Add tiered anomaly policy and safety lock arbitration

backend/models/oauth_token_monitoring_models.py

@@ -40,10 +40,6 @@ class OAuthTokenMonitoringTask(Base):
     
     # Scheduling
     next_check = Column(DateTime, nullable=True, index=True)  # Next scheduled check time
-    next_retry_at = Column(DateTime, nullable=True, index=True)  # Backoff retry schedule for refresh failures
-    refresh_attempts = Column(Integer, default=0)  # Current retry attempt count for refresh workflow
-    terminal_failure_reason = Column(Text, nullable=True)  # Permanent failure reason requiring user action
-    channel_status = Column(String(32), default='connected')  # connected, degraded, disconnected
     
     # Metadata
     created_at = Column(DateTime, default=datetime.utcnow)
@@ -101,3 +97,4 @@ class OAuthTokenExecutionLog(Base):
     
     def __repr__(self):
         return f"<OAuthTokenExecutionLog(id={self.id}, task_id={self.task_id}, status={self.status}, execution_date={self.execution_date})>"
+

backend/services/intelligence/agents/performance_monitor.py

@@ -99,6 +99,17 @@ class OptimizationRecommendation:
             expires = datetime.utcnow().timestamp() + (7 * 24 * 60 * 60)
             self.expires_at = datetime.fromtimestamp(expires).isoformat()
 
+
+@dataclass
+class TierPolicyConfig:
+    """Structured policy for anomaly tiers and remediation controls"""
+    tier: int
+    trigger_metrics: List[str]
+    thresholds: Dict[str, float]
+    max_iterations: int
+    lock_criteria: Dict[str, Any]
+
+
 class AgentPerformanceMonitor:
     """Main performance monitoring system for agents"""
     
@@ -108,6 +119,32 @@ class AgentPerformanceMonitor:
         self.agent_snapshots: Dict[str, AgentPerformanceSnapshot] = {}
         self.recommendations: List[OptimizationRecommendation] = []
         self.performance_history: deque = deque(maxlen=1000)  # Keep last 1000 data points
+        self.systemic_alerts: List[Dict[str, Any]] = []
+
+        # Structured tier policy config
+        self.tier_policy_config: Dict[int, TierPolicyConfig] = {
+            1: TierPolicyConfig(
+                tier=1,
+                trigger_metrics=["success_rate", "efficiency_score", "response_time"],
+                thresholds={"success_rate": 0.80, "efficiency_score": 0.65, "response_time": 45.0},
+                max_iterations=3,
+                lock_criteria={"min_confidence": 0.85, "consecutive_failures": 6}
+            ),
+            2: TierPolicyConfig(
+                tier=2,
+                trigger_metrics=["success_rate", "efficiency_score", "response_time", "market_impact"],
+                thresholds={"success_rate": 0.70, "efficiency_score": 0.50, "response_time": 60.0, "market_impact": 0.35},
+                max_iterations=2,
+                lock_criteria={"min_confidence": 0.75, "consecutive_failures": 4}
+            ),
+            3: TierPolicyConfig(
+                tier=3,
+                trigger_metrics=["success_rate", "efficiency_score", "response_time", "market_impact"],
+                thresholds={"success_rate": 0.55, "efficiency_score": 0.35, "response_time": 90.0, "market_impact": 0.25},
+                max_iterations=1,
+                lock_criteria={"min_confidence": 0.65, "consecutive_failures": 3}
+            )
+        }
         
         # Performance thresholds and targets
         self.performance_targets = {
@@ -513,6 +550,54 @@ class AgentPerformanceMonitor:
         }
         return priority_weights.get(priority, 0)
     
