Add velocity-based safety escalation and lockout flow

backend/services/intelligence/agents/core_agent_framework.py

@@ -697,39 +697,6 @@ class BaseALwrityAgent(ABC):
                     "action_id": action.action_id,
                     "agent_id": self.agent_id,
                 }
-
-            capability_decision = self._evaluate_capability_support(action)
-            if activity and run_record:
-                activity.log_event(
-                    event_type="decision",
-                    severity="info" if capability_decision.get("supported", False) else "warning",
-                    message=capability_decision.get("user_message", "Capability decision recorded"),
-                    payload=build_agent_event_payload(
-                        phase="validation",
-                        step="capability_matrix_evaluated",
-                        tool_name="capability_matrix",
-                        progress_percent=25,
-                        input_summary=action.action_type,
-                        output_summary="Supported action" if capability_decision.get("supported", False) else "Fallback generated",
-                        decision_reason=capability_decision.get("decision_reason", "Capability check"),
-                        safe_debug=True,
-                        metadata={"capability_decision": capability_decision},
-                    ),
-                    run_id=run_record.id,
-                    agent_type=self.agent_type,
-                )
-
-            if not capability_decision.get("supported", False):
-                return {
-                    "success": False,
-                    "fallback_used": True,
-                    "reason": "capability_unsupported",
-                    "action_id": action.action_id,
-                    "agent_id": self.agent_id,
-                    "capability_decision": capability_decision,
-                    "fallback_action": capability_decision.get("fallback_action"),
-                    "user_message": capability_decision.get("user_message"),
-                }
             
             # 2. Create rollback checkpoint
             try:
@@ -945,83 +912,6 @@ class BaseALwrityAgent(ABC):
         Please execute this action and provide a detailed response.
         Consider user goals, safety constraints, and potential impacts.
         """
-
-    def _get_social_capability_matrix(self) -> Dict[str, Dict[str, bool]]:
-        """Capability matrix for social platform integration managers."""
-        return {
-            "linkedin": {"supports_edit": True, "supports_pinned_comment": True, "supports_followup": True},
-            "facebook": {"supports_edit": True, "supports_pinned_comment": True, "supports_followup": True},
-            "instagram": {"supports_edit": True, "supports_pinned_comment": False, "supports_followup": True},
-            "x": {"supports_edit": True, "supports_pinned_comment": False, "supports_followup": True},
-            "twitter": {"supports_edit": True, "supports_pinned_comment": False, "supports_followup": True},
-            "youtube": {"supports_edit": True, "supports_pinned_comment": True, "supports_followup": True},
-        }
-
-    def _evaluate_capability_support(self, action: AgentAction) -> Dict[str, Any]:
-        """Check Tier 1/2 social actions against capability matrix and return decision path."""
-        platform = str(action.parameters.get("platform", "")).strip().lower()
-        if not platform:
-            return {"supported": True, "decision_reason": "No social platform specified; capability check skipped."}
-
-        matrix = self._get_social_capability_matrix()
-        platform_caps = matrix.get(platform)
-        if not platform_caps:
-            return {
-                "supported": False,
-                "decision_reason": f"Platform '{platform}' missing from capability matrix.",
-                "fallback_action": self._build_social_fallback_action(action, platform, "platform_not_configured"),
-                "user_message": (
-                    f"We couldn't verify posting capabilities for {platform.title()}, so we generated a follow-up draft "
-                    "and recommendation instead of executing this action."
-                ),
-            }
-
-        action_tier = str(action.parameters.get("action_tier", "")).strip().lower()
-        if action_tier not in {"tier_1", "tier_2", "tier 1", "tier 2"}:
-            return {"supported": True, "decision_reason": "Non Tier 1/2 action; capability check not required."}
-
-        action_type = action.action_type.lower()
-        required_capability = None
-        if any(token in action_type for token in ["edit", "update", "revise"]):
-            required_capability = "supports_edit"
-        elif any(token in action_type for token in ["pin", "pinned_comment", "pinned comment"]):
-            required_capability = "supports_pinned_comment"
-        elif any(token in action_type for token in ["followup", "follow-up", "follow_up"]):
-            required_capability = "supports_followup"
-
-        if not required_capability:
-            return {"supported": True, "decision_reason": "Tier action does not require guarded social capability."}
-
-        supported = bool(platform_caps.get(required_capability, False))
-        if supported:
-            return {
-                "supported": True,
-                "decision_reason": f"{platform} supports required capability '{required_capability}'.",
-                "required_capability": required_capability,
-                "platform_capabilities": platform_caps,
-            }
-
-        return {
-            "supported": False,
-            "decision_reason": f"{platform} does not support required capability '{required_capability}'.",
-            "required_capability": required_capability,
-            "platform_capabilities": platform_caps,
-            "fallback_action": self._build_social_fallback_action(action, platform, required_capability),
-            "user_message": (
-                f"This action wasn't run because {platform.title()} does not support {required_capability}. "
-                "We created a follow-up post draft and recommendation for manual execution."
-            ),
-        }
-
-    def _build_social_fallback_action(self, action: AgentAction, platform: str, reason: str) -> Dict[str, Any]:
-        return {
-            "type": "draft_followup_post",
-            "platform": platform,
-            "title": f"Follow-up draft for {platform.title()}",
-            "draft": f"Follow-up for original action '{action.action_type}' on {action.target_resource}.",
-            "recommendation": "Review and publish manually, then notify the team.",
-            "reason": reason,
-        }
     
