""" True ARF OSS v3.3.7 - Real Implementation Production-grade multi-agent AI for reliability monitoring (Advisory only) Core Agents: 1. Detection Agent: Anomaly detection and incident identification 2. Recall Agent: RAG-based memory for similar incidents 3. Decision Agent: Healing intent generation with confidence scoring OSS Edition: Apache 2.0 Licensed, Advisory mode only """ import asyncio import logging import time import uuid from typing import Dict, Any, List, Optional, Tuple from dataclasses import dataclass, field from datetime import datetime import numpy as np logger = logging.getLogger(__name__) # ============================================================================ # DATA MODELS # ============================================================================ @dataclass class TelemetryPoint: """Telemetry data point""" timestamp: float metric: str value: float component: str @dataclass class Anomaly: """Detected anomaly""" id: str component: str metric: str value: float expected_range: Tuple[float, float] confidence: float severity: str # "low", "medium", "high", "critical" timestamp: float = field(default_factory=time.time) @dataclass class Incident: """Incident representation for RAG memory""" id: str component: str anomaly: Anomaly telemetry: List[TelemetryPoint] context: Dict[str, Any] timestamp: float = field(default_factory=time.time) resolved: bool = False resolution: Optional[str] = None def to_vector(self) -> List[float]: """Convert incident to vector for similarity search""" # Create a feature vector based on incident characteristics features = [] # Component encoding (simple hash) features.append(hash(self.component) % 1000 / 1000.0) # Metric severity encoding severity_map = {"low": 0.1, "medium": 0.3, "high": 0.7, "critical": 1.0} features.append(severity_map.get(self.anomaly.severity, 0.5)) # Anomaly confidence features.append(self.anomaly.confidence) # Telemetry features (averages) if self.telemetry: values = [p.value for p in self.telemetry] features.append(np.mean(values)) features.append(np.std(values) if len(values) > 1 else 0.0) else: features.extend([0.0, 0.0]) # Context features if "error_rate" in self.context: features.append(self.context["error_rate"]) else: features.append(0.0) if "latency_p99" in self.context: features.append(min(self.context["latency_p99"] / 1000.0, 1.0)) # Normalize else: features.append(0.0) return features # ============================================================================ # DETECTION AGENT # ============================================================================ class DetectionAgent: """ Detection Agent - Identifies anomalies in telemetry data Features: - Statistical anomaly detection - Multi-metric correlation analysis - Confidence scoring - Severity classification """ def __init__(self, config: Optional[Dict[str, Any]] = None): self.config = config or {} self.detection_history: List[Anomaly] = [] self.telemetry_buffer: Dict[str, List[TelemetryPoint]] = {} # Detection thresholds self.thresholds = { "error_rate": {"warning": 0.01, "critical": 0.05}, "latency_p99": {"warning": 200, "critical": 500}, # ms "cpu_util": {"warning": 0.8, "critical": 0.95}, "memory_util": {"warning": 0.85, "critical": 0.95}, "throughput": {"warning": 0.7, "critical": 0.3}, # relative to baseline } logger.info("Detection Agent initialized") async def analyze_telemetry(self, component: str, telemetry: List[TelemetryPoint]) -> List[Anomaly]: """ Analyze telemetry data for anomalies Args: component: Target component name telemetry: List of telemetry data points Returns: List of detected anomalies """ anomalies = [] # Group telemetry by metric metrics = {} for point in telemetry: if point.metric not in metrics: metrics[point.metric] = [] metrics[point.metric].append(point) # Analyze each metric for metric, points in metrics.items(): if len(points) < 3: # Need at least 3 points for meaningful analysis continue values = [p.value for p in points] recent_value = values[-1] # Check against thresholds if metric in self.thresholds: threshold = self.thresholds[metric] # Determine severity and confidence if recent_value >= threshold["critical"]: severity = "critical" confidence = min(0.95 + (recent_value - threshold["critical"]) * 2, 0.99) elif recent_value >= threshold["warning"]: severity = "high" confidence = 0.85 + (recent_value - threshold["warning"]) * 0.5 else: # No anomaly continue # Create anomaly anomaly = Anomaly( id=str(uuid.uuid4()), component=component, metric=metric, value=recent_value, expected_range=(0, threshold["warning"]), confidence=min(confidence, 0.99), severity=severity ) anomalies.append(anomaly) # Store in buffer for correlation analysis self._store_in_buffer(component, metric, points[-5:]) # Last 5 points logger.info(f"Detection Agent: Found {severity} anomaly in {component}.