🤖 Implement Story 4: Reflexive Core

- Add ReflexiveEngine for self-monitoring and corrective actions
- Implement PolicyMonitor, LedgerMonitor, and AnomalyDetector
- Add HaltAction, EscalateAction, MonitorAction, and AllowAction
- Create ActionFactory and ActionExecutor for action management
- Add HTTP API endpoints for risk simulation and engine status
- Integrate ReflexiveEngine into FastMCP server
- Add comprehensive test suite with 70 passing tests
- Support halt(), escalate(), monitor(), and allow() decisions
- Record reflexive events with cryptographic proof hashes
- Enable automatic risk assessment and corrective actions

Closes: Story 4 - Reflexive Core implementation

examples/reflexive_example.py

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+"""Example usage of the Reflexive Core."""
+
+import asyncio
+from datetime import datetime
+
+from fastmcp import FastMCP
+from fastmcp.reflexive import ReflexiveEngine, ActionContext, DecisionType, RiskLevel
+from fastmcp.reflexive.monitor import PolicyMonitor, LedgerMonitor, AnomalyDetector
+from fastmcp.reflexive.actions import ActionFactory, ActionExecutor
+
+
+async def main():
+    """Demonstrate reflexive core functionality."""
+    print("🚀 Reflexive Core Example")
+    print("=" * 50)
+    
+    # Create a FastMCP server with reflexive core
+    server = FastMCP("ReflexiveExampleServer")
+    
+    # Enable the reflexive core
+    reflexive_engine = server.enable_reflexive_core()
+    
+    # Add monitors
+    policy_monitor = PolicyMonitor()
+    ledger_monitor = LedgerMonitor()
+    anomaly_detector = AnomalyDetector()
+    
+    reflexive_engine.add_monitor(policy_monitor)
+    reflexive_engine.add_monitor(ledger_monitor)
+    reflexive_engine.add_monitor(anomaly_detector)
+    
+    print(f"✅ Reflexive core enabled with {len(reflexive_engine.monitors)} monitors")
+    
+    # Start the reflexive engine
+    await reflexive_engine.start()
+    print("✅ Reflexive engine started")
+    
+    # Create action executor
+    action_executor = ActionExecutor()
+    
+    # Example 1: Normal action (should be allowed)
+    print("\n📋 Example 1: Normal Action")
+    print("-" * 30)
+    
+    normal_action = ActionContext(
+        action_id="normal_action_001",
+        actor_id="authorized_user",
+        action_type="data_read",
+        resource_id="public_data",
+        metadata={"authorized": True}
+    )
+    
+    decision = await reflexive_engine._evaluate_action(normal_action)
+    print(f"Decision: {decision.decision_type}")
+    print(f"Risk Level: {decision.risk_level}")
+    print(f"Reason: {decision.reason}")
+    
+    # Execute the action
+    action = ActionFactory.create_action(decision)
+    result = await action_executor.execute_action(action)
+    print(f"Action Result: {action.get_action_type()} - {result.get('allowed', 'N/A')}")
+    
+    # Example 2: Policy violation (should be halted)
+    print("\n🚨 Example 2: Policy Violation")
+    print("-" * 30)
+    
+    violation_action = ActionContext(
+        action_id="violation_action_002",
+        actor_id="guest_user",
+        action_type="admin_access",
+        resource_id="admin_panel",
+        metadata={"authorized": False}
+    )
+    
+    decision = await reflexive_engine._evaluate_action(violation_action)
+    print(f"Decision: {decision.decision_type}")
+    print(f"Risk Level: {decision.risk_level}")
+    print(f"Reason: {decision.reason}")
+    
+    # Execute the action
+    action = ActionFactory.create_action(decision)
+    result = await action_executor.execute_action(action)
+    print(f"Action Result: {action.get_action_type()}")
+    print(f"Halted Operations: {result.get('halted_operations', [])}")
+    
+    # Example 3: Anomaly detection (should be escalated)
+    print("\n⚠️  Example 3: Anomaly Detection")
+    print("-" * 30)
+    
+    # Simulate multiple rapid actions to trigger anomaly
+    for i in range(25):
+        rapid_action = ActionContext(
+            action_id=f"rapid_action_{i:03d}",
+            actor_id="suspicious_user",
+            action_type="api_call",
+            resource_id="api_endpoint"
+        )
+        anomaly_detector._update_patterns(rapid_action)
+    
+    # Now test the anomaly detection
+    anomaly_action = ActionContext(
+        action_id="anomaly_action_003",
+        actor_id="suspicious_user",
+        action_type="api_call",
+        resource_id="api_endpoint"
+    )
+    
+    decision = await reflexive_engine._evaluate_action(anomaly_action)
+    print(f"Decision: {decision.decision_type}")
+    print(f"Risk Level: {decision.risk_level}")
+    print(f"Reason: {decision.reason}")
+    
+    # Execute the action
+    action = ActionFactory.create_action(decision)
+    result = await action_executor.execute_action(action)
+    print(f"Action Result: {action.get_action_type()}")
+    print(f"Escalation Target: {result.get('escalation_target', 'N/A')}")
+    
+    # Example 4: Risk simulation
+    print("\n🎯 Example 4: Risk Simulation")
+    print("-" * 30)
+    
+    risk_scenario = {
+        "action_context": {
+            "action_id": "simulation_action",
+            "actor_id": "test_actor",
+            "action_type": "privilege_escalation",
+            "resource_id": "root_access",
+            "metadata": {"escalation_attempt": True}
+        },
+        "monitors": [
+            lambda ctx: {
+                "type": "violation",
+                "severity": "critical",
+                "violations": [{
+                    "rule": "privilege_escalation",
+                    "message": "Unauthorized privilege escalation attempt",
+                    "severity": "critical"
+                }]
+            }
+        ]
+    }
+    
+    decision = await reflexive_engine.simulate_risk(risk_scenario)
+    print(f"Simulation Decision: {decision.decision_type}")
+    print(f"Simulation Risk Level: {decision.risk_level}")
+    print(f"Simulation Reason: {decision.reason}")
+    
+    # Example 5: Monitor statistics
+    print("\n📊 Example 5: Monitor Statistics")
+    print("-" * 30)
+    
+    policy_stats = policy_monitor.get_violation_stats()
+    ledger_stats = ledger_monitor.get_integrity_stats()
+    anomaly_stats = anomaly_detector.get_anomaly_stats()
+    
+    print(f"Policy Monitor - Total Violations: {policy_stats['total_violations']}")
+    print(f"Policy Monitor - Actor Violations: {policy_stats['actor_violations']}")
+    print(f"Ledger Monitor - Total Checks: {ledger_stats['total_checks']}")
+    print(f"Anomaly Detector - Tracked Actors: {anomaly_stats['tracked_actors']}")
+    
+    # Example 6: Engine status
+    print("\n🔧 Example 6: Engine Status")
+    print("-" * 30)
+    
+    status = reflexive_engine.get_engine_status()
+    print(f"Engine Running: {status['is_running']}")
+    print(f"Monitor Count: {status['monitor_count']}")
+    print(f"Queue Size: {status['queue_size']}")
+    print(f"Decision Handlers: {status['decision_handlers']}")
+    
+    # Example 7: Action execution statistics
+    print("\n📈 Example 7: Action Execution Statistics")
+    print("-" * 30)
+    
+    exec_stats = action_executor.get_execution_stats()
+    print(f"Total Actions: {exec_stats['total_actions']}")
+    print(f"Completed Actions: {exec_stats['completed_actions']}")
+    print(f"Failed Actions: {exec_stats['failed_actions']}")
+    print(f"Success Rate: {exec_stats['success_rate']:.2%}")
+    
+    # Stop the reflexive engine
+    await reflexive_engine.stop()
+    print("\n✅ Reflexive engine stopped")
+    
+    print("\n🎉 Reflexive Core Example Complete!")
+    print("=" * 50)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/fastmcp/reflexive/__init__.py

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+"""FastMCP Reflexive Core - Self-monitoring and corrective action system."""
+
+from .engine import ReflexiveEngine, ReflexiveDecision, DecisionType, RiskLevel, ActionContext
+from .monitor import PolicyMonitor, LedgerMonitor, AnomalyDetector
+from .actions import HaltAction, EscalateAction, ReflexiveAction
+
+__all__ = [
+    "ReflexiveEngine",
+    "ReflexiveDecision", 
+    "DecisionType",
+    "RiskLevel",
+    "ActionContext",
+    "PolicyMonitor",
+    "LedgerMonitor", 
+    "AnomalyDetector",
+    "HaltAction",
+    "EscalateAction",
+    "ReflexiveAction"
+]