+    def _build_recommended_action_payload(self, agent_id: str, snapshot: AgentPerformanceSnapshot) -> Dict[str, Any]:
+        """Build recommended action payload including tier and confidence."""
+        tier = 1
+        if (snapshot.success_rate <= self.tier_policy_config[3].thresholds["success_rate"] or
+            snapshot.efficiency_score <= self.tier_policy_config[3].thresholds["efficiency_score"] or
+            snapshot.average_response_time >= self.tier_policy_config[3].thresholds["response_time"] or
+            snapshot.market_impact_score <= self.tier_policy_config[3].thresholds["market_impact"]):
+            tier = 3
+        elif (snapshot.success_rate <= self.tier_policy_config[2].thresholds["success_rate"] or
+              snapshot.efficiency_score <= self.tier_policy_config[2].thresholds["efficiency_score"] or
+              snapshot.average_response_time >= self.tier_policy_config[2].thresholds["response_time"] or
+              snapshot.market_impact_score <= self.tier_policy_config[2].thresholds["market_impact"]):
+            tier = 2
+
+        confidence = round(max(0.0, min(1.0, 1.0 - abs(0.75 - self._calculate_health_score(snapshot)))) , 2)
+        policy = self.tier_policy_config[tier]
+
+        return {
+            "agent_id": agent_id,
+            "tier": tier,
+            "confidence": confidence,
+            "max_iterations": policy.max_iterations,
+            "lock_criteria": policy.lock_criteria,
+            "trigger_metrics": policy.trigger_metrics
+        }
+
+    def _route_tier3_systemic_alert(self, action_payload: Dict[str, Any], alerts: List[Dict[str, Any]]) -> None:
+        """Route Tier 3 systemic anomalies to alerting subsystem with diagnostic brief."""
+        diagnostic_brief = {
+            "type": "systemic_anomaly",
+            "severity": "critical",
+            "tier": 3,
+            "confidence": action_payload.get("confidence", 0.0),
+            "agent_id": action_payload.get("agent_id"),
+            "timestamp": datetime.utcnow().isoformat(),
+            "diagnostic_brief": {
+                "trigger_metrics": action_payload.get("trigger_metrics", []),
+                "alerts": alerts,
+                "max_iterations": action_payload.get("max_iterations"),
+                "lock_criteria": action_payload.get("lock_criteria", {})
+            }
+        }
+        self.systemic_alerts.append(diagnostic_brief)
+        if len(self.systemic_alerts) > 200:
+            self.systemic_alerts = self.systemic_alerts[-200:]
+        logger.critical(f"[ALERTING_SUBSYSTEM] Tier 3 systemic anomaly routed: {json.dumps(diagnostic_brief)}")
+
+
     async def get_performance_alerts(self, agent_id: str) -> List[Dict[str, Any]]:
         """Get performance alerts for an agent"""
         alerts = []
@@ -574,6 +659,13 @@ class AgentPerformanceMonitor:
                     "timestamp": datetime.utcnow().isoformat()
                 })
             
+            action_payload = self._build_recommended_action_payload(agent_id, snapshot)
+            if action_payload["tier"] == 3:
+                self._route_tier3_systemic_alert(action_payload, alerts)
+
+            for alert in alerts:
+                alert["recommended_action"] = action_payload
+
             return alerts
             
         except Exception as e:

backend/services/intelligence/agents/safety_framework.py

@@ -84,6 +84,17 @@ class SafetyValidation:
         if self.validation_timestamp is None:
             self.validation_timestamp = datetime.utcnow().isoformat()
 
+
+@dataclass
+class SafetyArbitrationDecision:
+    """Explicit allow/deny/lock decision with reasons."""
+    decision: str
+    reasons: List[str]
+    tier: int
+    confidence: float
+    lock_state_active: bool
+
+
 class SafetyConstraintManager:
     """Manages safety constraints for agent actions"""
     
@@ -92,6 +103,8 @@ class SafetyConstraintManager:
         self.constraints: Dict[str, SafetyConstraint] = {}
         self.action_history: List[Dict[str, Any]] = []
         self.violation_history: List[Dict[str, Any]] = []
+        self.lock_state_active: bool = False
+        self.lock_state_reason: Optional[str] = None
         
