fix(aci milestone 3): anomaly detection processing fixes (#95261)

- Update issue title
- Fix a bug with AlertContext that prevented actions from firing
- Add logs to investigate why alerts aren't firing through the legacy
system once they make it through workflow engine processing

Author: mifu67

src/sentry/incidents/grouptype.py

@@ -113,6 +113,7 @@ def construct_title(
         priority: DetectorPriorityLevel,
     ) -> str:
         comparison_delta = self.detector.config.get("comparison_delta")
+        detection_type = self.detector.config.get("detection_type")
         agg_display_key = snuba_query.aggregate
 
         if is_mri_field(agg_display_key):
@@ -125,6 +126,10 @@ def construct_title(
         else:
             aggregate = QUERY_AGGREGATION_DISPLAY.get(agg_display_key, agg_display_key)
 
+        if detection_type == "dynamic":
+            alert_type = aggregate
+            return f"Detected an anomaly in the query for {alert_type}"
+
         # Determine the higher or lower comparison
         higher_or_lower = ""
         if detector_trigger.type == Condition.GREATER:

src/sentry/incidents/subscription_processor.py

@@ -453,6 +453,14 @@ def process_update(self, subscription_update: QuerySubscriptionUpdate) -> None:
                         raise ResourceDoesNotExist("Detector not found, cannot evaluate anomaly")
 
                     is_anomalous = get_anomaly_evaluation_from_workflow_engine(detector, results)
+                    logger.info(
+                        "dual processing anomaly detection alert",
+                        extra={
+                            "rule_id": self.alert_rule.id,
+                            "detector_id": detector.id,
+                            "anomaly_evaluation": is_anomalous,
+                        },
+                    )
                     if is_anomalous is None:
                         # we only care about True and False — None indicates no change
                         continue

src/sentry/incidents/typings/metric_detector.py

@@ -36,22 +36,33 @@
 }
 
 
-def fetch_threshold_type(type: Condition) -> AlertRuleThresholdType:
-    return CONDITION_TO_ALERT_RULE_THRESHOLD_TYPE[type]
+def fetch_threshold_type(condition: dict[str, Any]) -> AlertRuleThresholdType:
+    condition_type = condition["type"]
+    if condition_type == Condition.ANOMALY_DETECTION:
+        return condition["comparison"]["threshold_type"]
+    return CONDITION_TO_ALERT_RULE_THRESHOLD_TYPE[condition_type]
 
 
-def fetch_alert_threshold(comparison_value: float, group_status: GroupStatus) -> float | None:
+def fetch_alert_threshold(condition: dict[str, Any], group_status: GroupStatus) -> float | None:
+    condition_type = condition["type"]
+    if condition_type == Condition.ANOMALY_DETECTION:
+        return None
+    comparison_value = condition["comparison"]
     if group_status == GroupStatus.RESOLVED or group_status == GroupStatus.IGNORED:
         return None
     else:
         return comparison_value
 
 
-def fetch_resolve_threshold(comparison_value: float, group_status: GroupStatus) -> float | None:
+def fetch_resolve_threshold(condition: dict[str, Any], group_status: GroupStatus) -> float | None:
     """
     This is the opposite of `fetch_alert_threshold`.
     We keep it explicitly separate to make it clear that we are fetching the resolve threshold and to consolidate tech debt.
     """
+    condition_type = condition["type"]
+    if condition_type == Condition.ANOMALY_DETECTION:
+        return None
+    comparison_value = condition["comparison"]
     if group_status == GroupStatus.RESOLVED or group_status == GroupStatus.IGNORED:
         return comparison_value
     else:
@@ -79,6 +90,12 @@ class AlertContext:
     def from_alert_rule_incident(
         cls, alert_rule: AlertRule, alert_rule_threshold: float | None = None
     ) -> AlertContext:
+        resolve_threshold = alert_rule.resolve_threshold
+
+        if alert_rule.detection_type == AlertRuleDetectionType.DYNAMIC:
+            alert_rule_threshold = None
+            resolve_threshold = None
+
         return cls(
             name=alert_rule.name,
             action_identifier_id=alert_rule.id,
@@ -87,7 +104,7 @@ def from_alert_rule_incident(
             comparison_delta=alert_rule.comparison_delta,
             sensitivity=alert_rule.sensitivity,
             alert_threshold=alert_rule_threshold,
-            resolve_threshold=alert_rule.resolve_threshold,
+            resolve_threshold=resolve_threshold,
         )
 
     @classmethod
@@ -109,9 +126,9 @@ def from_workflow_engine_models(
                 for cond in evidence_data.conditions
                 if cond["condition_result"] == target_priority
             )
-            threshold_type = fetch_threshold_type(Condition(condition["type"]))
-            resolve_threshold = fetch_resolve_threshold(condition["comparison"], group_status)
-            alert_threshold = fetch_alert_threshold(condition["comparison"], group_status)
+            threshold_type = fetch_threshold_type(condition)
+            resolve_threshold = fetch_resolve_threshold(condition, group_status)
+            alert_threshold = fetch_alert_threshold(condition, group_status)
             sensitivity = fetch_sensitivity(condition)
         except StopIteration:
             raise ValueError("No threshold type found for metric issues")