src/fastmcp/reflexive/actions.py

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+"""Action components for the reflexive core."""
+
+from abc import ABC, abstractmethod
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+from uuid import UUID, uuid4
+
+from pydantic import BaseModel, Field
+
+from .engine import ReflexiveDecision, ActionContext
+from fastmcp.utilities.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+class ReflexiveAction(BaseModel, ABC):
+    """Base class for reflexive actions."""
+    
+    action_id: UUID = Field(default_factory=uuid4, description="Unique action identifier")
+    decision: ReflexiveDecision = Field(..., description="The decision that triggered this action")
+    timestamp: datetime = Field(default_factory=datetime.utcnow, description="When the action was created")
+    status: str = Field(default="pending", description="Action status")
+    result: Optional[Dict[str, Any]] = Field(default=None, description="Action result")
+    
+    @abstractmethod
+    async def execute(self) -> Dict[str, Any]:
+        """Execute the reflexive action."""
+        pass
+    
+    @abstractmethod
+    def get_action_type(self) -> str:
+        """Get the type of this action."""
+        pass
+
+
+class HaltAction(ReflexiveAction):
+    """Action to halt unsafe execution."""
+    
+    halt_reason: str = Field(..., description="Reason for halting")
+    halt_level: str = Field(default="immediate", description="Level of halt (immediate, graceful, etc.)")
+    affected_operations: List[str] = Field(default_factory=list, description="Operations affected by the halt")
+    
+    async def execute(self) -> Dict[str, Any]:
+        """Execute the halt action."""
+        try:
+            self.status = "executing"
+            
+            # Log the halt
+            logger.critical(f"HALTING OPERATIONS: {self.halt_reason}")
+            logger.critical(f"Affected operations: {self.affected_operations}")
+            logger.critical(f"Decision ID: {self.decision.decision_id}")
+            
+            # In a real implementation, this would:
+            # 1. Stop the current operation
+            # 2. Cancel pending operations
+            # 3. Notify relevant systems
+            # 4. Update system state
+            
+            # Simulate halt execution
+            halt_result = {
+                "halted_operations": self.affected_operations,
+                "halt_timestamp": self.timestamp.isoformat(),
+                "halt_reason": self.halt_reason,
+                "halt_level": self.halt_level,
+                "decision_id": str(self.decision.decision_id)
+            }
+            
+            self.result = halt_result
+            self.status = "completed"
+            
+            logger.info(f"Halt action completed: {self.action_id}")
+            return halt_result
+            
+        except Exception as e:
+            self.status = "failed"
+            self.result = {"error": str(e)}
+            logger.error(f"Halt action failed: {e}")
+            raise
+    
+    def get_action_type(self) -> str:
+        """Get the type of this action."""
+        return "halt"
+
+
+class EscalateAction(ReflexiveAction):
+    """Action to escalate an issue to higher authority."""
+    
+    escalation_target: str = Field(..., description="Target role/entity for escalation")
+    escalation_priority: str = Field(default="normal", description="Priority of the escalation")
+    escalation_context: Dict[str, Any] = Field(default_factory=dict, description="Additional context for escalation")
+    notification_channels: List[str] = Field(default_factory=list, description="Channels to use for notification")
+    
+    async def execute(self) -> Dict[str, Any]:
+        """Execute the escalation action."""
+        try:
+            self.status = "executing"
+            
+            # Log the escalation
+            logger.warning(f"ESCALATING TO {self.escalation_target}: {self.decision.reason}")
+            logger.warning(f"Priority: {self.escalation_priority}")
+            logger.warning(f"Decision ID: {self.decision.decision_id}")
+            
+            # In a real implementation, this would:
+            # 1. Create escalation ticket/alert
+            # 2. Send notifications via configured channels
+            # 3. Update escalation tracking
+            # 4. Set up monitoring for response
+            
+            # Simulate escalation execution
+            escalation_result = {
+                "escalation_target": self.escalation_target,
+                "escalation_priority": self.escalation_priority,
+                "escalation_timestamp": self.timestamp.isoformat(),
+                "escalation_context": self.escalation_context,
+                "notification_channels": self.notification_channels,
+                "decision_id": str(self.decision.decision_id),
+                "action_context": self.decision.action_context.model_dump(mode='json')
+            }
+            
+            self.result = escalation_result
+            self.status = "completed"
+            
+            logger.info(f"Escalation action completed: {self.action_id}")
+            return escalation_result
+            
+        except Exception as e:
+            self.status = "failed"
+            self.result = {"error": str(e)}
+            logger.error(f"Escalation action failed: {e}")
+            raise
+    
+    def get_action_type(self) -> str:
+        """Get the type of this action."""
+        return "escalate"
+
+
+class MonitorAction(ReflexiveAction):
+    """Action to increase monitoring for an operation."""
+    
+    monitoring_level: str = Field(default="enhanced", description="Level of monitoring to apply")
+    monitoring_duration: int = Field(default=3600, description="Duration of enhanced monitoring in seconds")
+    monitoring_scope: List[str] = Field(default_factory=list, description="Scope of monitoring")
+    
+    async def execute(self) -> Dict[str, Any]:
+        """Execute the monitoring action."""
+        try:
+            self.status = "executing"
+            
+            # Log the monitoring increase
+            logger.info(f"ENHANCING MONITORING: {self.decision.reason}")
+            logger.info(f"Monitoring level: {self.monitoring_level}")
+            logger.info(f"Duration: {self.monitoring_duration} seconds")
+            
+            # In a real implementation, this would:
+            # 1. Increase logging verbosity
+            # 2. Add additional monitoring points
+            # 3. Set up alerts for specific conditions
+            # 4. Schedule monitoring reduction after duration
+            
+            # Simulate monitoring execution
+            monitoring_result = {
+                "monitoring_level": self.monitoring_level,
+                "monitoring_duration": self.monitoring_duration,
+                "monitoring_scope": self.monitoring_scope,
+                "monitoring_timestamp": self.timestamp.isoformat(),
+                "decision_id": str(self.decision.decision_id),
+                "action_context": self.decision.action_context.model_dump(mode='json')
+            }
+            
+            self.result = monitoring_result
+            self.status = "completed"
+            
+            logger.info(f"Monitoring action completed: {self.action_id}")
+            return monitoring_result
+            
+        except Exception as e:
+            self.status = "failed"
+            self.result = {"error": str(e)}
+            logger.error(f"Monitoring action failed: {e}")
+            raise
+    
+    def get_action_type(self) -> str:
+        """Get the type of this action."""
+        return "monitor"
+
+
+class AllowAction(ReflexiveAction):
+    """Action to allow an operation to proceed."""
+    
+    allow_conditions: List[str] = Field(default_factory=list, description="Conditions under which the action is allowed")
+    allow_restrictions: List[str] = Field(default_factory=list, description="Restrictions that still apply")
+    
+    async def execute(self) -> Dict[str, Any]:
+        """Execute the allow action."""
+        try:
+            self.status = "executing"
+            
+            # Log the allowance
+            logger.debug(f"ALLOWING OPERATION: {self.decision.action_context.action_id}")
+            logger.debug(f"Reason: {self.decision.reason}")
+            
+            # In a real implementation, this would:
+            # 1. Remove any temporary restrictions
+            # 2. Log the decision for audit
+            # 3. Continue normal operation flow
+            
+            # Simulate allow execution
+            allow_result = {
+                "allowed": True,
+                "allow_timestamp": self.timestamp.isoformat(),
+                "allow_conditions": self.allow_conditions,
+                "allow_restrictions": self.allow_restrictions,
+                "decision_id": str(self.decision.decision_id),
+                "action_context": self.decision.action_context.model_dump(mode='json')
+            }
+            
+            self.result = allow_result
+            self.status = "completed"
+            
+            logger.debug(f"Allow action completed: {self.action_id}")
+            return allow_result
+            
+        except Exception as e:
+            self.status = "failed"
+            self.result = {"error": str(e)}
+            logger.error(f"Allow action failed: {e}")
+            raise
+    
+    def get_action_type(self) -> str:
+        """Get the type of this action."""
+        return "allow"
+
+
+class ActionFactory:
+    """Factory for creating reflexive actions."""
+    
+    @staticmethod
+    def create_action(decision: ReflexiveDecision, **kwargs) -> ReflexiveAction:
+        """Create a reflexive action based on a decision."""
+        decision_type = decision.decision_type.value if hasattr(decision.decision_type, 'value') else str(decision.decision_type)
+        
+        if decision_type == "halt":
+            return HaltAction(
+                decision=decision,
+                halt_reason=decision.reason,
+                halt_level=kwargs.get("halt_level", "immediate"),
+                affected_operations=kwargs.get("affected_operations", [decision.action_context.action_id])
+            )
+        elif decision_type == "escalate":
+            return EscalateAction(
+                decision=decision,
+                escalation_target=decision.escalated_to or "default_admin",
+                escalation_priority=kwargs.get("escalation_priority", "normal"),
+                escalation_context=kwargs.get("escalation_context", {}),
+                notification_channels=kwargs.get("notification_channels", ["email", "slack"])
+            )
+        elif decision_type == "monitor":
+            return MonitorAction(
+                decision=decision,
+                monitoring_level=kwargs.get("monitoring_level", "enhanced"),
+                monitoring_duration=kwargs.get("monitoring_duration", 3600),
+                monitoring_scope=kwargs.get("monitoring_scope", [decision.action_context.actor_id])
+            )
+        elif decision_type == "allow":
+            return AllowAction(
+                decision=decision,
+                allow_conditions=kwargs.get("allow_conditions", []),
+                allow_restrictions=kwargs.get("allow_restrictions", [])
+            )
+        else:
+            raise ValueError(f"Unknown decision type: {decision.decision_type}")
+
+
+class ActionExecutor:
+    """Executor for reflexive actions."""
+    
+    def __init__(self):
+        """Initialize the action executor."""
+        self.execution_history = []
+        self.active_actions = {}
+    
+    async def execute_action(self, action: ReflexiveAction) -> Dict[str, Any]:
+        """Execute a reflexive action."""
+        try:
+            # Record the action
+            self.active_actions[str(action.action_id)] = action
+            self.execution_history.append({
+                "action_id": str(action.action_id),
+                "action_type": action.get_action_type(),
+                "decision_id": str(action.decision.decision_id),
+                "start_time": action.timestamp.isoformat(),
+                "status": action.status
+            })
+            
+            # Execute the action
+            result = await action.execute()
+            
+            # Update history
+            for record in self.execution_history:
+                if record["action_id"] == str(action.action_id):
+                    record["end_time"] = datetime.utcnow().isoformat()
+                    record["status"] = action.status
+                    record["result"] = result
+                    break
+            
+            # Remove from active actions
+            if str(action.action_id) in self.active_actions:
+                del self.active_actions[str(action.action_id)]
+            
+            return result
+            
+        except Exception as e:
+            # Update history with error
+            for record in self.execution_history:
+                if record["action_id"] == str(action.action_id):
+                    record["end_time"] = datetime.utcnow().isoformat()
+                    record["status"] = "failed"
+                    record["error"] = str(e)
+                    break
+            
+            # Remove from active actions
+            if str(action.action_id) in self.active_actions:
+                del self.active_actions[str(action.action_id)]
+            
+            raise
+    
+    def get_execution_stats(self) -> Dict[str, Any]:
+        """Get execution statistics."""
+        total_actions = len(self.execution_history)
+        completed_actions = len([a for a in self.execution_history if a.get("status") == "completed"])
+        failed_actions = len([a for a in self.execution_history if a.get("status") == "failed"])
+        active_actions = len(self.active_actions)
+        
+        return {
+            "total_actions": total_actions,
+            "completed_actions": completed_actions,
+            "failed_actions": failed_actions,
+            "active_actions": active_actions,
+            "success_rate": completed_actions / total_actions if total_actions > 0 else 0
+        }

src/fastmcp/reflexive/engine.py

@@ -0,0 +1,376 @@
+"""Reflexive Core Engine - Main runtime for self-monitoring and corrective actions."""
+
+import asyncio
+import json
+import hashlib
+from datetime import datetime
+from enum import Enum
+from typing import Any, Dict, List, Optional, Callable, Union
+from uuid import UUID, uuid4
+
+from pydantic import BaseModel, Field
+
+from fastmcp.utilities.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+class DecisionType(str, Enum):
+    """Types of reflexive decisions."""
+    HALT = "halt"
+    ESCALATE = "escalate"
+    MONITOR = "monitor"
+    ALLOW = "allow"
+
+
+class RiskLevel(str, Enum):
+    """Risk levels for reflexive decisions."""
+    LOW = "low"
+    MEDIUM = "medium"
+    HIGH = "high"
+    CRITICAL = "critical"
+
+
+class ActionContext(BaseModel):
+    """Context for an action being evaluated by the reflexive core."""
+    
+    action_id: str = Field(..., description="Unique identifier for the action")
+    actor_id: str = Field(..., description="ID of the entity performing the action")
+    action_type: str = Field(..., description="Type of action being performed")
+    resource_id: Optional[str] = Field(default=None, description="ID of the resource being accessed")
+    metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional action metadata")
+    timestamp: datetime = Field(default_factory=datetime.utcnow, description="When the action occurred")
+    session_id: Optional[str] = Field(default=None, description="Session identifier")
+    request_id: Optional[str] = Field(default=None, description="Request identifier")
+    
+    def get_context_hash(self) -> str:
+        """Get SHA-256 hash of action context for integrity verification."""
+        content = {
+            "action_id": self.action_id,
+            "actor_id": self.actor_id,
+            "action_type": self.action_type,
+            "resource_id": self.resource_id,
+            "metadata": self.metadata,
+            "session_id": self.session_id,
+            "request_id": self.request_id
+        }
+        content_str = json.dumps(content, sort_keys=True, default=str)
+        return hashlib.sha256(content_str.encode()).hexdigest()
+
+
+class ReflexiveDecision(BaseModel):
+    """A decision made by the reflexive core."""
+    
+    decision_id: UUID = Field(default_factory=uuid4, description="Unique decision identifier")
+    decision_type: DecisionType = Field(..., description="Type of decision made")
+    risk_level: RiskLevel = Field(..., description="Risk level of the situation")
+    action_context: ActionContext = Field(..., description="Context of the action being evaluated")
+    reason: str = Field(..., description="Reason for the decision")
+    evidence: Dict[str, Any] = Field(default_factory=dict, description="Evidence supporting the decision")
+    timestamp: datetime = Field(default_factory=datetime.utcnow, description="When the decision was made")
+    escalated_to: Optional[str] = Field(default=None, description="Role/entity escalated to")
+    proof_hash: Optional[str] = Field(default=None, description="Hash of decision proof")
+    
+    model_config = {"use_enum_values": True}
+    
+    def get_decision_hash(self) -> str:
+        """Get SHA-256 hash of decision for integrity verification."""
+        content = {
+            "decision_id": str(self.decision_id),
+            "decision_type": self.decision_type,
+            "risk_level": self.risk_level,
+            "action_context": self.action_context.model_dump(),
+            "reason": self.reason,
+            "evidence": self.evidence,
+            "escalated_to": self.escalated_to
+        }
+        content_str = json.dumps(content, sort_keys=True, default=str)
+        return hashlib.sha256(content_str.encode()).hexdigest()
+
+
+class ReflexiveEngine:
+    """Main reflexive core engine for self-monitoring and corrective actions."""
+    
+    def __init__(self, policy_engine=None, ledger=None):
+        """Initialize the reflexive engine.
+        
+        Args:
+            policy_engine: Policy engine instance for policy monitoring
+            ledger: Provenance ledger instance for audit logging
+        """
+        self.policy_engine = policy_engine
+        self.ledger = ledger
+        self.monitors: List[Callable] = []
+        self.decision_handlers: Dict[DecisionType, Callable] = {}
+        self.is_running = False
+        self.event_queue = asyncio.Queue()
+        
+        # Register default decision handlers
+        self._register_default_handlers()
+        
+        logger.info("Reflexive engine initialized")
+    
+    def _register_default_handlers(self):
+        """Register default decision handlers."""
+        self.decision_handlers[DecisionType.HALT] = self._handle_halt
+        self.decision_handlers[DecisionType.ESCALATE] = self._handle_escalate
+        self.decision_handlers[DecisionType.MONITOR] = self._handle_monitor
+        self.decision_handlers[DecisionType.ALLOW] = self._handle_allow
+    
+    async def start(self):
+        """Start the reflexive engine."""
+        if self.is_running:
+            logger.warning("Reflexive engine is already running")
+            return
+        
+        self.is_running = True
+        logger.info("Reflexive engine started")
+        
+        # Start the main event processing loop
+        asyncio.create_task(self._process_events())
+    
+    async def stop(self):
+        """Stop the reflexive engine."""
+        self.is_running = False
+        logger.info("Reflexive engine stopped")
+    
+    async def _process_events(self):
+        """Main event processing loop."""
+        while self.is_running:
+            try:
+                # Wait for events with timeout
+                event = await asyncio.wait_for(self.event_queue.get(), timeout=1.0)
+                await self._handle_event(event)
+            except asyncio.TimeoutError:
+                # No events, continue
+                continue
+            except Exception as e:
+                logger.error(f"Error processing reflexive event: {e}")
+    
+    async def _handle_event(self, event: Dict[str, Any]):
+        """Handle a reflexive event."""
+        try:
+            # Create action context from event
+            action_context = ActionContext(**event.get("action_context", {}))
+            
+            # Evaluate the action
+            decision = await self._evaluate_action(action_context)
+            
+            # Execute the decision
+            await self._execute_decision(decision)
+            
+            # Log the decision to audit trail
+            await self._log_decision(decision)
+            
+        except Exception as e:
+            logger.error(f"Error handling reflexive event: {e}")
+    
+    async def _evaluate_action(self, action_context: ActionContext) -> ReflexiveDecision:
+        """Evaluate an action and make a reflexive decision."""
+        # Run all monitors
+        violations = []
+        anomalies = []
+        
+        for monitor in self.monitors:
+            try:
+                # Check if monitor is async
+                if asyncio.iscoroutinefunction(monitor):
+                    result = await monitor(action_context)
+                else:
+                    result = monitor(action_context)
+                if result:
+                    if result.get("type") == "violation":
+                        violations.append(result)
+                    elif result.get("type") == "anomaly":
+                        anomalies.append(result)
+            except Exception as e:
+                logger.error(f"Monitor error: {e}")
+        
+        # Make decision based on findings
+        if violations or anomalies:
+            # Determine risk level
+            risk_level = self._assess_risk_level(violations, anomalies)
+            
+            # Make decision based on risk level
+            if risk_level == RiskLevel.CRITICAL:
+                decision_type = DecisionType.HALT
+                reason = f"Critical risk detected: {len(violations)} violations, {len(anomalies)} anomalies"
+            elif risk_level == RiskLevel.HIGH:
+                decision_type = DecisionType.HALT
+                reason = f"High risk detected: {len(violations)} violations, {len(anomalies)} anomalies"
+            elif risk_level == RiskLevel.MEDIUM:
+                decision_type = DecisionType.ESCALATE
+                reason = f"Medium risk detected: {len(violations)} violations, {len(anomalies)} anomalies"
+            else:
+                decision_type = DecisionType.MONITOR
+                reason = f"Low risk detected: {len(violations)} violations, {len(anomalies)} anomalies"
+        else:
+            decision_type = DecisionType.ALLOW
+            reason = "No violations or anomalies detected"
+            risk_level = RiskLevel.LOW
+        
+        # Create decision
+        decision = ReflexiveDecision(
+            decision_type=decision_type,
+            risk_level=risk_level,
+            action_context=action_context,
+            reason=reason,
+            evidence={
+                "violations": violations,
+                "anomalies": anomalies
+            }
+        )
+        
+        # Set proof hash
+        decision.proof_hash = decision.get_decision_hash()
+        
+        return decision
+    
+    def _assess_risk_level(self, violations: List[Dict], anomalies: List[Dict]) -> RiskLevel:
+        """Assess the overall risk level based on violations and anomalies."""
+        total_issues = len(violations) + len(anomalies)
+        
+        # Check for critical violations or anomalies
+        critical_violations = [v for v in violations if v.get("severity") == "critical"]
+        critical_anomalies = [a for a in anomalies if a.get("severity") == "critical"]
+        if critical_violations or critical_anomalies:
+            return RiskLevel.CRITICAL
+        
+        # Check for high severity issues
+        high_violations = [v for v in violations if v.get("severity") == "high"]
+        high_anomalies = [a for a in anomalies if a.get("severity") == "high"]
+        if high_violations or high_anomalies or total_issues >= 5:
+            return RiskLevel.HIGH
+        
+        # Check for medium severity issues
+        medium_violations = [v for v in violations if v.get("severity") == "medium"]
+        medium_anomalies = [a for a in anomalies if a.get("severity") == "medium"]
+        if medium_violations or medium_anomalies or total_issues >= 2:
+            return RiskLevel.MEDIUM
+        
+        return RiskLevel.LOW
+    
+    async def _execute_decision(self, decision: ReflexiveDecision):
+        """Execute a reflexive decision."""
+        handler = self.decision_handlers.get(decision.decision_type)
+        if handler:
+            try:
+                await handler(decision)
+            except Exception as e:
+                logger.error(f"Error executing decision {decision.decision_type}: {e}")
+        else:
+            logger.warning(f"No handler for decision type: {decision.decision_type}")
+    
+    async def _handle_halt(self, decision: ReflexiveDecision):
+        """Handle a halt decision."""
+        logger.critical(f"HALTING ACTION: {decision.action_context.action_id} - {decision.reason}")
+        # In a real implementation, this would stop the action execution
+        # For now, we just log the halt decision
+    
+    async def _handle_escalate(self, decision: ReflexiveDecision):
+        """Handle an escalate decision."""
+        # Determine escalation target
+        escalation_target = self._determine_escalation_target(decision)
+        decision.escalated_to = escalation_target
+        
+        logger.warning(f"ESCALATING TO {escalation_target}: {decision.action_context.action_id} - {decision.reason}")
+        # In a real implementation, this would notify the escalation target
+    
+    async def _handle_monitor(self, decision: ReflexiveDecision):
+        """Handle a monitor decision."""
+        logger.info(f"MONITORING ACTION: {decision.action_context.action_id} - {decision.reason}")
+        # In a real implementation, this would increase monitoring for this action
+    
+    async def _handle_allow(self, decision: ReflexiveDecision):
+        """Handle an allow decision."""
+        logger.debug(f"ALLOWING ACTION: {decision.action_context.action_id} - {decision.reason}")
+        # Action is allowed to proceed
+    
+    def _determine_escalation_target(self, decision: ReflexiveDecision) -> str:
+        """Determine the appropriate escalation target based on the decision."""
+        if decision.risk_level == RiskLevel.CRITICAL:
+            return "security_admin"
+        elif decision.risk_level == RiskLevel.HIGH:
+            return "system_admin"
+        else:
+            return "monitoring_team"
+    
+    async def _log_decision(self, decision: ReflexiveDecision):
+        """Log the reflexive decision to the audit trail."""
+        if self.ledger:
+            try:
+                from fastmcp.ledger import LedgerEvent, EventType
+                
+                event = LedgerEvent(
+                    event_type=EventType.REFLEXIVE_DECISION,
+                    actor_id="reflexive_core",
+                    resource_id=decision.action_context.action_id,
+                    action=f"reflexive_{decision.decision_type}",
+                    metadata={
+                        "decision_id": str(decision.decision_id),
+                        "risk_level": decision.risk_level,
+                        "reason": decision.reason,
+                        "proof_hash": decision.proof_hash,
+                        "escalated_to": decision.escalated_to
+                    }
+                )
+                
+                self.ledger.append_event(event)
+                logger.debug(f"Logged reflexive decision {decision.decision_id} to audit trail")
+                
+            except Exception as e:
+                logger.error(f"Failed to log reflexive decision: {e}")
+        else:
+            # Log to standard logger if no ledger is available
+            logger.info(f"Reflexive decision: {decision.decision_type} - {decision.reason} (Decision ID: {decision.decision_id})")
+    
+    def add_monitor(self, monitor: Callable):
+        """Add a monitor function to the reflexive engine."""
+        self.monitors.append(monitor)
+        logger.info(f"Added monitor: {monitor.__name__}")
+    
+    def remove_monitor(self, monitor: Callable):
+        """Remove a monitor function from the reflexive engine."""
+        if monitor in self.monitors:
+            self.monitors.remove(monitor)
+            logger.info(f"Removed monitor: {monitor.__name__}")
+    
+    async def submit_action(self, action_context: ActionContext):
+        """Submit an action for reflexive evaluation."""
+        event = {
+            "action_context": action_context.model_dump(),
+            "timestamp": datetime.utcnow().isoformat()
+        }
+        
+        await self.event_queue.put(event)
+        logger.debug(f"Submitted action {action_context.action_id} for reflexive evaluation")
+    
+    async def simulate_risk(self, risk_scenario: Dict[str, Any]) -> ReflexiveDecision:
+        """Simulate a risk scenario and return the reflexive decision."""
+        # Create action context from scenario
+        action_context = ActionContext(**risk_scenario.get("action_context", {}))
+        
+        # Override monitors temporarily for simulation
+        original_monitors = self.monitors.copy()
+        
+        # Add simulation monitors
+        simulation_monitors = risk_scenario.get("monitors", [])
+        for monitor_func in simulation_monitors:
+            self.monitors.append(monitor_func)
+        
+        try:
+            # Evaluate the action
+            decision = await self._evaluate_action(action_context)
+            return decision
+        finally:
+            # Restore original monitors
+            self.monitors = original_monitors
+    
+    def get_engine_status(self) -> Dict[str, Any]:
+        """Get the current status of the reflexive engine."""
+        return {
+            "is_running": self.is_running,
+            "monitor_count": len(self.monitors),
+            "queue_size": self.event_queue.qsize(),
+            "decision_handlers": list(self.decision_handlers.keys())
+        }