         # Initialize default constraints
         self._initialize_default_constraints()
@@ -163,6 +176,17 @@ class SafetyConstraintManager:
         """Validate an action against safety constraints"""
         try:
             logger.info(f"Validating action for user {self.user_id}: {action_data.get('action_type', 'unknown')}")
+
+            if self.lock_state_active and action_data.get("autonomous_modification", True):
+                reason = self.lock_state_reason or "Safety lock is active due to Tier 3 systemic anomaly"
+                return SafetyValidation(
+                    is_valid=False,
+                    risk_level=RiskLevel.CRITICAL,
+                    violations=["Autonomous modifications blocked while lock state is active"],
+                    recommendations=[reason],
+                    requires_approval=True,
+                    confidence_score=1.0
+                )
             
             violations = []
             recommendations = []
@@ -207,19 +231,29 @@ class SafetyConstraintManager:
             
             # Final validation
             is_valid = len(violations) == 0 and not requires_approval
-            
-            logger.info(f"Action validation completed for user {self.user_id}. Valid: {is_valid}, Risk: {risk_level.value}, Violations: {len(violations)}")
-            
+            confidence_score = max(0.0, min(1.0, confidence_score))
+            arbitration = self._arbitrate_decision(action_data, risk_level, violations, requires_approval, confidence_score)
+
+            if arbitration.decision == "lock":
+                self.lock_state_active = True
+                self.lock_state_reason = "; ".join(arbitration.reasons)
+                is_valid = False
+                requires_approval = True
+
+            recommendations.extend([f"Arbitration decision: {arbitration.decision}", *arbitration.reasons])
+
+            logger.info(f"Action validation completed for user {self.user_id}. Decision: {arbitration.decision}, Valid: {is_valid}, Risk: {risk_level.value}, Violations: {len(violations)}")
+
             # Record in history
             await self._record_validation_history(action_data, is_valid, violations)
-            
+
             return SafetyValidation(
                 is_valid=is_valid,
                 risk_level=risk_level,
                 violations=violations,
                 recommendations=recommendations,
                 requires_approval=requires_approval,
-                confidence_score=max(0.0, min(1.0, confidence_score))
+                confidence_score=confidence_score
             )
             
         except Exception as e:
@@ -235,6 +269,30 @@ class SafetyConstraintManager:
                 confidence_score=0.0
             )
     
+    def _arbitrate_decision(self, action_data: Dict[str, Any], risk_level: RiskLevel, violations: List[str], requires_approval: bool, confidence_score: float) -> SafetyArbitrationDecision:
+        """Arbitrate allow/deny/lock with explicit reasons."""
+        reasons: List[str] = []
+        tier = int(action_data.get("recommended_tier", 1))
+
+        if self.lock_state_active:
+            reasons.append("Existing lock state is active")
+            return SafetyArbitrationDecision("lock", reasons, tier, confidence_score, True)
+
+        if tier >= 3 or risk_level == RiskLevel.CRITICAL:
+            reasons.append("Tier 3 systemic anomaly or critical risk detected")
+            if violations:
+                reasons.extend(violations)
+            return SafetyArbitrationDecision("lock", reasons, 3, confidence_score, True)
+
+        if violations or requires_approval:
+            reasons.append("Safety policy violation or approval requirement triggered")
+            reasons.extend(violations)
+            return SafetyArbitrationDecision("deny", reasons, tier, confidence_score, False)
+
+        reasons.append("No policy violations detected")
+        return SafetyArbitrationDecision("allow", reasons, tier, confidence_score, False)
+
+
     def _determine_action_category(self, action_type: str) -> ActionCategory:
         """Determine the category of an action"""
         action_type_lower = action_type.lower()