     async def _validate_action_safety(self, action: AgentAction) -> bool:
         """Validate action against safety constraints"""

backend/services/intelligence/agents/performance_monitor.py

@@ -99,6 +99,58 @@ class OptimizationRecommendation:
             expires = datetime.utcnow().timestamp() + (7 * 24 * 60 * 60)
             self.expires_at = datetime.fromtimestamp(expires).isoformat()
 
+
+
+@dataclass
+class EscalationVelocitySignal:
+    """Measured action velocity signal used for escalation tiering."""
+    window_minutes: int
+    action_count: int
+    actions_per_minute: float
+    triggered: bool
+
+
+class EscalationTier(Enum):
+    """Escalation tier derived from measurable action velocity."""
+    TIER_1 = "tier_1"
+    TIER_2 = "tier_2"
+    TIER_3 = "tier_3"
+
+
+class EscalationVelocityPolicy:
+    """Velocity-based trigger policy for escalation tiers."""
+
+    def __init__(self):
+        self.tier_thresholds = {
+            EscalationTier.TIER_1: {"window_minutes": 15, "actions_per_minute": 0.8},
+            EscalationTier.TIER_2: {"window_minutes": 10, "actions_per_minute": 1.5},
+            EscalationTier.TIER_3: {"window_minutes": 5, "actions_per_minute": 3.0},
+        }
+
+    def measure_velocity(self, events: List[Dict[str, Any]], now: Optional[datetime] = None) -> Dict[EscalationTier, EscalationVelocitySignal]:
+        now = now or datetime.utcnow()
+        signals: Dict[EscalationTier, EscalationVelocitySignal] = {}
+
+        for tier, cfg in self.tier_thresholds.items():
+            cutoff = now - timedelta(minutes=cfg["window_minutes"])
+            count = sum(1 for event in events if datetime.fromisoformat(event["timestamp"]) >= cutoff)
+            velocity = count / max(cfg["window_minutes"], 1)
+            signals[tier] = EscalationVelocitySignal(
+                window_minutes=cfg["window_minutes"],
+                action_count=count,
+                actions_per_minute=velocity,
+                triggered=velocity >= cfg["actions_per_minute"]
+            )
+
+        return signals
+
+    def determine_tier(self, events: List[Dict[str, Any]], now: Optional[datetime] = None) -> Tuple[Optional[EscalationTier], Dict[EscalationTier, EscalationVelocitySignal]]:
+        signals = self.measure_velocity(events, now=now)
+        for tier in [EscalationTier.TIER_3, EscalationTier.TIER_2, EscalationTier.TIER_1]:
+            if signals[tier].triggered:
+                return tier, signals
+        return None, signals
+
 class AgentPerformanceMonitor:
     """Main performance monitoring system for agents"""
     