src/fastmcp/reflexive/monitor.py

@@ -0,0 +1,356 @@
+"""Monitoring components for the reflexive core."""
+
+import asyncio
+from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional, Callable
+from collections import defaultdict, deque
+
+from fastmcp.utilities.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+class PolicyMonitor:
+    """Monitor for policy violations."""
+    
+    def __init__(self, policy_engine=None):
+        """Initialize the policy monitor.
+        
+        Args:
+            policy_engine: Policy engine instance to monitor
+        """
+        self.policy_engine = policy_engine
+        self.violation_history = deque(maxlen=1000)  # Keep last 1000 violations
+        self.actor_violations = defaultdict(int)  # Track violations per actor
+        
+    async def __call__(self, action_context) -> Optional[Dict[str, Any]]:
+        """Monitor an action for policy violations."""
+        try:
+            # Check for policy violations (works with or without policy engine)
+            violations = await self._check_policy_violations(action_context)
+            
+            if violations:
+                # Record violation
+                violation_record = {
+                    "type": "violation",
+                    "severity": self._assess_violation_severity(violations),
+                    "violations": violations,
+                    "actor_id": action_context.actor_id,
+                    "action_id": action_context.action_id,
+                    "timestamp": datetime.utcnow().isoformat()
+                }
+                
+                self.violation_history.append(violation_record)
+                self.actor_violations[action_context.actor_id] += 1
+                
+                return violation_record
+            
+            return None
+            
+        except Exception as e:
+            logger.error(f"Policy monitor error: {e}")
+            return None
+    
+    async def _check_policy_violations(self, action_context) -> List[Dict[str, Any]]:
+        """Check for policy violations in an action."""
+        violations = []
+        
+        try:
+            # Simulate policy checking
+            # In a real implementation, this would use the actual policy engine
+            
+            # Check for suspicious patterns
+            if action_context.action_type == "admin_access" and action_context.actor_id.startswith("guest"):
+                violations.append({
+                    "rule": "admin_access_restriction",
+                    "message": "Guest user attempting admin access",
+                    "severity": "high"
+                })
+            
+            # Check for rate limiting
+            recent_violations = [v for v in self.violation_history 
+                               if v.get("actor_id") == action_context.actor_id 
+                               and datetime.fromisoformat(v["timestamp"]) > datetime.utcnow() - timedelta(minutes=5)]
+            
+            if len(recent_violations) >= 3:
+                violations.append({
+                    "rule": "rate_limit_exceeded",
+                    "message": f"Actor {action_context.actor_id} has {len(recent_violations)} recent violations",
+                    "severity": "medium"
+                })
+            
+            # Check for resource access patterns
+            if action_context.resource_id and "sensitive" in action_context.resource_id.lower():
+                if not action_context.metadata.get("authorized"):
+                    violations.append({
+                        "rule": "unauthorized_sensitive_access",
+                        "message": "Unauthorized access to sensitive resource",
+                        "severity": "critical"
+                    })
+            
+        except Exception as e:
+            logger.error(f"Error checking policy violations: {e}")
+        
+        return violations
+    
+    def _assess_violation_severity(self, violations: List[Dict[str, Any]]) -> str:
+        """Assess the overall severity of violations."""
+        if not violations:
+            return "low"
+        
+        severities = [v.get("severity", "low") for v in violations]
+        
+        if "critical" in severities:
+            return "critical"
+        elif "high" in severities:
+            return "high"
+        elif "medium" in severities:
+            return "medium"
+        else:
+            return "low"
+    
+    def get_violation_stats(self) -> Dict[str, Any]:
+        """Get violation statistics."""
+        return {
+            "total_violations": len(self.violation_history),
+            "actor_violations": dict(self.actor_violations),
+            "recent_violations": len([v for v in self.violation_history 
+                                    if datetime.fromisoformat(v["timestamp"]) > datetime.utcnow() - timedelta(hours=1)])
+        }
+
+
+class LedgerMonitor:
+    """Monitor for ledger inconsistencies and anomalies."""
+    
+    def __init__(self, ledger=None):
+        """Initialize the ledger monitor.
+        
+        Args:
+            ledger: Provenance ledger instance to monitor
+        """
+        self.ledger = ledger
+        self.integrity_checks = deque(maxlen=100)  # Keep last 100 integrity checks
+        
+    async def __call__(self, action_context) -> Optional[Dict[str, Any]]:
+        """Monitor ledger for inconsistencies."""
+        try:
+            if not self.ledger:
+                return None
+            
+            # Check ledger integrity
+            integrity_issues = await self._check_ledger_integrity()
+            
+            if integrity_issues:
+                # Record integrity issue
+                issue_record = {
+                    "type": "anomaly",
+                    "severity": self._assess_integrity_severity(integrity_issues),
+                    "issues": integrity_issues,
+                    "action_id": action_context.action_id,
+                    "timestamp": datetime.utcnow().isoformat()
+                }
+                
+                self.integrity_checks.append(issue_record)
+                return issue_record
+            
+            return None
+            
+        except Exception as e:
+            logger.error(f"Ledger monitor error: {e}")
+            return None
+    
+    async def _check_ledger_integrity(self) -> List[Dict[str, Any]]:
+        """Check ledger for integrity issues."""
+        issues = []
+        
+        try:
+            # Check chain integrity
+            is_valid = self.ledger.verify_chain_integrity()
+            if not is_valid:
+                issues.append({
+                    "type": "chain_integrity",
+                    "message": "Ledger chain integrity verification failed",
+                    "severity": "critical"
+                })
+            
+            # Check for missing blocks
+            stats = self.ledger.get_ledger_statistics()
+            if stats.get("total_entries", 0) > 0 and stats.get("total_blocks", 0) == 0:
+                issues.append({
+                    "type": "missing_blocks",
+                    "message": "Entries exist but no blocks found",
+                    "severity": "high"
+                })
+            
+            # Check for unsealed blocks
+            # This would require additional ledger methods to check for unsealed blocks
+            
+        except Exception as e:
+            logger.error(f"Error checking ledger integrity: {e}")
+            issues.append({
+                "type": "integrity_check_error",
+                "message": f"Error during integrity check: {str(e)}",
+                "severity": "medium"
+            })
+        
+        return issues
+    
+    def _assess_integrity_severity(self, issues: List[Dict[str, Any]]) -> str:
+        """Assess the overall severity of integrity issues."""
+        if not issues:
+            return "low"
+        
+        severities = [i.get("severity", "low") for i in issues]
+        
+        if "critical" in severities:
+            return "critical"
+        elif "high" in severities:
+            return "high"
+        elif "medium" in severities:
+            return "medium"
+        else:
+            return "low"
+    
+    def get_integrity_stats(self) -> Dict[str, Any]:
+        """Get integrity check statistics."""
+        return {
+            "total_checks": len(self.integrity_checks),
+            "recent_issues": len([i for i in self.integrity_checks 
+                                if datetime.fromisoformat(i["timestamp"]) > datetime.utcnow() - timedelta(hours=1)])
+        }
+
+
+class AnomalyDetector:
+    """Detector for behavioral anomalies."""
+    
+    def __init__(self):
+        """Initialize the anomaly detector."""
+        self.actor_patterns = defaultdict(lambda: {
+            "action_counts": defaultdict(int),
+            "resource_access": defaultdict(int),
+            "session_times": deque(maxlen=100),
+            "last_seen": None
+        })
+        self.global_patterns = {
+            "action_frequency": defaultdict(int),
+            "resource_access": defaultdict(int),
+            "time_patterns": defaultdict(int)
+        }
+        
+    async def __call__(self, action_context) -> Optional[Dict[str, Any]]:
+        """Detect anomalies in an action."""
+        try:
+            # Update patterns
+            self._update_patterns(action_context)
+            
+            # Detect anomalies
+            anomalies = await self._detect_anomalies(action_context)
+            
+            if anomalies:
+                return {
+                    "type": "anomaly",
+                    "severity": self._assess_anomaly_severity(anomalies),
+                    "anomalies": anomalies,
+                    "actor_id": action_context.actor_id,
+                    "action_id": action_context.action_id,
+                    "timestamp": datetime.utcnow().isoformat()
+                }
+            
+            return None
+            
+        except Exception as e:
+            logger.error(f"Anomaly detector error: {e}")
+            return None
+    
+    def _update_patterns(self, action_context):
+        """Update behavioral patterns."""
+        actor_id = action_context.actor_id
+        actor_data = self.actor_patterns[actor_id]
+        
+        # Update action counts
+        actor_data["action_counts"][action_context.action_type] += 1
+        self.global_patterns["action_frequency"][action_context.action_type] += 1
+        
+        # Update resource access
+        if action_context.resource_id:
+            actor_data["resource_access"][action_context.resource_id] += 1
+            self.global_patterns["resource_access"][action_context.resource_id] += 1
+        
+        # Update session times
+        actor_data["session_times"].append(action_context.timestamp)
+        actor_data["last_seen"] = action_context.timestamp
+        
+        # Update time patterns
+        hour = action_context.timestamp.hour
+        self.global_patterns["time_patterns"][hour] += 1
+    
+    async def _detect_anomalies(self, action_context) -> List[Dict[str, Any]]:
+        """Detect anomalies in the action."""
+        anomalies = []
+        actor_id = action_context.actor_id
+        actor_data = self.actor_patterns[actor_id]
+        
+        # Check for unusual action frequency
+        if len(actor_data["session_times"]) >= 10:
+            recent_actions = [t for t in actor_data["session_times"] 
+                            if t > datetime.utcnow() - timedelta(minutes=5)]
+            if len(recent_actions) > 20:  # More than 20 actions in 5 minutes
+                anomalies.append({
+                    "type": "high_frequency",
+                    "message": f"Actor {actor_id} performing {len(recent_actions)} actions in 5 minutes",
+                    "severity": "medium"
+                })
+        
+        # Check for unusual time patterns
+        current_hour = action_context.timestamp.hour
+        if current_hour < 6 or current_hour > 22:  # Unusual hours
+            if actor_data["action_counts"].get(action_context.action_type, 0) < 5:  # New action type
+                anomalies.append({
+                    "type": "unusual_timing",
+                    "message": f"Actor {actor_id} performing {action_context.action_type} at unusual hour {current_hour}",
+                    "severity": "low"
+                })
+        
+        # Check for new resource access
+        if action_context.resource_id:
+            if actor_data["resource_access"].get(action_context.resource_id, 0) == 1:
+                # First time accessing this resource
+                anomalies.append({
+                    "type": "new_resource_access",
+                    "message": f"Actor {actor_id} accessing new resource {action_context.resource_id}",
+                    "severity": "low"
+                })
+        
+        # Check for privilege escalation patterns
+        if action_context.action_type in ["admin_access", "root_access", "privilege_escalation"]:
+            if actor_data["action_counts"].get(action_context.action_type, 0) == 1:
+                # First time performing privileged action
+                anomalies.append({
+                    "type": "privilege_escalation",
+                    "message": f"Actor {actor_id} attempting privileged action for first time",
+                    "severity": "high"
+                })
+        
+        return anomalies
+    
+    def _assess_anomaly_severity(self, anomalies: List[Dict[str, Any]]) -> str:
+        """Assess the overall severity of anomalies."""
+        if not anomalies:
+            return "low"
+        
+        severities = [a.get("severity", "low") for a in anomalies]
+        
+        if "high" in severities:
+            return "high"
+        elif "medium" in severities:
+            return "medium"
+        else:
+            return "low"
+    
+    def get_anomaly_stats(self) -> Dict[str, Any]:
+        """Get anomaly detection statistics."""
+        return {
+            "tracked_actors": len(self.actor_patterns),
+            "global_action_types": len(self.global_patterns["action_frequency"]),
+            "global_resources": len(self.global_patterns["resource_access"])
+        }