backend/services/scheduler/__init__.py

@@ -26,10 +26,7 @@ from .executors.advertools_executor import AdvertoolsExecutor
 from .executors.sif_indexing_executor import SIFIndexingExecutor
 from .executors.market_trends_executor import MarketTrendsExecutor
 from .utils.task_loader import load_due_monitoring_tasks
-from .utils.oauth_token_task_loader import (
-    load_due_oauth_token_monitoring_tasks,
-    load_near_expiry_oauth_token_tasks
-)
+from .utils.oauth_token_task_loader import load_due_oauth_token_monitoring_tasks
 from .utils.website_analysis_task_loader import load_due_website_analysis_tasks
 from .utils.onboarding_full_website_analysis_task_loader import load_due_onboarding_full_website_analysis_tasks
 from .utils.deep_competitor_analysis_task_loader import load_due_deep_competitor_analysis_tasks
@@ -73,11 +70,6 @@ def get_scheduler() -> TaskScheduler:
             oauth_token_executor,
             load_due_oauth_token_monitoring_tasks
         )
-        _scheduler_instance.register_executor(
-            'oauth_token_refresh',
-            oauth_token_executor,
-            load_near_expiry_oauth_token_tasks
-        )
         
         # Register website analysis executor
         website_analysis_executor = WebsiteAnalysisExecutor()

backend/services/scheduler/executors/oauth_token_monitoring_executor.py

@@ -42,8 +42,6 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
         self.exception_handler = SchedulerExceptionHandler()
         # Expiration warning window (7 days before expiration)
         self.expiration_warning_days = 7
-        self.max_refresh_retries = 3
-        self.base_retry_backoff_minutes = 15
     
     async def execute_task(self, task: OAuthTokenMonitoringTask, db: Session) -> TaskExecutionResult:
         """
@@ -95,10 +93,6 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
                 task.last_success = datetime.utcnow()
                 task.status = 'active'
                 task.failure_reason = None
-                task.terminal_failure_reason = None
-                task.channel_status = 'connected'
-                task.refresh_attempts = 0
-                task.next_retry_at = None
                 # Reset failure tracking on success
                 task.consecutive_failures = 0
                 task.failure_pattern = None
@@ -118,7 +112,6 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
                 
                 task.last_failure = datetime.utcnow()
                 task.failure_reason = result.error_message
-                task.refresh_attempts = (task.refresh_attempts or 0) + 1
                 
                 if pattern and pattern.should_cool_off:
                     # Mark task for human intervention
@@ -133,9 +126,6 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
                     }
                     # Clear next_check - task won't run automatically
                     task.next_check = None
-                    task.next_retry_at = None
-                    task.channel_status = "disconnected"
-                    task.terminal_failure_reason = result.error_message
                     
                     self.logger.warning(
                         f"Task {task.id} marked for human intervention: "
@@ -143,17 +133,10 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
                         f"reason: {pattern.failure_reason.value}"
                     )
                 else:
+                    # Normal failure handling
+                    task.status = 'failed'
                     task.consecutive_failures = (task.consecutive_failures or 0) + 1
-                    if task.refresh_attempts >= self.max_refresh_retries:
-                        task.status = 'failed'
-                        task.channel_status = 'disconnected'
-                        task.terminal_failure_reason = result.error_message
-                        task.next_retry_at = None
-                    else:
-                        task.status = 'degraded'
-                        task.channel_status = 'degraded'
-                        delay_minutes = self.base_retry_backoff_minutes * (2 ** (task.refresh_attempts - 1))
-                        task.next_retry_at = datetime.utcnow() + timedelta(minutes=delay_minutes)
+                    # Do NOT update next_check - wait for manual trigger
                 
                 self.logger.warning(
                     f"OAuth token refresh failed for user {user_id}, platform {platform}. "
@@ -161,7 +144,7 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
                 )
                 