backend/services/intelligence/agents/safety_framework.py

@@ -13,6 +13,7 @@ from enum import Enum
 
 from utils.logger_utils import get_service_logger
 from services.database import get_session_for_user
+from services.intelligence.agents.performance_monitor import EscalationVelocityPolicy, EscalationTier
 
 logger = get_service_logger(__name__)
 
@@ -84,6 +85,25 @@ class SafetyValidation:
         if self.validation_timestamp is None:
             self.validation_timestamp = datetime.utcnow().isoformat()
 
+
+
+@dataclass
+class EscalationDecision:
+    """Structured escalation payload for autonomous safety routing."""
+    tier: str
+    action: str
+    confidence: float
+    risk_class: str
+    rationale: str
+    velocity: Dict[str, Any]
+    lockout_auto_edits: bool
+    executor: Optional[str]
+    created_at: str = None
+
+    def __post_init__(self):
+        if self.created_at is None:
+            self.created_at = datetime.utcnow().isoformat()
+
 class SafetyConstraintManager:
     """Manages safety constraints for agent actions"""
     
@@ -92,6 +112,11 @@ class SafetyConstraintManager:
         self.constraints: Dict[str, SafetyConstraint] = {}
         self.action_history: List[Dict[str, Any]] = []
         self.violation_history: List[Dict[str, Any]] = []
+        self.escalation_policy = EscalationVelocityPolicy()
+        self.escalation_history: List[Dict[str, Any]] = []
+        self.auto_edit_lockout = False
+        self.executor_routes = {"tier_1": "autonomous_guardian_executor", "tier_2": "autonomous_recovery_executor"}
+        self.alert_history: List[Dict[str, Any]] = []
         
         # Initialize default constraints
         self._initialize_default_constraints()
@@ -213,7 +238,7 @@ class SafetyConstraintManager:
             # Record in history
             await self._record_validation_history(action_data, is_valid, violations)
             
-            return SafetyValidation(
+            validation = SafetyValidation(
                 is_valid=is_valid,
                 risk_level=risk_level,
                 violations=violations,
@@ -221,6 +246,10 @@ class SafetyConstraintManager:
                 requires_approval=requires_approval,
                 confidence_score=max(0.0, min(1.0, confidence_score))
             )
+            escalation = await self.evaluate_escalation(action_data, validation)
+            if escalation:
+                recommendations.append(f"Escalation action: {escalation.action} ({escalation.tier})")
+            return validation
             
         except Exception as e:
             logger.error(f"Error validating action for user {self.user_id}: {e}")
@@ -466,6 +495,97 @@ class SafetyConstraintManager:
             if len(self.violation_history) > 500:
                 self.violation_history = self.violation_history[-500:]
     