src/fastmcp/server/reflexive_routes.py

@@ -0,0 +1,385 @@
+"""Reflexive Core HTTP routes."""
+
+from typing import Any, Dict, List, Optional
+from uuid import UUID
+
+from starlette.requests import Request
+from starlette.responses import JSONResponse
+from starlette.routing import Route
+
+from fastmcp.reflexive import ReflexiveEngine, ActionContext, DecisionType, RiskLevel
+from fastmcp.reflexive.actions import ActionFactory, ActionExecutor
+from fastmcp.utilities.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+async def simulate_risk_endpoint(request: Request) -> JSONResponse:
+    """HTTP endpoint for simulating risk scenarios.
+    
+    Expected JSON body:
+    {
+        "action_context": {
+            "action_id": "test_action_123",
+            "actor_id": "test_user",
+            "action_type": "admin_access",
+            "resource_id": "sensitive_data",
+            "metadata": {...}
+        },
+        "monitors": [
+            // List of monitor functions (for simulation)
+        ],
+        "scenario_type": "policy_violation" | "anomaly" | "integrity_issue"
+    }
+    """
+    try:
+        # Parse request body
+        body = await request.json()
+        
+        # Get reflexive engine from request state
+        reflexive_engine: ReflexiveEngine = request.app.state.reflexive_engine
+        
+        # Validate required fields
+        if "action_context" not in body:
+            return JSONResponse(
+                status_code=400,
+                content={
+                    "error": "Missing required field: action_context"
+                }
+            )
+        
+        # Simulate the risk scenario
+        decision = await reflexive_engine.simulate_risk(body)
+        
+        # Create and execute the corresponding action
+        action = ActionFactory.create_action(decision)
+        executor = ActionExecutor()
+        action_result = await executor.execute_action(action)
+        
+        return JSONResponse(
+            status_code=200,
+            content={
+                "simulation_id": str(decision.decision_id),
+                "decision": {
+                    "decision_id": str(decision.decision_id),
+                    "decision_type": decision.decision_type,
+                    "risk_level": decision.risk_level,
+                    "reason": decision.reason,
+                    "evidence": decision.evidence,
+                    "proof_hash": decision.proof_hash,
+                    "escalated_to": decision.escalated_to,
+                    "timestamp": decision.timestamp.isoformat()
+                },
+                "action": {
+                    "action_id": str(action.action_id),
+                    "action_type": action.get_action_type(),
+                    "status": action.status,
+                    "result": action_result
+                },
+                "action_context": decision.action_context.model_dump(mode='json')
+            }
+        )
+        
+    except ValueError as e:
+        logger.error(f"Invalid simulation data: {e}")
+        return JSONResponse(
+            status_code=400,
+            content={
+                "error": "Invalid simulation data",
+                "reason": str(e)
+            }
+        )
+    except Exception as e:
+        logger.error(f"Failed to simulate risk: {e}")
+        return JSONResponse(
+            status_code=500,
+            content={
+                "error": "Failed to simulate risk",
+                "reason": str(e)
+            }
+        )
+
+
+async def get_engine_status_endpoint(request: Request) -> JSONResponse:
+    """HTTP endpoint for getting reflexive engine status."""
+    try:
+        # Get reflexive engine from request state
+        reflexive_engine: ReflexiveEngine = request.app.state.reflexive_engine
+        
+        # Get engine status
+        status = reflexive_engine.get_engine_status()
+        
+        return JSONResponse(
+            status_code=200,
+            content=status
+        )
+        
+    except Exception as e:
+        logger.error(f"Failed to get engine status: {e}")
+        return JSONResponse(
+            status_code=500,
+            content={
+                "error": "Failed to get engine status",
+                "reason": str(e)
+            }
+        )
+
+
+async def submit_action_endpoint(request: Request) -> JSONResponse:
+    """HTTP endpoint for submitting an action for reflexive evaluation.
+    
+    Expected JSON body:
+    {
+        "action_id": "action_123",
+        "actor_id": "user_456",
+        "action_type": "tool_call",
+        "resource_id": "resource_789",
+        "metadata": {...},
+        "session_id": "session_abc",
+        "request_id": "request_def"
+    }
+    """
+    try:
+        # Parse request body
+        body = await request.json()
+        
+        # Get reflexive engine from request state
+        reflexive_engine: ReflexiveEngine = request.app.state.reflexive_engine
+        
+        # Create action context
+        action_context = ActionContext(**body)
+        
+        # Submit action for evaluation
+        await reflexive_engine.submit_action(action_context)
+        
+        return JSONResponse(
+            status_code=202,
+            content={
+                "message": "Action submitted for reflexive evaluation",
+                "action_id": action_context.action_id,
+                "submitted_at": action_context.timestamp.isoformat()
+            }
+        )
+        
+    except ValueError as e:
+        logger.error(f"Invalid action data: {e}")
+        return JSONResponse(
+            status_code=400,
+            content={
+                "error": "Invalid action data",
+                "reason": str(e)
+            }
+        )
+    except Exception as e:
+        logger.error(f"Failed to submit action: {e}")
+        return JSONResponse(
+            status_code=500,
+            content={
+                "error": "Failed to submit action",
+                "reason": str(e)
+            }
+        )
+
+
+async def get_monitor_stats_endpoint(request: Request) -> JSONResponse:
+    """HTTP endpoint for getting monitor statistics."""
+    try:
+        # Get reflexive engine from request state
+        reflexive_engine: ReflexiveEngine = request.app.state.reflexive_engine
+        
+        # Collect stats from all monitors
+        stats = {}
+        
+        for monitor in reflexive_engine.monitors:
+            if hasattr(monitor, 'get_violation_stats'):
+                stats['policy_monitor'] = monitor.get_violation_stats()
+            elif hasattr(monitor, 'get_integrity_stats'):
+                stats['ledger_monitor'] = monitor.get_integrity_stats()
+            elif hasattr(monitor, 'get_anomaly_stats'):
+                stats['anomaly_detector'] = monitor.get_anomaly_stats()
+        
+        return JSONResponse(
+            status_code=200,
+            content=stats
+        )
+        
+    except Exception as e:
+        logger.error(f"Failed to get monitor stats: {e}")
+        return JSONResponse(
+            status_code=500,
+            content={
+                "error": "Failed to get monitor stats",
+                "reason": str(e)
+            }
+        )
+
+
+async def create_risk_scenario_endpoint(request: Request) -> JSONResponse:
+    """HTTP endpoint for creating predefined risk scenarios.
+    
+    Expected JSON body:
+    {
+        "scenario_name": "admin_privilege_escalation",
+        "scenario_type": "policy_violation",
+        "parameters": {
+            "actor_type": "guest_user",
+            "target_resource": "admin_panel",
+            "severity": "high"
+        }
+    }
+    """
+    try:
+        # Parse request body
+        body = await request.json()
+        
+        scenario_name = body.get("scenario_name")
+        scenario_type = body.get("scenario_type")
+        parameters = body.get("parameters", {})
+        
+        if not scenario_name or not scenario_type:
+            return JSONResponse(
+                status_code=400,
+                content={
+                    "error": "Missing required fields: scenario_name, scenario_type"
+                }
+            )
+        
+        # Create scenario based on type
+        scenario = _create_risk_scenario(scenario_name, scenario_type, parameters)
+        
+        return JSONResponse(
+            status_code=200,
+            content={
+                "scenario": scenario,
+                "message": f"Risk scenario '{scenario_name}' created successfully"
+            }
+        )
+        
+    except Exception as e:
+        logger.error(f"Failed to create risk scenario: {e}")
+        return JSONResponse(
+            status_code=500,
+            content={
+                "error": "Failed to create risk scenario",
+                "reason": str(e)
+            }
+        )
+
+
+def _create_risk_scenario(scenario_name: str, scenario_type: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
+    """Create a predefined risk scenario."""
+    scenarios = {
+        "admin_privilege_escalation": {
+            "action_context": {
+                "action_id": f"admin_escalation_{scenario_name}",
+                "actor_id": parameters.get("actor_type", "guest_user"),
+                "action_type": "admin_access",
+                "resource_id": parameters.get("target_resource", "admin_panel"),
+                "metadata": {
+                    "privilege_level": "admin",
+                    "escalation_attempt": True
+                }
+            },
+            "expected_decision": "halt",
+            "expected_risk_level": "high"
+        },
+        "suspicious_activity": {
+            "action_context": {
+                "action_id": f"suspicious_{scenario_name}",
+                "actor_id": "suspicious_user",
+                "action_type": "data_access",
+                "resource_id": "sensitive_data",
+                "metadata": {
+                    "access_pattern": "unusual",
+                    "time_of_day": "off_hours"
+                }
+            },
+            "expected_decision": "escalate",
+            "expected_risk_level": "medium"
+        },
+        "integrity_violation": {
+            "action_context": {
+                "action_id": f"integrity_{scenario_name}",
+                "actor_id": "system",
+                "action_type": "ledger_modification",
+                "resource_id": "provenance_ledger",
+                "metadata": {
+                    "modification_type": "unauthorized",
+                    "integrity_check": "failed"
+                }
+            },
+            "expected_decision": "halt",
+            "expected_risk_level": "critical"
+        },
+        "rate_limit_exceeded": {
+            "action_context": {
+                "action_id": f"rate_limit_{scenario_name}",
+                "actor_id": "high_frequency_user",
+                "action_type": "api_call",
+                "resource_id": "api_endpoint",
+                "metadata": {
+                    "request_count": 1000,
+                    "time_window": "1_minute"
+                }
+            },
+            "expected_decision": "escalate",
+            "expected_risk_level": "medium"
+        }
+    }
+    
+    return scenarios.get(scenario_name, {
+        "action_context": {
+            "action_id": f"custom_{scenario_name}",
+            "actor_id": "test_user",
+            "action_type": "custom_action",
+            "resource_id": "test_resource",
+            "metadata": parameters
+        },
+        "expected_decision": "monitor",
+        "expected_risk_level": "low"
+    })
+
+
+def create_reflexive_routes(reflexive_engine: ReflexiveEngine) -> List[Route]:
+    """Create reflexive core routes.
+    
+    Args:
+        reflexive_engine: The reflexive engine instance
+        
+    Returns:
+        List of Starlette Route objects for reflexive core management
+    """
+    def endpoint_with_engine(endpoint_func):
+        async def wrapper(request: Request) -> JSONResponse:
+            # Store reflexive engine in app state for access in endpoint
+            request.app.state.reflexive_engine = reflexive_engine
+            return await endpoint_func(request)
+        return wrapper
+    
+    return [
+        Route(
+            path="/core/simulate-risk",
+            endpoint=endpoint_with_engine(simulate_risk_endpoint),
+            methods=["POST"]
+        ),
+        Route(
+            path="/core/status",
+            endpoint=endpoint_with_engine(get_engine_status_endpoint),
+            methods=["GET"]
+        ),
+        Route(
+            path="/core/submit-action",
+            endpoint=endpoint_with_engine(submit_action_endpoint),
+            methods=["POST"]
+        ),
+        Route(
+            path="/core/monitor-stats",
+            endpoint=endpoint_with_engine(get_monitor_stats_endpoint),
+            methods=["GET"]
+        ),
+        Route(
+            path="/core/risk-scenario",
+            endpoint=endpoint_with_engine(create_risk_scenario_endpoint),
+            methods=["POST"]
+        )
+    ]