                 # Create UsageAlert notification for the user
-                self._create_failure_alert(user_id, platform, result.error_message, result.result_data, db, task)
+                self._create_failure_alert(user_id, platform, result.error_message, result.result_data, db)
             
             task.updated_at = datetime.utcnow()
             db.commit()
@@ -210,14 +193,12 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
                 task.last_failure = datetime.utcnow()
                 task.failure_reason = str(e)
                 task.status = 'failed'
-                task.channel_status = 'disconnected'
-                task.terminal_failure_reason = str(e)
                 task.last_check = datetime.utcnow()
                 task.updated_at = datetime.utcnow()
-                task.next_retry_at = None
+                # Do NOT update next_check - wait for manual trigger
                 
                 # Create UsageAlert notification for the user
-                self._create_failure_alert(user_id, task.platform, str(e), None, db, task)
+                self._create_failure_alert(user_id, task.platform, str(e), None, db)
                 
                 db.commit()
             except Exception as commit_error:
@@ -670,8 +651,7 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
         platform: str,
         error_message: str,
         result_data: Optional[Dict[str, Any]],
-        db: Session,
-        task: Optional[OAuthTokenMonitoringTask] = None
+        db: Session
     ):
         """
         Create a UsageAlert notification when OAuth token refresh fails.
@@ -743,20 +723,6 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
             # Get current billing period (YYYY-MM format)
             from datetime import datetime
             billing_period = datetime.utcnow().strftime("%Y-%m")
-
-            alert_payload = {
-                "requires_user_action": True,
-                "platform": platform,
-                "channel_status": getattr(task, "channel_status", "disconnected"),
-                "terminal_failure_reason": getattr(task, "terminal_failure_reason", error_message),
-                "next_retry_at": (
-                    task.next_retry_at.isoformat() if task and task.next_retry_at else None
-                ),
-                "refresh_attempts": getattr(task, "refresh_attempts", 0),
-                "max_refresh_retries": self.max_refresh_retries,
-            }
-
-            message = f"{message} [ALERT_PAYLOAD] {alert_payload}"
             
             # Create UsageAlert
             alert = UsageAlert(
@@ -820,3 +786,4 @@ class OAuthTokenMonitoringExecutor(TaskExecutor):
                 f"Defaulting to Weekly (7 days)."
             )
             return last_execution + timedelta(days=7)
+

backend/services/scheduler/utils/oauth_token_task_loader.py

@@ -3,7 +3,7 @@ OAuth Token Monitoring Task Loader
 Functions to load due OAuth token monitoring tasks from database.
 """
 
-from datetime import datetime, timedelta
+from datetime import datetime
 from typing import List, Optional, Union
 from sqlalchemy.orm import Session
 from sqlalchemy import and_, or_
@@ -52,34 +52,3 @@ def load_due_oauth_token_monitoring_tasks(
     
     return query.all()
 
-
-def load_near_expiry_oauth_token_tasks(
-    db: Session,
-    refresh_horizon_hours: int = 24,
-    user_id: Optional[Union[str, int]] = None
-) -> List[OAuthTokenMonitoringTask]:
-    """
-    Load OAuth tasks that should run token refresh logic soon.
-
-    Includes:
-    - tasks with a scheduled retry now due (next_retry_at <= now)
-    - tasks whose routine check is inside the near-expiry horizon window
-    """
-    now = datetime.utcnow()
-    horizon = now + timedelta(hours=max(refresh_horizon_hours, 1))
-
-    query = db.query(OAuthTokenMonitoringTask).filter(
-        and_(
-            OAuthTokenMonitoringTask.status.in_(['active', 'failed', 'degraded']),
-            or_(
-                OAuthTokenMonitoringTask.next_retry_at <= now,
-                OAuthTokenMonitoringTask.next_check <= horizon,
-                OAuthTokenMonitoringTask.next_check.is_(None)
-            )
-        )
-    )
-
-    if user_id is not None:
-        query = query.filter(OAuthTokenMonitoringTask.user_id == str(user_id))
-
-    return query.all()