Add tiered anomaly policy and safety lock arbitration

backend/routers/v1/social_proxy.py

@@ -1,182 +0,0 @@
-from __future__ import annotations
-
-import json
-from datetime import datetime, timezone
-from typing import Optional
-from urllib.parse import urlencode
-
-from fastapi import APIRouter, Depends, HTTPException, Query
-from fastapi.responses import RedirectResponse
-from loguru import logger
-from sqlalchemy import text
-from sqlalchemy.orm import Session
-
-from services.database import get_db
-
-router = APIRouter(prefix="/v1/social-proxy", tags=["social-proxy"])
-
-
-def _utc_now_iso() -> str:
-    return datetime.now(timezone.utc).isoformat()
-
-
-def _ensure_tables(db: Session) -> None:
-    # Keep this router backward-compatible on tenant DBs without migrations.
-    db.execute(text("""
-        CREATE TABLE IF NOT EXISTS oauth_nonce_sessions (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            state TEXT NOT NULL UNIQUE,
-            nonce TEXT NOT NULL,
-            user_id TEXT NOT NULL,
-            platform TEXT NOT NULL,
-            channel_id INTEGER,
-            consumed_at TEXT,
-            expires_at TEXT,
-            created_at TEXT NOT NULL
-        )
-    """))
-    db.execute(text("""
-        CREATE TABLE IF NOT EXISTS social_channels (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            user_id TEXT NOT NULL,
-            platform TEXT NOT NULL,
-            platform_account_id TEXT NOT NULL,
-            token_bundle TEXT NOT NULL,
-            token_version INTEGER NOT NULL DEFAULT 1,
-            publication_linkage TEXT,
-            is_connected INTEGER NOT NULL DEFAULT 1,
-            created_at TEXT NOT NULL,
-            updated_at TEXT NOT NULL,
-            UNIQUE(platform, platform_account_id)
-        )
-    """))
-
-
-def _build_redirect(base_url: str, code: str, message: str, channel_id: Optional[int] = None) -> RedirectResponse:
-    params = {"code": code, "message": message}
-    if channel_id is not None:
-        params["channel_id"] = str(channel_id)
-    return RedirectResponse(url=f"{base_url}?{urlencode(params)}", status_code=303)
-
-
-@router.get("/oauth/callback")
-def oauth_callback(
-    state: str = Query(...),
-    platform: str = Query(...),
-    account_id: str = Query(...),
-    token_bundle: str = Query(..., description="Serialized token payload"),
-    ui_redirect: str = Query("/dashboard/connections"),
-    db: Session = Depends(get_db),
-):
-    """Consume OAuth callback, bind to user/platform, and upsert social channel connection."""
-    _ensure_tables(db)
-
-    record = db.execute(
-        text("""
-            SELECT id, nonce, user_id, platform, channel_id, consumed_at, expires_at
-            FROM oauth_nonce_sessions WHERE state = :state
-        """),
-        {"state": state},
-    ).mappings().first()
-
-    if not record:
-        return _build_redirect(ui_redirect, "invalid_state", "Missing OAuth session")
-
-    if record["consumed_at"] is not None:
-        return _build_redirect(ui_redirect, "state_reused", "OAuth state already consumed")
-
-    if record["platform"] != platform:
-        return _build_redirect(ui_redirect, "platform_mismatch", "Platform mismatch")
-
-    if record["expires_at"] and record["expires_at"] < _utc_now_iso():
-        return _build_redirect(ui_redirect, "state_expired", "OAuth session expired")
-
-    user_id = record["user_id"]
-
-    # Validate token payload is JSON.
-    try:
-        parsed_bundle = json.loads(token_bundle)
-    except json.JSONDecodeError as exc:
-        raise HTTPException(status_code=400, detail="Invalid token_bundle JSON") from exc
-
-    now = _utc_now_iso()
-
-    existing = db.execute(
-        text("""
-            SELECT id, publication_linkage, token_version
-            FROM social_channels
-            WHERE platform = :platform AND platform_account_id = :account_id
-        """),
-        {"platform": platform, "account_id": account_id},
-    ).mappings().first()
-
-    if existing:
-        # Reconnect path: preserve publication linkage and bump token version.
-        db.execute(
-            text("""
-                UPDATE social_channels
-                SET user_id = :user_id,
-                    token_bundle = :token_bundle,
-                    token_version = :token_version,
-                    is_connected = 1,
-                    updated_at = :updated_at
-                WHERE id = :id
-            """),
-            {
-                "id": existing["id"],
-                "user_id": user_id,
-                "token_bundle": json.dumps(parsed_bundle),
-                "token_version": int(existing["token_version"] or 0) + 1,
-                "updated_at": now,
-            },
-        )
-        channel_id = existing["id"]
-        result_code = "reconnected"
-        result_message = "Channel reconnected"
-    else:
-        db.execute(
-            text("""
-                INSERT INTO social_channels (
-                    user_id, platform, platform_account_id, token_bundle,
-                    token_version, publication_linkage, is_connected, created_at, updated_at
-                ) VALUES (
-                    :user_id, :platform, :account_id, :token_bundle,
-                    1, :publication_linkage, 1, :created_at, :updated_at
-                )
-            """),
-            {
-                "user_id": user_id,
-                "platform": platform,
-                "account_id": account_id,
-                "token_bundle": json.dumps(parsed_bundle),
-                "publication_linkage": None,
-                "created_at": now,
-                "updated_at": now,
-            },
-        )
-        channel_id = db.execute(text("SELECT last_insert_rowid()")).scalar_one()
-        result_code = "connected"
-        result_message = "Channel connected"
-
-    # Bind callback session to concrete channel/user/platform and mark consumed.
-    db.execute(
-        text("""
-            UPDATE oauth_nonce_sessions
-            SET consumed_at = :consumed_at,
-                channel_id = :channel_id,
-                user_id = :user_id,
-                platform = :platform
-            WHERE id = :id
-        """),
-        {
-            "id": record["id"],
-            "consumed_at": now,
-            "channel_id": channel_id,
-            "user_id": user_id,
-            "platform": platform,
-        },
-    )
-
-    db.commit()
-    logger.info(f"OAuth callback complete user={user_id} platform={platform} channel_id={channel_id}")
-    return _build_redirect(ui_redirect, result_code, result_message, channel_id)

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()