src/fastmcp/server/server.py

@@ -64,6 +64,7 @@ from fastmcp.tools import ToolManager
 from fastmcp.tools.tool import FunctionTool, Tool, ToolResult
 from fastmcp.tools.tool_transform import ToolTransformConfig
 from fastmcp.policy import PolicyEngine
+from fastmcp.reflexive import ReflexiveEngine
 from fastmcp.utilities.cli import log_server_banner
 from fastmcp.utilities.components import FastMCPComponent
 from fastmcp.utilities.logging import get_logger
@@ -176,6 +177,7 @@ class FastMCP(Generic[LifespanResultT]):
         self._additional_http_routes: list[BaseRoute] = []
         self._mounted_servers: list[MountedServer] = []
         self._policy_engine: Optional[PolicyEngine] = None
+        self._reflexive_engine: Optional[ReflexiveEngine] = None
         self._tool_manager = ToolManager(
             duplicate_behavior=on_duplicate_tools,
             mask_error_details=mask_error_details,
@@ -514,6 +516,14 @@ class FastMCP(Generic[LifespanResultT]):
             policy_route = create_policy_evaluate_route(self._policy_engine)
             routes.append(policy_route)
 
+
+        # Add reflexive core endpoints if reflexive engine is configured
+        if self._reflexive_engine is not None:
+            from fastmcp.server.reflexive_routes import create_reflexive_routes
+            
+            reflexive_routes = create_reflexive_routes(self._reflexive_engine)
+            routes.extend(reflexive_routes)
+
         # Recursively get routes from mounted servers
         for mounted_server in self._mounted_servers:
             mounted_routes = mounted_server.server._get_additional_http_routes()
@@ -545,6 +555,34 @@ class FastMCP(Generic[LifespanResultT]):
         """
         return self._policy_engine
 
+
+    def enable_reflexive_core(self, reflexive_engine: Optional[ReflexiveEngine] = None) -> ReflexiveEngine:
+        """Enable the reflexive core for this server.
+        
+        Args:
+            reflexive_engine: Optional reflexive engine instance. If None, creates a new one.
+            
+        Returns:
+            The reflexive engine instance
+        """
+        if reflexive_engine is None:
+            reflexive_engine = ReflexiveEngine(
+                policy_engine=self._policy_engine,
+                ledger=None
+            )
+        
+        self._reflexive_engine = reflexive_engine
+        logger.info("Reflexive core enabled for server")
+        return reflexive_engine
+
+    def get_reflexive_engine(self) -> Optional[ReflexiveEngine]:
+        """Get the reflexive engine instance.
+        
+        Returns:
+            The reflexive engine instance, or None if not enabled
+        """
+        return self._reflexive_engine
+
     async def _mcp_list_tools(self) -> list[MCPTool]:
         logger.debug("Handler called: list_tools")
 

tests/reflexive/__init__.py

@@ -0,0 +1 @@
+"""Tests for the reflexive core module."""

tests/reflexive/test_actions.py

@@ -0,0 +1,461 @@
+"""Tests for the reflexive actions."""
+
+import pytest
+from datetime import datetime
+from unittest.mock import Mock
+
+from fastmcp.reflexive.actions import (
+    HaltAction, EscalateAction, MonitorAction, AllowAction,
+    ActionFactory, ActionExecutor
+)
+from fastmcp.reflexive.engine import ReflexiveDecision, ActionContext, DecisionType, RiskLevel
+
+
+class TestHaltAction:
+    """Test the HaltAction class."""
+    
+    @pytest.fixture
+    def halt_decision(self):
+        """Create a halt decision for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="admin_access"
+        )
+        
+        return ReflexiveDecision(
+            decision_type=DecisionType.HALT,
+            risk_level=RiskLevel.HIGH,
+            action_context=action_context,
+            reason="Unauthorized admin access attempt"
+        )
+    
+    @pytest.fixture
+    def halt_action(self, halt_decision):
+        """Create a halt action for testing."""
+        return HaltAction(
+            decision=halt_decision,
+            halt_reason="Security violation detected",
+            halt_level="immediate",
+            affected_operations=["admin_access", "user_management"]
+        )
+    
+    def test_halt_action_creation(self, halt_action, halt_decision):
+        """Test halt action creation."""
+        assert halt_action.decision == halt_decision
+        assert halt_action.halt_reason == "Security violation detected"
+        assert halt_action.halt_level == "immediate"
+        assert halt_action.affected_operations == ["admin_access", "user_management"]
+        assert halt_action.status == "pending"
+        assert halt_action.result is None
+    
+    async def test_halt_action_execution(self, halt_action):
+        """Test halt action execution."""
+        result = await halt_action.execute()
+        
+        assert halt_action.status == "completed"
+        assert halt_action.result is not None
+        assert result["halted_operations"] == ["admin_access", "user_management"]
+        assert result["halt_reason"] == "Security violation detected"
+        assert result["halt_level"] == "immediate"
+        assert "halt_timestamp" in result
+        assert "decision_id" in result
+    
+    def test_halt_action_type(self, halt_action):
+        """Test halt action type."""
+        assert halt_action.get_action_type() == "halt"
+
+
+class TestEscalateAction:
+    """Test the EscalateAction class."""
+    
+    @pytest.fixture
+    def escalate_decision(self):
+        """Create an escalate decision for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="data_access"
+        )
+        
+        return ReflexiveDecision(
+            decision_type=DecisionType.ESCALATE,
+            risk_level=RiskLevel.MEDIUM,
+            action_context=action_context,
+            reason="Suspicious data access pattern"
+        )
+    
+    @pytest.fixture
+    def escalate_action(self, escalate_decision):
+        """Create an escalate action for testing."""
+        return EscalateAction(
+            decision=escalate_decision,
+            escalation_target="security_team",
+            escalation_priority="high",
+            escalation_context={"alert_level": "medium"},
+            notification_channels=["email", "slack"]
+        )
+    
+    def test_escalate_action_creation(self, escalate_action, escalate_decision):
+        """Test escalate action creation."""
+        assert escalate_action.decision == escalate_decision
+        assert escalate_action.escalation_target == "security_team"
+        assert escalate_action.escalation_priority == "high"
+        assert escalate_action.escalation_context == {"alert_level": "medium"}
+        assert escalate_action.notification_channels == ["email", "slack"]
+        assert escalate_action.status == "pending"
+        assert escalate_action.result is None
+    
+    async def test_escalate_action_execution(self, escalate_action):
+        """Test escalate action execution."""
+        result = await escalate_action.execute()
+        
+        assert escalate_action.status == "completed"
+        assert escalate_action.result is not None
+        assert result["escalation_target"] == "security_team"
+        assert result["escalation_priority"] == "high"
+        assert result["escalation_context"] == {"alert_level": "medium"}
+        assert result["notification_channels"] == ["email", "slack"]
+        assert "escalation_timestamp" in result
+        assert "decision_id" in result
+        assert "action_context" in result
+    
+    def test_escalate_action_type(self, escalate_action):
+        """Test escalate action type."""
+        assert escalate_action.get_action_type() == "escalate"
+
+
+class TestMonitorAction:
+    """Test the MonitorAction class."""
+    
+    @pytest.fixture
+    def monitor_decision(self):
+        """Create a monitor decision for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="api_call"
+        )
+        
+        return ReflexiveDecision(
+            decision_type=DecisionType.MONITOR,
+            risk_level=RiskLevel.LOW,
+            action_context=action_context,
+            reason="Unusual but not suspicious activity"
+        )
+    
+    @pytest.fixture
+    def monitor_action(self, monitor_decision):
+        """Create a monitor action for testing."""
+        return MonitorAction(
+            decision=monitor_decision,
+            monitoring_level="enhanced",
+            monitoring_duration=1800,
+            monitoring_scope=["test_user", "api_calls"]
+        )
+    
+    def test_monitor_action_creation(self, monitor_action, monitor_decision):
+        """Test monitor action creation."""
+        assert monitor_action.decision == monitor_decision
+        assert monitor_action.monitoring_level == "enhanced"
+        assert monitor_action.monitoring_duration == 1800
+        assert monitor_action.monitoring_scope == ["test_user", "api_calls"]
+        assert monitor_action.status == "pending"
+        assert monitor_action.result is None
+    
+    async def test_monitor_action_execution(self, monitor_action):
+        """Test monitor action execution."""
+        result = await monitor_action.execute()
+        
+        assert monitor_action.status == "completed"
+        assert monitor_action.result is not None
+        assert result["monitoring_level"] == "enhanced"
+        assert result["monitoring_duration"] == 1800
+        assert result["monitoring_scope"] == ["test_user", "api_calls"]
+        assert "monitoring_timestamp" in result
+        assert "decision_id" in result
+        assert "action_context" in result
+    
+    def test_monitor_action_type(self, monitor_action):
+        """Test monitor action type."""
+        assert monitor_action.get_action_type() == "monitor"
+
+
+class TestAllowAction:
+    """Test the AllowAction class."""
+    
+    @pytest.fixture
+    def allow_decision(self):
+        """Create an allow decision for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="normal_operation"
+        )
+        
+        return ReflexiveDecision(
+            decision_type=DecisionType.ALLOW,
+            risk_level=RiskLevel.LOW,
+            action_context=action_context,
+            reason="No violations or anomalies detected"
+        )
+    
+    @pytest.fixture
+    def allow_action(self, allow_decision):
+        """Create an allow action for testing."""
+        return AllowAction(
+            decision=allow_decision,
+            allow_conditions=["authenticated", "authorized"],
+            allow_restrictions=["rate_limited"]
+        )
+    
+    def test_allow_action_creation(self, allow_action, allow_decision):
+        """Test allow action creation."""
+        assert allow_action.decision == allow_decision
+        assert allow_action.allow_conditions == ["authenticated", "authorized"]
+        assert allow_action.allow_restrictions == ["rate_limited"]
+        assert allow_action.status == "pending"
+        assert allow_action.result is None
+    
+    async def test_allow_action_execution(self, allow_action):
+        """Test allow action execution."""
+        result = await allow_action.execute()
+        
+        assert allow_action.status == "completed"
+        assert allow_action.result is not None
+        assert result["allowed"] is True
+        assert result["allow_conditions"] == ["authenticated", "authorized"]
+        assert result["allow_restrictions"] == ["rate_limited"]
+        assert "allow_timestamp" in result
+        assert "decision_id" in result
+        assert "action_context" in result
+    
+    def test_allow_action_type(self, allow_action):
+        """Test allow action type."""
+        assert allow_action.get_action_type() == "allow"
+
+
+class TestActionFactory:
+    """Test the ActionFactory class."""
+    
+    @pytest.fixture
+    def halt_decision(self):
+        """Create a halt decision for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="admin_access"
+        )
+        
+        return ReflexiveDecision(
+            decision_type=DecisionType.HALT,
+            risk_level=RiskLevel.HIGH,
+            action_context=action_context,
+            reason="Security violation"
+        )
+    
+    @pytest.fixture
+    def escalate_decision(self):
+        """Create an escalate decision for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="data_access"
+        )
+        
+        return ReflexiveDecision(
+            decision_type=DecisionType.ESCALATE,
+            risk_level=RiskLevel.MEDIUM,
+            action_context=action_context,
+            reason="Suspicious activity"
+        )
+    
+    @pytest.fixture
+    def monitor_decision(self):
+        """Create a monitor decision for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="api_call"
+        )
+        
+        return ReflexiveDecision(
+            decision_type=DecisionType.MONITOR,
+            risk_level=RiskLevel.LOW,
+            action_context=action_context,
+            reason="Unusual activity"
+        )
+    
+    @pytest.fixture
+    def allow_decision(self):
+        """Create an allow decision for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="normal_operation"
+        )
+        
+        return ReflexiveDecision(
+            decision_type=DecisionType.ALLOW,
+            risk_level=RiskLevel.LOW,
+            action_context=action_context,
+            reason="No issues detected"
+        )
+    
+    def test_create_halt_action(self, halt_decision):
+        """Test creating a halt action."""
+        action = ActionFactory.create_action(halt_decision)
+        
+        assert isinstance(action, HaltAction)
+        assert action.decision == halt_decision
+        assert action.halt_reason == halt_decision.reason
+        assert action.halt_level == "immediate"
+        assert halt_decision.action_context.action_id in action.affected_operations
+    
+    def test_create_escalate_action(self, escalate_decision):
+        """Test creating an escalate action."""
+        action = ActionFactory.create_action(escalate_decision)
+        
+        assert isinstance(action, EscalateAction)
+        assert action.decision == escalate_decision
+        assert action.escalation_target == "default_admin"
+        assert action.escalation_priority == "normal"
+    
+    def test_create_monitor_action(self, monitor_decision):
+        """Test creating a monitor action."""
+        action = ActionFactory.create_action(monitor_decision)
+        
+        assert isinstance(action, MonitorAction)
+        assert action.decision == monitor_decision
+        assert action.monitoring_level == "enhanced"
+        assert action.monitoring_duration == 3600
+    
+    def test_create_allow_action(self, allow_decision):
+        """Test creating an allow action."""
+        action = ActionFactory.create_action(allow_decision)
+        
+        assert isinstance(action, AllowAction)
+        assert action.decision == allow_decision
+        assert action.allow_conditions == []
+        assert action.allow_restrictions == []
+    
+    def test_create_action_with_kwargs(self, halt_decision):
+        """Test creating an action with additional kwargs."""
+        action = ActionFactory.create_action(
+            halt_decision,
+            halt_level="graceful",
+            affected_operations=["operation1", "operation2"]
+        )
+        
+        assert isinstance(action, HaltAction)
+        assert action.halt_level == "graceful"
+        assert action.affected_operations == ["operation1", "operation2"]
+    
+    def test_create_action_unknown_type(self, halt_decision):
+        """Test creating an action with unknown decision type."""
+        halt_decision.decision_type = "unknown_type"  # type: ignore
+        
+        with pytest.raises(ValueError, match="Unknown decision type"):
+            ActionFactory.create_action(halt_decision)
+
+
+class TestActionExecutor:
+    """Test the ActionExecutor class."""
+    
+    @pytest.fixture
+    def action_executor(self):
+        """Create an action executor for testing."""
+        return ActionExecutor()
+    
+    @pytest.fixture
+    def halt_action(self):
+        """Create a halt action for testing."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="admin_access"
+        )
+        
+        decision = ReflexiveDecision(
+            decision_type=DecisionType.HALT,
+            risk_level=RiskLevel.HIGH,
+            action_context=action_context,
+            reason="Security violation"
+        )
+        
+        return HaltAction(
+            decision=decision,
+            halt_reason="Unauthorized access",
+            halt_level="immediate",
+            affected_operations=[action_context.action_id]
+        )
+    
+    async def test_execute_action_success(self, action_executor, halt_action):
+        """Test successful action execution."""
+        result = await action_executor.execute_action(halt_action)
+        
+        assert result["halted_operations"] == [halt_action.decision.action_context.action_id]
+        assert result["halt_reason"] == "Unauthorized access"
+        assert result["halt_level"] == "immediate"
+        
+        # Check execution history
+        assert len(action_executor.execution_history) == 1
+        history_record = action_executor.execution_history[0]
+        assert history_record["action_id"] == str(halt_action.action_id)
+        assert history_record["action_type"] == "halt"
+        assert history_record["status"] == "completed"
+        assert "end_time" in history_record
+        assert "result" in history_record
+    
+    async def test_execute_action_failure(self, action_executor):
+        """Test action execution failure."""
+        # Create a mock action that raises an exception
+        mock_action = Mock()
+        mock_action.action_id = "test_action_id"
+        mock_action.decision = Mock()
+        mock_action.decision.decision_id = "test_decision_id"
+        mock_action.execute.side_effect = Exception("Execution failed")
+        mock_action.get_action_type.return_value = "test_type"
+        mock_action.timestamp = datetime.utcnow()
+        mock_action.status = "pending"
+        
+        with pytest.raises(Exception, match="Execution failed"):
+            await action_executor.execute_action(mock_action)
+        
+        # Check execution history
+        assert len(action_executor.execution_history) == 1
+        history_record = action_executor.execution_history[0]
+        assert history_record["action_id"] == "test_action_id"
+        assert history_record["status"] == "failed"
+        assert "error" in history_record
+        assert history_record["error"] == "Execution failed"
+    
+    def test_get_execution_stats(self, action_executor):
+        """Test getting execution statistics."""
+        # Add some execution history
+        action_executor.execution_history = [
+            {"action_id": "1", "status": "completed"},
+            {"action_id": "2", "status": "completed"},
+            {"action_id": "3", "status": "failed"},
+        ]
+        
+        stats = action_executor.get_execution_stats()
+        
+        assert stats["total_actions"] == 3
+        assert stats["completed_actions"] == 2
+        assert stats["failed_actions"] == 1
+        assert stats["active_actions"] == 0
+        assert stats["success_rate"] == 2/3
+    
+    def test_active_actions_tracking(self, action_executor, halt_action):
+        """Test active actions tracking."""
+        # Start execution (this would normally be async)
+        action_executor.active_actions[str(halt_action.action_id)] = halt_action
+        
+        assert len(action_executor.active_actions) == 1
+        assert str(halt_action.action_id) in action_executor.active_actions
+        
+        # Simulate completion
+        del action_executor.active_actions[str(halt_action.action_id)]
+        
+        assert len(action_executor.active_actions) == 0

