Add tiered anomaly policy and safety lock arbitration

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/executors/self_healing_executor.py

@@ -1,271 +0,0 @@
-"""Self-healing executor for social post engagement recovery.
-
-Implements:
-- Per-post evaluation windows and cooldown timers
-- Stagnation trigger evaluation with tiered action selection
-- Action idempotency keys for edit/comment/thread operations
-- Duplicate and over-frequency suppression within cooldown boundaries
-- Outcome persistence and safe retry policy for transient failures
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field, asdict
-from datetime import datetime, timedelta, timezone
-from enum import Enum
-import hashlib
-import json
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-
-class ActionType(str, Enum):
-    EDIT = "edit"
-    COMMENT = "comment"
-    THREAD = "thread"
-
-
-class ActionTier(str, Enum):
-    TIER_1 = "tier_1"  # low-intensity nudge (comment)
-    TIER_2 = "tier_2"  # medium-intensity enhancement (edit)
-    TIER_3 = "tier_3"  # high-intensity amplification (thread)
-
-
-SAFE_TRANSIENT_ERROR_CODES = {
-    "timeout",
-    "rate_limit",
-    "service_unavailable",
-    "network_error",
-}
-
-
-@dataclass
-class EvaluationConfig:
-    per_post_window_minutes: int = 90
-    min_samples_required: int = 3
-    cooldown_by_action_seconds: Dict[ActionType, int] = field(
-        default_factory=lambda: {
-            ActionType.COMMENT: 30 * 60,
-            ActionType.EDIT: 2 * 60 * 60,
-            ActionType.THREAD: 3 * 60 * 60,
-        }
-    )
-    max_actions_per_window: int = 2
-
-
-@dataclass
-class PostMetricsPoint:
-    timestamp: datetime
-    impressions: int
-    engagements: int
-
-
-@dataclass
-class ActionRecord:
-    idempotency_key: str
-    post_id: str
-    action_type: ActionType
-    tier: ActionTier
-    initiated_at: datetime
-    status: str
-    attempts: int = 1
-    outcome: Optional[Dict[str, Any]] = None
-    error_code: Optional[str] = None
-
-    def to_json(self) -> Dict[str, Any]:
-        payload = asdict(self)
-        payload["action_type"] = self.action_type.value
-        payload["tier"] = self.tier.value
-        payload["initiated_at"] = self.initiated_at.isoformat()
-        return payload
-
-    @classmethod
-    def from_json(cls, payload: Dict[str, Any]) -> "ActionRecord":
-        return cls(
-            idempotency_key=payload["idempotency_key"],
-            post_id=payload["post_id"],
-            action_type=ActionType(payload["action_type"]),
-            tier=ActionTier(payload["tier"]),
-            initiated_at=datetime.fromisoformat(payload["initiated_at"]),
-            status=payload["status"],
-            attempts=payload.get("attempts", 1),
-            outcome=payload.get("outcome"),
-            error_code=payload.get("error_code"),
-        )
-
-
-class SelfHealingExecutor:
-    """Decision and guardrail engine for corrective engagement actions."""
-
-    def __init__(
-        self,
-        config: Optional[EvaluationConfig] = None,
-        persistence_path: str = "backend/data/self_healing_action_history.json",
-    ) -> None:
-        self.config = config or EvaluationConfig()
-        self.persistence_path = Path(persistence_path)
-        self._history: List[ActionRecord] = self._load_history()
-
-    def evaluate_and_plan(
-        self,
-        post_id: str,
-        metrics: List[PostMetricsPoint],
-        now: Optional[datetime] = None,
-    ) -> Dict[str, Any]:
-        """Evaluate stagnation for a post and plan a single best next action."""