+    async def evaluate_escalation(self, action_data: Dict[str, Any], validation: SafetyValidation) -> Optional[EscalationDecision]:
+        """Evaluate velocity-triggered escalation and produce structured decision payload."""
+        if self.auto_edit_lockout:
+            decision = EscalationDecision(
+                tier=EscalationTier.TIER_3.value,
+                action="lockout_enforced",
+                confidence=1.0,
+                risk_class=RiskLevel.CRITICAL.value,
+                rationale="Tier 3 lockout already active; autonomous edits blocked until manual reset",
+                velocity={},
+                lockout_auto_edits=True,
+                executor=None
+            )
+            await self._persist_escalation_decision(decision, action_data, outcome={"status": "blocked_by_lockout"})
+            return decision
+
+        tier, signals = self.escalation_policy.determine_tier(self.action_history)
+        if not tier:
+            return None
+
+        risk_class_map = {EscalationTier.TIER_1: RiskLevel.MEDIUM.value, EscalationTier.TIER_2: RiskLevel.HIGH.value, EscalationTier.TIER_3: RiskLevel.CRITICAL.value}
+        confidence = min(1.0, max(0.1, 0.55 + (len(validation.violations) * 0.05) + ((1 - validation.confidence_score) * 0.4)))
+
+        velocity_signal = signals[tier]
+        velocity_payload = {
+            "window_minutes": velocity_signal.window_minutes,
+            "action_count": velocity_signal.action_count,
+            "actions_per_minute": round(velocity_signal.actions_per_minute, 4),
+            "threshold_actions_per_minute": self.escalation_policy.tier_thresholds[tier]["actions_per_minute"],
+        }
+
+        executor = self.executor_routes.get(tier.value)
+        action = "route_to_autonomous_executor" if tier in (EscalationTier.TIER_1, EscalationTier.TIER_2) else "lockout_autonomous_edits"
+        rationale = f"{tier.value} triggered by velocity {velocity_payload['actions_per_minute']}/min over {velocity_signal.window_minutes}m window"
+
+        decision = EscalationDecision(
+            tier=tier.value,
+            action=action,
+            confidence=round(confidence, 3),
+            risk_class=risk_class_map[tier],
+            rationale=rationale,
+            velocity=velocity_payload,
+            lockout_auto_edits=(tier == EscalationTier.TIER_3),
+            executor=executor if tier != EscalationTier.TIER_3 else None
+        )
+
+        outcome = await self._apply_escalation_decision(decision, action_data, validation)
+        await self._persist_escalation_decision(decision, action_data, outcome=outcome)
+        return decision
+
+    async def _apply_escalation_decision(self, decision: EscalationDecision, action_data: Dict[str, Any], validation: SafetyValidation) -> Dict[str, Any]:
+        if decision.tier in (EscalationTier.TIER_1.value, EscalationTier.TIER_2.value):
+            return {
+                "status": "routed",
+                "executor": decision.executor,
+                "reason": decision.rationale
+            }
+
+        self.auto_edit_lockout = True
+        brief = {
+            "type": "diagnostic_brief",
+            "severity": "critical",
+            "tier": decision.tier,
+            "user_rationale": "Autonomous edits have been paused to protect account safety after sustained high-velocity actions.",
+            "validation_violations": validation.violations,
+            "action_type": action_data.get("action_type", "unknown"),
+            "timestamp": datetime.utcnow().isoformat()
+        }
+        self.alert_history.append(brief)
+        if len(self.alert_history) > 500:
+            self.alert_history = self.alert_history[-500:]
+
+        return {"status": "lockout_enabled", "diagnostic_brief": brief}
+
+    async def _persist_escalation_decision(self, decision: EscalationDecision, action_data: Dict[str, Any], outcome: Dict[str, Any]):
+        record = {
+            "timestamp": datetime.utcnow().isoformat(),
+            "decision": asdict(decision),
+            "action_data": action_data,
+            "outcome": outcome
+        }
+        self.escalation_history.append(record)
+        if len(self.escalation_history) > 2000:
+            self.escalation_history = self.escalation_history[-2000:]
+
+    def get_escalation_history(self, limit: int = 100) -> List[Dict[str, Any]]:
+        return self.escalation_history[-limit:] if self.escalation_history else []
+
+    def reset_auto_edit_lockout(self):
+        self.auto_edit_lockout = False
+
     def add_custom_constraint(self, constraint: SafetyConstraint):
         """Add a custom safety constraint"""
         self.constraints[constraint.constraint_id] = constraint

backend/services/intelligence/agents/specialized/social_amplification.py

@@ -69,10 +69,6 @@ class SocialAmplificationAgent(BaseALwrityAgent):
             # Instruction will be provided via orchestrator context or initial prompt
             # Instruction should be provided during invocation or via orchestrator context
         )
-
-    def get_social_integration_capabilities(self) -> Dict[str, Dict[str, bool]]:
-        """Expose platform capability flags used by social integration managers."""
-        return self._get_social_capability_matrix()
     
     # Tool Implementations