tests/reflexive/test_monitors.py

@@ -0,0 +1,428 @@
+"""Tests for the reflexive monitors."""
+
+import pytest
+from datetime import datetime, timedelta
+from unittest.mock import Mock
+
+from fastmcp.reflexive.monitor import PolicyMonitor, LedgerMonitor, AnomalyDetector
+from fastmcp.reflexive.engine import ActionContext
+
+
+class TestPolicyMonitor:
+    """Test the PolicyMonitor class."""
+    
+    @pytest.fixture
+    def policy_monitor(self):
+        """Create a policy monitor for testing."""
+        return PolicyMonitor()
+    
+    @pytest.fixture
+    def action_context(self):
+        """Create an action context for testing."""
+        return ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="tool_call",
+            resource_id="test_resource"
+        )
+    
+    async def test_monitor_no_violations(self, policy_monitor, action_context):
+        """Test monitoring with no violations."""
+        result = await policy_monitor(action_context)
+        assert result is None
+    
+    async def test_monitor_admin_access_violation(self, policy_monitor, action_context):
+        """Test monitoring admin access violation."""
+        # Create action context with admin access by guest user
+        admin_context = ActionContext(
+            action_id="admin_action",
+            actor_id="guest_user",
+            action_type="admin_access",
+            resource_id="admin_panel"
+        )
+        
+        result = await policy_monitor(admin_context)
+        
+        assert result is not None
+        assert result["type"] == "violation"
+        assert result["severity"] == "high"
+        assert len(result["violations"]) == 1
+        assert result["violations"][0]["rule"] == "admin_access_restriction"
+    
+    async def test_monitor_rate_limit_violation(self, policy_monitor, action_context):
+        """Test monitoring rate limit violation."""
+        # Add multiple violations for the same actor
+        for i in range(5):
+            violation_context = ActionContext(
+                action_id=f"action_{i}",
+                actor_id="rate_limit_user",
+                action_type="api_call"
+            )
+            
+            # Manually add violations to history
+            policy_monitor.violation_history.append({
+                "type": "violation",
+                "severity": "low",
+                "actor_id": "rate_limit_user",
+                "action_id": f"action_{i}",
+                "timestamp": datetime.utcnow().isoformat()
+            })
+        
+        # Test rate limit violation
+        result = await policy_monitor(action_context)
+        
+        # Should not trigger rate limit for different actor
+        assert result is None
+        
+        # Test with same actor
+        rate_limit_context = ActionContext(
+            action_id="rate_limit_action",
+            actor_id="rate_limit_user",
+            action_type="api_call"
+        )
+        
+        result = await policy_monitor(rate_limit_context)
+        
+        assert result is not None
+        assert result["type"] == "violation"
+        assert result["severity"] == "medium"
+        assert len(result["violations"]) == 1
+        assert result["violations"][0]["rule"] == "rate_limit_exceeded"
+    
+    async def test_monitor_sensitive_resource_violation(self, policy_monitor, action_context):
+        """Test monitoring sensitive resource access violation."""
+        sensitive_context = ActionContext(
+            action_id="sensitive_action",
+            actor_id="unauthorized_user",
+            action_type="data_access",
+            resource_id="sensitive_data",
+            metadata={"authorized": False}
+        )
+        
+        result = await policy_monitor(sensitive_context)
+        
+        assert result is not None
+        assert result["type"] == "violation"
+        assert result["severity"] == "critical"
+        assert len(result["violations"]) == 1
+        assert result["violations"][0]["rule"] == "unauthorized_sensitive_access"
+    
+    def test_assess_violation_severity(self, policy_monitor):
+        """Test violation severity assessment."""
+        # Test critical severity
+        critical_violations = [{"severity": "critical"}]
+        assert policy_monitor._assess_violation_severity(critical_violations) == "critical"
+        
+        # Test high severity
+        high_violations = [{"severity": "high"}]
+        assert policy_monitor._assess_violation_severity(high_violations) == "high"
+        
+        # Test medium severity
+        medium_violations = [{"severity": "medium"}]
+        assert policy_monitor._assess_violation_severity(medium_violations) == "medium"
+        
+        # Test low severity
+        low_violations = [{"severity": "low"}]
+        assert policy_monitor._assess_violation_severity(low_violations) == "low"
+        
+        # Test mixed severities
+        mixed_violations = [{"severity": "low"}, {"severity": "high"}]
+        assert policy_monitor._assess_violation_severity(mixed_violations) == "high"
+    
+    def test_get_violation_stats(self, policy_monitor):
+        """Test getting violation statistics."""
+        # Add some violations
+        policy_monitor.violation_history.append({
+            "type": "violation",
+            "severity": "high",
+            "actor_id": "user1",
+            "action_id": "action1",
+            "timestamp": datetime.utcnow().isoformat()
+        })
+        
+        policy_monitor.actor_violations["user1"] = 1
+        policy_monitor.actor_violations["user2"] = 2
+        
+        stats = policy_monitor.get_violation_stats()
+        
+        assert stats["total_violations"] == 1
+        assert stats["actor_violations"]["user1"] == 1
+        assert stats["actor_violations"]["user2"] == 2
+        assert "recent_violations" in stats
+
+
+class TestLedgerMonitor:
+    """Test the LedgerMonitor class."""
+    
+    @pytest.fixture
+    def ledger_monitor(self):
+        """Create a ledger monitor for testing."""
+        return LedgerMonitor()
+    
+    @pytest.fixture
+    def mock_ledger(self):
+        """Create a mock ledger for testing."""
+        mock_ledger = Mock()
+        mock_ledger.verify_chain_integrity.return_value = True
+        mock_ledger.get_ledger_statistics.return_value = {
+            "total_entries": 10,
+            "total_blocks": 2
+        }
+        return mock_ledger
+    
+    @pytest.fixture
+    def action_context(self):
+        """Create an action context for testing."""
+        return ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="tool_call"
+        )
+    
+    async def test_monitor_no_issues(self, ledger_monitor, mock_ledger, action_context):
+        """Test monitoring with no ledger issues."""
+        ledger_monitor.ledger = mock_ledger
+        
+        result = await ledger_monitor(action_context)
+        assert result is None
+    
+    async def test_monitor_chain_integrity_issue(self, ledger_monitor, mock_ledger, action_context):
+        """Test monitoring chain integrity issue."""
+        # Mock chain integrity failure
+        mock_ledger.verify_chain_integrity.return_value = False
+        ledger_monitor.ledger = mock_ledger
+        
+        result = await ledger_monitor(action_context)
+        
+        assert result is not None
+        assert result["type"] == "anomaly"
+        assert result["severity"] == "critical"
+        assert len(result["issues"]) == 1
+        assert result["issues"][0]["type"] == "chain_integrity"
+    
+    async def test_monitor_missing_blocks_issue(self, ledger_monitor, mock_ledger, action_context):
+        """Test monitoring missing blocks issue."""
+        # Mock missing blocks scenario
+        mock_ledger.get_ledger_statistics.return_value = {
+            "total_entries": 10,
+            "total_blocks": 0
+        }
+        ledger_monitor.ledger = mock_ledger
+        
+        result = await ledger_monitor(action_context)
+        
+        assert result is not None
+        assert result["type"] == "anomaly"
+        assert result["severity"] == "high"
+        assert len(result["issues"]) == 1
+        assert result["issues"][0]["type"] == "missing_blocks"
+    
+    async def test_monitor_integrity_check_error(self, ledger_monitor, action_context):
+        """Test monitoring with integrity check error."""
+        # Mock ledger that raises exception
+        mock_ledger = Mock()
+        mock_ledger.verify_chain_integrity.side_effect = Exception("Database error")
+        ledger_monitor.ledger = mock_ledger
+        
+        result = await ledger_monitor(action_context)
+        
+        assert result is not None
+        assert result["type"] == "anomaly"
+        assert result["severity"] == "medium"
+        assert len(result["issues"]) == 1
+        assert result["issues"][0]["type"] == "integrity_check_error"
+    
+    def test_assess_integrity_severity(self, ledger_monitor):
+        """Test integrity severity assessment."""
+        # Test critical severity
+        critical_issues = [{"severity": "critical"}]
+        assert ledger_monitor._assess_integrity_severity(critical_issues) == "critical"
+        
+        # Test high severity
+        high_issues = [{"severity": "high"}]
+        assert ledger_monitor._assess_integrity_severity(high_issues) == "high"
+        
+        # Test medium severity
+        medium_issues = [{"severity": "medium"}]
+        assert ledger_monitor._assess_integrity_severity(medium_issues) == "medium"
+        
+        # Test low severity
+        low_issues = [{"severity": "low"}]
+        assert ledger_monitor._assess_integrity_severity(low_issues) == "low"
+    
+    def test_get_integrity_stats(self, ledger_monitor):
+        """Test getting integrity statistics."""
+        # Add some integrity checks
+        ledger_monitor.integrity_checks.append({
+            "type": "anomaly",
+            "severity": "high",
+            "timestamp": datetime.utcnow().isoformat()
+        })
+        
+        stats = ledger_monitor.get_integrity_stats()
+        
+        assert stats["total_checks"] == 1
+        assert "recent_issues" in stats
+
+
+class TestAnomalyDetector:
+    """Test the AnomalyDetector class."""
+    
+    @pytest.fixture
+    def anomaly_detector(self):
+        """Create an anomaly detector for testing."""
+        return AnomalyDetector()
+    
+    @pytest.fixture
+    def action_context(self):
+        """Create an action context for testing."""
+        return ActionContext(
+            action_id="test_action",
+            actor_id="test_user",
+            action_type="tool_call",
+            resource_id="test_resource"
+        )
+    
+    async def test_detector_no_anomalies(self, anomaly_detector, action_context):
+        """Test detection with no anomalies."""
+        # First access to a resource will be flagged as new resource access
+        # So we need to access the resource twice to avoid the "new resource" anomaly
+        await anomaly_detector(action_context)
+        result = await anomaly_detector(action_context)
+        assert result is None
+    
+    async def test_detector_high_frequency_anomaly(self, anomaly_detector, action_context):
+        """Test detection of high frequency anomaly."""
+        # Add many recent actions for the same actor
+        for i in range(25):
+            recent_context = ActionContext(
+                action_id=f"action_{i}",
+                actor_id="high_frequency_user",
+                action_type="api_call"
+            )
+            anomaly_detector._update_patterns(recent_context)
+        
+        # Test high frequency detection
+        high_freq_context = ActionContext(
+            action_id="high_freq_action",
+            actor_id="high_frequency_user",
+            action_type="api_call"
+        )
+        
+        result = await anomaly_detector(high_freq_context)
+        
+        assert result is not None
+        assert result["type"] == "anomaly"
+        assert result["severity"] == "medium"
+        assert len(result["anomalies"]) == 1
+        assert result["anomalies"][0]["type"] == "high_frequency"
+    
+    async def test_detector_unusual_timing_anomaly(self, anomaly_detector, action_context):
+        """Test detection of unusual timing anomaly."""
+        # Create action at unusual hour with new action type
+        unusual_context = ActionContext(
+            action_id="unusual_action",
+            actor_id="test_user",
+            action_type="new_action_type"  # New action type
+        )
+        unusual_context.timestamp = datetime.utcnow().replace(hour=3)  # 3 AM
+        
+        result = await anomaly_detector(unusual_context)
+        
+        assert result is not None
+        assert result["type"] == "anomaly"
+        assert result["severity"] == "low"
+        assert len(result["anomalies"]) == 1
+        assert result["anomalies"][0]["type"] == "unusual_timing"
+    
+    async def test_detector_new_resource_access_anomaly(self, anomaly_detector, action_context):
+        """Test detection of new resource access anomaly."""
+        # First access to a resource
+        new_resource_context = ActionContext(
+            action_id="new_resource_action",
+            actor_id="test_user",
+            action_type="data_access",
+            resource_id="new_resource"
+        )
+        
+        result = await anomaly_detector(new_resource_context)
+        
+        assert result is not None
+        assert result["type"] == "anomaly"
+        assert result["severity"] == "low"
+        assert len(result["anomalies"]) == 1
+        assert result["anomalies"][0]["type"] == "new_resource_access"
+    
+    async def test_detector_privilege_escalation_anomaly(self, anomaly_detector, action_context):
+        """Test detection of privilege escalation anomaly."""
+        # First time performing privileged action
+        privilege_context = ActionContext(
+            action_id="privilege_action",
+            actor_id="test_user",
+            action_type="admin_access",
+            resource_id="admin_panel"
+        )
+        
+        result = await anomaly_detector(privilege_context)
+        
+        assert result is not None
+        assert result["type"] == "anomaly"
+        assert result["severity"] == "high"
+        # Should detect both new resource access and privilege escalation
+        assert len(result["anomalies"]) >= 1
+        # Check that privilege escalation is detected
+        privilege_anomalies = [a for a in result["anomalies"] if a["type"] == "privilege_escalation"]
+        assert len(privilege_anomalies) == 1
+        assert privilege_anomalies[0]["type"] == "privilege_escalation"
+    
+    def test_update_patterns(self, anomaly_detector, action_context):
+        """Test pattern updating."""
+        # Update patterns
+        anomaly_detector._update_patterns(action_context)
+        
+        # Check that patterns were updated
+        actor_data = anomaly_detector.actor_patterns[action_context.actor_id]
+        assert actor_data["action_counts"][action_context.action_type] == 1
+        assert actor_data["resource_access"][action_context.resource_id] == 1
+        assert len(actor_data["session_times"]) == 1
+        assert actor_data["last_seen"] == action_context.timestamp
+        
+        # Check global patterns
+        assert anomaly_detector.global_patterns["action_frequency"][action_context.action_type] == 1
+        assert anomaly_detector.global_patterns["resource_access"][action_context.resource_id] == 1
+    
+    def test_assess_anomaly_severity(self, anomaly_detector):
+        """Test anomaly severity assessment."""
+        # Test high severity
+        high_anomalies = [{"severity": "high"}]
+        assert anomaly_detector._assess_anomaly_severity(high_anomalies) == "high"
+        
+        # Test medium severity
+        medium_anomalies = [{"severity": "medium"}]
+        assert anomaly_detector._assess_anomaly_severity(medium_anomalies) == "medium"
+        
+        # Test low severity
+        low_anomalies = [{"severity": "low"}]
+        assert anomaly_detector._assess_anomaly_severity(low_anomalies) == "low"
+        
+        # Test mixed severities
+        mixed_anomalies = [{"severity": "low"}, {"severity": "high"}]
+        assert anomaly_detector._assess_anomaly_severity(mixed_anomalies) == "high"
+    
+    def test_get_anomaly_stats(self, anomaly_detector, action_context):
+        """Test getting anomaly statistics."""
+        # Update patterns for some actors
+        anomaly_detector._update_patterns(action_context)
+        
+        another_context = ActionContext(
+            action_id="another_action",
+            actor_id="another_user",
+            action_type="another_type"
+        )
+        anomaly_detector._update_patterns(another_context)
+        
+        stats = anomaly_detector.get_anomaly_stats()
+        
+        assert stats["tracked_actors"] == 2
+        assert stats["global_action_types"] == 2
+        assert stats["global_resources"] == 1