-        now = now or datetime.now(timezone.utc)
-        window_metrics = self._filter_window(metrics, now)
-
-        if len(window_metrics) < self.config.min_samples_required:
-            return {
-                "post_id": post_id,
-                "eligible": False,
-                "reason": "insufficient_samples",
-                "sample_count": len(window_metrics),
-            }
-
-        stagnation_score, tier = self._evaluate_stagnation(window_metrics)
-        action_type = self._choose_action_type(tier)
-        idempotency_key = self.generate_idempotency_key(post_id, action_type, tier)
-
-        if self._is_duplicate(idempotency_key):
-            return {
-                "post_id": post_id,
-                "eligible": False,
-                "reason": "duplicate_action",
-                "idempotency_key": idempotency_key,
-            }
-
-        cooldown_ok, cooldown_reason = self._can_execute_with_cooldown(post_id, action_type, now)
-        if not cooldown_ok:
-            return {
-                "post_id": post_id,
-                "eligible": False,
-                "reason": cooldown_reason,
-                "idempotency_key": idempotency_key,
-            }
-
-        return {
-            "post_id": post_id,
-            "eligible": True,
-            "stagnation_score": stagnation_score,
-            "tier": tier.value,
-            "action_type": action_type.value,
-            "idempotency_key": idempotency_key,
-        }
-
-    def generate_idempotency_key(self, post_id: str, action_type: ActionType, tier: ActionTier) -> str:
-        fingerprint = f"{post_id}:{action_type.value}:{tier.value}".encode("utf-8")
-        digest = hashlib.sha256(fingerprint).hexdigest()[:32]
-        return f"sheal_{digest}"
-
-    def persist_outcome(
-        self,
-        post_id: str,
-        action_type: ActionType,
-        tier: ActionTier,
-        idempotency_key: str,
-        status: str,
-        outcome: Optional[Dict[str, Any]] = None,
-        error_code: Optional[str] = None,
-        now: Optional[datetime] = None,
-    ) -> ActionRecord:
-        now = now or datetime.now(timezone.utc)
-
-        existing = next((h for h in self._history if h.idempotency_key == idempotency_key), None)
-        if existing:
-            existing.status = status
-            existing.outcome = outcome
-            existing.error_code = error_code
-            existing.attempts += 1
-            existing.initiated_at = now
-            record = existing
-        else:
-            record = ActionRecord(
-                idempotency_key=idempotency_key,
-                post_id=post_id,
-                action_type=action_type,
-                tier=tier,
-                initiated_at=now,
-                status=status,
-                outcome=outcome,
-                error_code=error_code,
-            )
-            self._history.append(record)
-
-        self._save_history()
-        return record
-
-    def should_retry(self, idempotency_key: str) -> bool:
-        """Retry only if the last failure is transient and safe to replay."""
-        rec = next((h for h in self._history if h.idempotency_key == idempotency_key), None)
-        if not rec or rec.status != "failed":
-            return False
-
-        if rec.error_code not in SAFE_TRANSIENT_ERROR_CODES:
-            return False
-
-        return rec.action_type in {ActionType.COMMENT, ActionType.EDIT, ActionType.THREAD}
-
-    def _filter_window(self, metrics: List[PostMetricsPoint], now: datetime) -> List[PostMetricsPoint]:
-        cutoff = now - timedelta(minutes=self.config.per_post_window_minutes)
-        return [m for m in metrics if m.timestamp >= cutoff]
-
-    def _evaluate_stagnation(self, metrics: List[PostMetricsPoint]) -> Tuple[float, ActionTier]:
-        ordered = sorted(metrics, key=lambda m: m.timestamp)
-        first, last = ordered[0], ordered[-1]
-
-        imp_delta = max(0, last.impressions - first.impressions)
-        eng_delta = max(0, last.engagements - first.engagements)
-        eng_rate = eng_delta / imp_delta if imp_delta > 0 else 0.0
-
-        stagnation_score = 1.0 - min(1.0, eng_rate * 20)
-        if stagnation_score >= 0.8:
-            return stagnation_score, ActionTier.TIER_3
-        if stagnation_score >= 0.55:
-            return stagnation_score, ActionTier.TIER_2
-        return stagnation_score, ActionTier.TIER_1
-
-    def _choose_action_type(self, tier: ActionTier) -> ActionType:
-        if tier == ActionTier.TIER_1:
-            return ActionType.COMMENT
-        if tier == ActionTier.TIER_2:
-            return ActionType.EDIT
-        return ActionType.THREAD
-
-    def _is_duplicate(self, idempotency_key: str) -> bool:
-        return any(h.idempotency_key == idempotency_key and h.status in {"success", "running"} for h in self._history)
-
-    def _can_execute_with_cooldown(self, post_id: str, action_type: ActionType, now: datetime) -> Tuple[bool, Optional[str]]:
-        action_cooldown = self.config.cooldown_by_action_seconds[action_type]
-
-        same_post = [h for h in self._history if h.post_id == post_id]
-        recent_in_window = [
-            h for h in same_post
-            if h.initiated_at >= now - timedelta(minutes=self.config.per_post_window_minutes)
-        ]
-        if len(recent_in_window) >= self.config.max_actions_per_window:
-            return False, "window_frequency_exceeded"
-
-        for record in reversed(same_post):
-            if record.action_type != action_type:
-                continue
-            if (now - record.initiated_at).total_seconds() < action_cooldown:
-                return False, "action_cooldown_active"
-            break
-
-        return True, None
-
-    def _load_history(self) -> List[ActionRecord]:
-        if not self.persistence_path.exists():
-            return []
-        try:
-            payload = json.loads(self.persistence_path.read_text(encoding="utf-8"))
-            return [ActionRecord.from_json(item) for item in payload]
-        except (json.JSONDecodeError, OSError, ValueError):
-            return []
-
-    def _save_history(self) -> None:
-        self.persistence_path.parent.mkdir(parents=True, exist_ok=True)
-        payload = [item.to_json() for item in self._history]
-        self.persistence_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")