ValueError, IndexError, TypeError):
+                ret = None
             if ret is None:
+                # skip result
                 raise AirflowSkipException
             return ret
 
@@ -125,25 +135,28 @@ def _choose_branch(branch_choices=branch_choices, **kwargs):
             trigger_rule="all_success",
         )
 
-        self >> self._sensor_failed_task >> self._loop_pass >> self._end_pass
-        self >> self._decide_task >> self._done_task
-        self >> self._decide_task >> self._running_task >> self._loop_pass >> self._end_pass
-        self >> self._decide_task >> self._failed_task >> self._loop_fail >> self._end_fail
+        self >> self._decide_task >> self._stop_pass
+        self >> self._decide_task >> self._stop_fail
+        self >> self._decide_task >> self._retrigger_pass
+        self >> self._decide_task >> self._retrigger_fail >> self._fail
+        self >> self._sensor_failed_task >> self._retrigger_pass
 
     @property
-    def check(self) -> Operator:
-        return self
+    def stop_fail(self) -> Operator:
+        return self._stop_fail
 
     @property
-    def failed(self) -> Operator:
-        # NOTE: use loop_fail as this will pass, but self._end_fail will fail to mark the DAG failed
-        return self._loop_fail
+    def stop_pass(self) -> Operator:
+        return self._stop_pass
 
     @property
-    def passed(self) -> Operator:
-        # NOTE: use loop_pass here to match failed()
-        return self._loop_pass
+    def retrigger_fail(self) -> Operator:
+        return self._retrigger_fail
 
     @property
-    def done(self) -> Operator:
-        return self._done_task
+    def retrigger_pass(self) -> Operator:
+        return self._retrigger_pass
+
+
+class HighAvailabilityOperator(HighAvailabilityOperatorMixin, PythonSensor):
+    pass
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