tests/reflexive/test_reflexive_engine.py

@@ -0,0 +1,320 @@
+"""Tests for the reflexive engine."""
+
+import pytest
+from datetime import datetime
+from unittest.mock import Mock, AsyncMock
+
+from fastmcp.reflexive import ReflexiveEngine, ActionContext, DecisionType, RiskLevel
+from fastmcp.reflexive.engine import ReflexiveDecision
+
+
+class TestReflexiveEngine:
+    """Test the reflexive engine functionality."""
+    
+    @pytest.fixture
+    def reflexive_engine(self):
+        """Create a reflexive engine for testing."""
+        return ReflexiveEngine()
+    
+    @pytest.fixture
+    def action_context(self):
+        """Create an action context for testing."""
+        return ActionContext(
+            action_id="test_action_123",
+            actor_id="test_user",
+            action_type="tool_call",
+            resource_id="test_resource",
+            metadata={"test": "data"}
+        )
+    
+    def test_engine_initialization(self, reflexive_engine):
+        """Test reflexive engine initialization."""
+        assert reflexive_engine.policy_engine is None
+        assert reflexive_engine.ledger is None
+        assert len(reflexive_engine.monitors) == 0
+        assert len(reflexive_engine.decision_handlers) == 4
+        assert not reflexive_engine.is_running
+    
+    def test_engine_status(self, reflexive_engine):
+        """Test getting engine status."""
+        status = reflexive_engine.get_engine_status()
+        
+        assert "is_running" in status
+        assert "monitor_count" in status
+        assert "queue_size" in status
+        assert "decision_handlers" in status
+        
+        assert status["is_running"] is False
+        assert status["monitor_count"] == 0
+        assert len(status["decision_handlers"]) == 4
+    
+    def test_add_remove_monitor(self, reflexive_engine):
+        """Test adding and removing monitors."""
+        def test_monitor(context):
+            return None
+        
+        # Add monitor
+        reflexive_engine.add_monitor(test_monitor)
+        assert len(reflexive_engine.monitors) == 1
+        assert test_monitor in reflexive_engine.monitors
+        
+        # Remove monitor
+        reflexive_engine.remove_monitor(test_monitor)
+        assert len(reflexive_engine.monitors) == 0
+        assert test_monitor not in reflexive_engine.monitors
+    
+    async def test_submit_action(self, reflexive_engine, action_context):
+        """Test submitting an action for evaluation."""
+        await reflexive_engine.submit_action(action_context)
+        
+        # Check that action was added to queue
+        assert reflexive_engine.event_queue.qsize() == 1
+    
+    async def test_evaluate_action_no_violations(self, reflexive_engine, action_context):
+        """Test evaluating an action with no violations."""
+        decision = await reflexive_engine._evaluate_action(action_context)
+        
+        assert decision.decision_type == DecisionType.ALLOW
+        assert decision.risk_level == RiskLevel.LOW
+        assert decision.reason == "No violations or anomalies detected"
+        assert decision.action_context == action_context
+        assert decision.proof_hash is not None
+    
+    async def test_evaluate_action_with_violations(self, reflexive_engine, action_context):
+        """Test evaluating an action with violations."""
+        # Add a monitor that returns violations
+        def violation_monitor(context):
+            return {
+                "type": "violation",
+                "severity": "high",
+                "violations": [{"rule": "test_rule", "message": "Test violation", "severity": "high"}]
+            }
+        
+        reflexive_engine.add_monitor(violation_monitor)
+        
+        decision = await reflexive_engine._evaluate_action(action_context)
+        
+        assert decision.decision_type == DecisionType.HALT
+        assert decision.risk_level == RiskLevel.HIGH
+        assert "violations" in decision.evidence
+        assert len(decision.evidence["violations"]) == 1
+    
+    async def test_evaluate_action_with_anomalies(self, reflexive_engine, action_context):
+        """Test evaluating an action with anomalies."""
+        # Add a monitor that returns anomalies
+        def anomaly_monitor(context):
+            return {
+                "type": "anomaly",
+                "severity": "medium",
+                "anomalies": [{"type": "test_anomaly", "message": "Test anomaly", "severity": "medium"}]
+            }
+        
+        reflexive_engine.add_monitor(anomaly_monitor)
+        
+        decision = await reflexive_engine._evaluate_action(action_context)
+        
+        assert decision.decision_type == DecisionType.ESCALATE
+        assert decision.risk_level == RiskLevel.MEDIUM
+        assert "anomalies" in decision.evidence
+        assert len(decision.evidence["anomalies"]) == 1
+    
+    async def test_evaluate_action_critical_violation(self, reflexive_engine, action_context):
+        """Test evaluating an action with critical violations."""
+        # Add a monitor that returns critical violations
+        def critical_monitor(context):
+            return {
+                "type": "violation",
+                "severity": "critical",
+                "violations": [{"rule": "critical_rule", "message": "Critical violation", "severity": "critical"}]
+            }
+        
+        reflexive_engine.add_monitor(critical_monitor)
+        
+        decision = await reflexive_engine._evaluate_action(action_context)
+        
+        assert decision.decision_type == DecisionType.HALT
+        assert decision.risk_level == RiskLevel.CRITICAL
+    
+    async def test_simulate_risk(self, reflexive_engine):
+        """Test risk simulation."""
+        risk_scenario = {
+            "action_context": {
+                "action_id": "simulation_action",
+                "actor_id": "test_actor",
+                "action_type": "admin_access",
+                "resource_id": "sensitive_data"
+            },
+            "monitors": [
+                lambda ctx: {
+                    "type": "violation",
+                    "severity": "high",
+                    "violations": [{"rule": "admin_restriction", "message": "Unauthorized admin access", "severity": "high"}]
+                }
+            ]
+        }
+        
+        decision = await reflexive_engine.simulate_risk(risk_scenario)
+        
+        assert decision.decision_type == DecisionType.HALT
+        assert decision.risk_level == RiskLevel.HIGH
+        assert decision.action_context.action_id == "simulation_action"
+    
+    def test_assess_risk_level(self, reflexive_engine):
+        """Test risk level assessment."""
+        # Test critical risk
+        critical_violations = [{"severity": "critical"}]
+        assert reflexive_engine._assess_risk_level(critical_violations, []) == RiskLevel.CRITICAL
+        
+        # Test high risk
+        high_violations = [{"severity": "high"}]
+        assert reflexive_engine._assess_risk_level(high_violations, []) == RiskLevel.HIGH
+        
+        # Test medium risk
+        medium_violations = [{"severity": "medium"}]
+        assert reflexive_engine._assess_risk_level(medium_violations, []) == RiskLevel.MEDIUM
+        
+        # Test low risk
+        low_violations = [{"severity": "low"}]
+        assert reflexive_engine._assess_risk_level(low_violations, []) == RiskLevel.LOW
+        
+        # Test multiple issues
+        multiple_issues = [{"severity": "low"}, {"severity": "low"}, {"severity": "low"}]
+        assert reflexive_engine._assess_risk_level(multiple_issues, []) == RiskLevel.MEDIUM
+    
+    def test_determine_escalation_target(self, reflexive_engine):
+        """Test escalation target determination."""
+        decision = ReflexiveDecision(
+            decision_type=DecisionType.ESCALATE,
+            risk_level=RiskLevel.CRITICAL,
+            action_context=ActionContext(action_id="test", actor_id="test", action_type="test"),
+            reason="Test"
+        )
+        
+        target = reflexive_engine._determine_escalation_target(decision)
+        assert target == "security_admin"
+        
+        decision.risk_level = RiskLevel.HIGH
+        target = reflexive_engine._determine_escalation_target(decision)
+        assert target == "system_admin"
+        
+        decision.risk_level = RiskLevel.MEDIUM
+        target = reflexive_engine._determine_escalation_target(decision)
+        assert target == "monitoring_team"
+
+
+class TestActionContext:
+    """Test the ActionContext class."""
+    
+    def test_action_context_creation(self):
+        """Test creating an action context."""
+        context = ActionContext(
+            action_id="test_action",
+            actor_id="test_actor",
+            action_type="test_type"
+        )
+        
+        assert context.action_id == "test_action"
+        assert context.actor_id == "test_actor"
+        assert context.action_type == "test_type"
+        assert context.resource_id is None
+        assert context.metadata == {}
+        assert isinstance(context.timestamp, datetime)
+    
+    def test_action_context_hash(self):
+        """Test action context hash generation."""
+        context1 = ActionContext(
+            action_id="test_action",
+            actor_id="test_actor",
+            action_type="test_type",
+            metadata={"key": "value"}
+        )
+        
+        context2 = ActionContext(
+            action_id="test_action",
+            actor_id="test_actor",
+            action_type="test_type",
+            metadata={"key": "value"}
+        )
+        
+        # Same content should produce same hash
+        assert context1.get_context_hash() == context2.get_context_hash()
+        
+        # Different content should produce different hash
+        context3 = ActionContext(
+            action_id="different_action",
+            actor_id="test_actor",
+            action_type="test_type",
+            metadata={"key": "value"}
+        )
+        
+        assert context1.get_context_hash() != context3.get_context_hash()
+
+
+class TestReflexiveDecision:
+    """Test the ReflexiveDecision class."""
+    
+    def test_decision_creation(self):
+        """Test creating a reflexive decision."""
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_actor",
+            action_type="test_type"
+        )
+        
+        decision = ReflexiveDecision(
+            decision_type=DecisionType.HALT,
+            risk_level=RiskLevel.HIGH,
+            action_context=action_context,
+            reason="Test reason"
+        )
+        
+        assert decision.decision_type == DecisionType.HALT
+        assert decision.risk_level == RiskLevel.HIGH
+        assert decision.action_context == action_context
+        assert decision.reason == "Test reason"
+        assert decision.evidence == {}
+        assert decision.escalated_to is None
+        assert decision.proof_hash is None
+        assert isinstance(decision.timestamp, datetime)
+    
+    def test_decision_hash(self):
+        """Test decision hash generation."""
+        from uuid import uuid4
+        
+        action_context = ActionContext(
+            action_id="test_action",
+            actor_id="test_actor",
+            action_type="test_type"
+        )
+        
+        # Use the same decision_id for both decisions
+        decision_id = uuid4()
+        
+        decision1 = ReflexiveDecision(
+            decision_id=decision_id,
+            decision_type=DecisionType.HALT,
+            risk_level=RiskLevel.HIGH,
+            action_context=action_context,
+            reason="Test reason"
+        )
+        
+        decision2 = ReflexiveDecision(
+            decision_id=decision_id,
+            decision_type=DecisionType.HALT,
+            risk_level=RiskLevel.HIGH,
+            action_context=action_context,
+            reason="Test reason"
+        )
+        
+        # Same content should produce same hash
+        assert decision1.get_decision_hash() == decision2.get_decision_hash()
+        
+        # Different content should produce different hash
+        decision3 = ReflexiveDecision(
+            decision_type=DecisionType.ESCALATE,
+            risk_level=RiskLevel.HIGH,
+            action_context=action_context,
+            reason="Test reason"
+        )
+        
+        assert decision1.get_decision_hash() != decision3.get_decision_hash()

tests/reflexive/test_reflexive_http.py

@@ -0,0 +1,325 @@
+"""Tests for the reflexive HTTP endpoints."""
+
+import pytest
+import httpx
+from unittest.mock import Mock, AsyncMock
+
+from fastmcp.server.server import FastMCP
+from fastmcp.reflexive import ReflexiveEngine, ActionContext, DecisionType, RiskLevel
+from fastmcp.server.http import create_streamable_http_app
+
+
+@pytest.fixture(name="server_with_reflexive")
+async def server_with_reflexive_fixture():
+    """Fixture for a FastMCP server with an enabled reflexive core."""
+    server = FastMCP("TestReflexiveServer")
+    reflexive_engine = server.enable_reflexive_core()
+    return server, reflexive_engine
+
+
+@pytest.fixture
+async def client(server_with_reflexive):
+    """Create an HTTP client for testing."""
+    server, reflexive_engine = server_with_reflexive
+    app = create_streamable_http_app(server, streamable_http_path="/")
+    async with httpx.AsyncClient(transport=httpx.ASGITransport(app=app), base_url="http://test") as client:
+        # Store server and reflexive engine in client for test access
+        client.server = server
+        client.reflexive_engine = reflexive_engine
+        yield client
+
+
+class TestReflexiveHTTPEndpoints:
+    """Test the reflexive HTTP endpoints."""
+    
+    async def test_simulate_risk_endpoint(self, client):
+        """Test the simulate risk endpoint."""
+        risk_scenario = {
+            "action_context": {
+                "action_id": "test_action",
+                "actor_id": "test_user",
+                "action_type": "admin_access",
+                "resource_id": "admin_panel"
+            },
+            "scenario_type": "policy_violation"
+        }
+        
+        response = await client.post("/core/simulate-risk", json=risk_scenario)
+        
+        assert response.status_code == 200
+        data = response.json()
+        assert "simulation_id" in data
+        assert "decision" in data
+        assert "action" in data
+        assert "action_context" in data
+        
+        # Check decision structure
+        decision = data["decision"]
+        assert "decision_id" in decision
+        assert "decision_type" in decision
+        assert "risk_level" in decision
+        assert "reason" in decision
+        assert "proof_hash" in decision
+        
+        # Check action structure
+        action = data["action"]
+        assert "action_id" in action
+        assert "action_type" in action
+        assert "status" in action
+        assert "result" in action
+    
+    async def test_simulate_risk_invalid_data(self, client):
+        """Test simulate risk endpoint with invalid data."""
+        invalid_scenario = {
+            "invalid_field": "invalid_value"
+        }
+        
+        response = await client.post("/core/simulate-risk", json=invalid_scenario)
+        
+        assert response.status_code == 400
+        data = response.json()
+        assert "error" in data
+        assert "Missing required field: action_context" in data["error"]
+    
+    async def test_get_engine_status_endpoint(self, client):
+        """Test the get engine status endpoint."""
+        response = await client.get("/core/status")
+        
+        assert response.status_code == 200
+        data = response.json()
+        assert "is_running" in data
+        assert "monitor_count" in data
+        assert "queue_size" in data
+        assert "decision_handlers" in data
+    
+    async def test_submit_action_endpoint(self, client):
+        """Test the submit action endpoint."""
+        action_data = {
+            "action_id": "test_action",
+            "actor_id": "test_user",
+            "action_type": "tool_call",
+            "resource_id": "test_resource",
+            "metadata": {"test": "data"}
+        }
+        
+        response = await client.post("/core/submit-action", json=action_data)
+        
+        assert response.status_code == 202
+        data = response.json()
+        assert "message" in data
+        assert "action_id" in data
+        assert "submitted_at" in data
+        assert data["action_id"] == "test_action"
+    
+    async def test_submit_action_invalid_data(self, client):
+        """Test submit action endpoint with invalid data."""
+        invalid_action = {
+            "invalid_field": "invalid_value"
+        }
+        
+        response = await client.post("/core/submit-action", json=invalid_action)
+        
+        assert response.status_code == 400
+        data = response.json()
+        assert "error" in data
+        assert "Invalid action data" in data["error"]
+    
+    async def test_get_monitor_stats_endpoint(self, client):
+        """Test the get monitor stats endpoint."""
+        response = await client.get("/core/monitor-stats")
+        
+        assert response.status_code == 200
+        data = response.json()
+        # Should return empty dict if no monitors are configured
+        assert isinstance(data, dict)
+    
+    async def test_create_risk_scenario_endpoint(self, client):
+        """Test the create risk scenario endpoint."""
+        scenario_data = {
+            "scenario_name": "admin_privilege_escalation",
+            "scenario_type": "policy_violation",
+            "parameters": {
+                "actor_type": "guest_user",
+                "target_resource": "admin_panel",
+                "severity": "high"
+            }
+        }
+        
+        response = await client.post("/core/risk-scenario", json=scenario_data)
+        
+        assert response.status_code == 200
+        data = response.json()
+        assert "scenario" in data
+        assert "message" in data
+        assert data["message"] == "Risk scenario 'admin_privilege_escalation' created successfully"
+        
+        # Check scenario structure
+        scenario = data["scenario"]
+        assert "action_context" in scenario
+        assert "expected_decision" in scenario
+        assert "expected_risk_level" in scenario
+        
+        # Check action context
+        action_context = scenario["action_context"]
+        assert action_context["action_type"] == "admin_access"
+        assert action_context["resource_id"] == "admin_panel"
+    
+    async def test_create_risk_scenario_missing_fields(self, client):
+        """Test create risk scenario endpoint with missing fields."""
+        invalid_scenario = {
+            "scenario_name": "test_scenario"
+            # Missing scenario_type
+        }
+        
+        response = await client.post("/core/risk-scenario", json=invalid_scenario)
+        
+        assert response.status_code == 400
+        data = response.json()
+        assert "error" in data
+        assert "Missing required fields" in data["error"]
+    
+    async def test_create_risk_scenario_unknown_type(self, client):
+        """Test create risk scenario endpoint with unknown scenario type."""
+        scenario_data = {
+            "scenario_name": "unknown_scenario",
+            "scenario_type": "unknown_type",
+            "parameters": {}
+        }
+        
+        response = await client.post("/core/risk-scenario", json=scenario_data)
+        
+        assert response.status_code == 200
+        data = response.json()
+        assert "scenario" in data
+        
+        # Should create a custom scenario
+        scenario = data["scenario"]
+        assert scenario["action_context"]["action_id"] == "custom_unknown_scenario"
+        assert scenario["expected_decision"] == "monitor"
+        assert scenario["expected_risk_level"] == "low"
+    
+    async def test_simulate_risk_with_violations(self, client):
+        """Test simulate risk with policy violations."""
+        # Add a monitor that returns violations
+        def violation_monitor(context):
+            return {
+                "type": "violation",
+                "severity": "high",
+                "violations": [{"rule": "admin_restriction", "message": "Unauthorized admin access", "severity": "high"}]
+            }
+        
+        client.reflexive_engine.add_monitor(violation_monitor)
+        
+        risk_scenario = {
+            "action_context": {
+                "action_id": "admin_action",
+                "actor_id": "guest_user",
+                "action_type": "admin_access",
+                "resource_id": "admin_panel"
+            }
+        }
+        
+        response = await client.post("/core/simulate-risk", json=risk_scenario)
+        
+        assert response.status_code == 200
+        data = response.json()
+        
+        # Should result in a halt decision
+        decision = data["decision"]
+        assert decision["decision_type"] == "halt"
+        assert decision["risk_level"] == "high"
+        assert "violations" in decision["evidence"]
+    
+    async def test_simulate_risk_with_anomalies(self, client):
+        """Test simulate risk with anomalies."""
+        # Add a monitor that returns anomalies
+        def anomaly_monitor(context):
+            return {
+                "type": "anomaly",
+                "severity": "medium",
+                "anomalies": [{"type": "unusual_timing", "message": "Action at unusual hour", "severity": "medium"}]
+            }
+        
+        client.reflexive_engine.add_monitor(anomaly_monitor)
+        
+        risk_scenario = {
+            "action_context": {
+                "action_id": "unusual_action",
+                "actor_id": "test_user",
+                "action_type": "data_access",
+                "resource_id": "sensitive_data"
+            }
+        }
+        
+        response = await client.post("/core/simulate-risk", json=risk_scenario)
+        
+        assert response.status_code == 200
+        data = response.json()
+        
+        # Should result in an escalate decision
+        decision = data["decision"]
+        assert decision["decision_type"] == "escalate"
+        assert decision["risk_level"] == "medium"
+        assert "anomalies" in decision["evidence"]
+    
+    async def test_simulate_risk_no_issues(self, client):
+        """Test simulate risk with no violations or anomalies."""
+        risk_scenario = {
+            "action_context": {
+                "action_id": "normal_action",
+                "actor_id": "authorized_user",
+                "action_type": "normal_operation",
+                "resource_id": "public_resource"
+            }
+        }
+        
+        response = await client.post("/core/simulate-risk", json=risk_scenario)
+        
+        assert response.status_code == 200
+        data = response.json()
+        
+        # Should result in an allow decision
+        decision = data["decision"]
+        assert decision["decision_type"] == "allow"
+        assert decision["risk_level"] == "low"
+        assert decision["reason"] == "No violations or anomalies detected"
+    
+    async def test_multiple_risk_scenarios(self, client):
+        """Test multiple predefined risk scenarios."""
+        scenarios = [
+            {
+                "name": "admin_privilege_escalation",
+                "expected_decision": "halt",
+                "expected_risk": "high"
+            },
+            {
+                "name": "suspicious_activity",
+                "expected_decision": "escalate",
+                "expected_risk": "medium"
+            },
+            {
+                "name": "integrity_violation",
+                "expected_decision": "halt",
+                "expected_risk": "critical"
+            },
+            {
+                "name": "rate_limit_exceeded",
+                "expected_decision": "escalate",
+                "expected_risk": "medium"
+            }
+        ]
+        
+        for scenario in scenarios:
+            scenario_data = {
+                "scenario_name": scenario["name"],
+                "scenario_type": "test",
+                "parameters": {}
+            }
+            
+            response = await client.post("/core/risk-scenario", json=scenario_data)
+            assert response.status_code == 200
+            
+            data = response.json()
+            created_scenario = data["scenario"]
+            assert created_scenario["expected_decision"] == scenario["expected_decision"]
+            assert created_scenario["expected_risk_level"] == scenario["expected_risk"]