{metric}: {recent_value}") # Correlated anomaly detection (cross-metric analysis) correlated = await self._detect_correlated_anomalies(component, metrics) anomalies.extend(correlated) # Update history self.detection_history.extend(anomalies) return anomalies async def _detect_correlated_anomalies(self, component: str, metrics: Dict[str, List[TelemetryPoint]]) -> List[Anomaly]: """Detect anomalies that correlate across multiple metrics""" anomalies = [] # Simple correlation: if multiple metrics are anomalous, confidence increases anomalous_metrics = [] for metric, points in metrics.items(): if metric in self.thresholds and len(points) >= 3: recent_value = points[-1].value threshold = self.thresholds[metric] if recent_value >= threshold["warning"]: anomalous_metrics.append({ "metric": metric, "value": recent_value, "severity": "critical" if recent_value >= threshold["critical"] else "high" }) # If multiple metrics are anomalous, create a composite anomaly if len(anomalous_metrics) >= 2: # Calculate combined confidence base_confidence = 0.7 + (len(anomalous_metrics) - 2) * 0.1 confidence = min(base_confidence, 0.97) # Determine overall severity (use highest severity) severities = [m["severity"] for m in anomalous_metrics] severity = "critical" if "critical" in severities else "high" anomaly = Anomaly( id=str(uuid.uuid4()), component=component, metric="correlated", value=len(anomalous_metrics), expected_range=(0, 1), confidence=confidence, severity=severity ) anomalies.append(anomaly) logger.info(f"Detection Agent: Found correlated anomaly across {len(anomalous_metrics)} metrics") return anomalies def _store_in_buffer(self, component: str, metric: str, points: List[TelemetryPoint]): """Store telemetry in buffer for trend analysis""" key = f"{component}:{metric}" if key not in self.telemetry_buffer: self.telemetry_buffer[key] = [] self.telemetry_buffer[key].extend(points) # Keep only last 100 points per metric if len(self.telemetry_buffer[key]) > 100: self.telemetry_buffer[key] = self.telemetry_buffer[key][-100:] def get_detection_stats(self) -> Dict[str, Any]: """Get detection statistics""" return { "total_detections": len(self.detection_history), "by_severity": { "critical": len([a for a in self.detection_history if a.severity == "critical"]), "high": len([a for a in self.detection_history if a.severity == "high"]), "medium": len([a for a in self.detection_history if a.severity == "medium"]), "low": len([a for a in self.detection_history if a.severity == "low"]), }, "buffer_size": sum(len(points) for points in self.telemetry_buffer.values()), "unique_metrics": len(self.telemetry_buffer), } # ============================================================================ # RECALL AGENT (RAG Memory) # ============================================================================ class RecallAgent: """ Recall Agent - RAG-based memory for similar incidents Features: - Vector similarity search - Incident clustering - Success rate tracking - Resolution pattern extraction """ def __init__(self, config: Optional[Dict[str, Any]] = None): self.config = config or {} self.incidents: List[Incident] = [] self.incident_vectors: List[List[float]] = [] # Resolution outcomes self.outcomes: Dict[str, Dict[str, Any]] = {} # incident_id -> outcome # Similarity cache self.similarity_cache: Dict[str, List[Dict[str, Any]]] = {} logger.info("Recall Agent initialized") async def add_incident(self, incident: Incident) -> str: """ Add incident to memory Args: incident: Incident to add Returns: Incident ID """ self.incidents.append(incident) self.incident_vectors.append(incident.to_vector()) logger.info(f"Recall Agent: Added incident {incident.id} for {incident.component}") return incident.id async def find_similar(self, current_incident: Incident, k: int = 5) -> List[Dict[str, Any]]: """ Find similar incidents using vector similarity Args: current_incident: Current incident to compare against k: Number of similar incidents to return Returns: List of similar incidents with similarity scores """ if not self.incidents: return [] # Check cache first cache_key = f"{current_incident.component}:{current_incident.anomaly.metric}" if cache_key in self.similarity_cache: return self.similarity_cache[cache_key][:k] # Calculate similarity current_vector = np.array(current_incident.to_vector()) similarities = [] for idx, (incident, vector) in enumerate(zip(self.incidents, self.incident_vectors)): # Skip if component doesn't match (optional) if current_incident.component != incident.component: continue # Calculate cosine similarity incident_vector = np.array(vector) if np.linalg.norm(current_vector) == 0 or np.linalg.norm(incident_vector) == 0: similarity = 0.0 else: similarity = np.dot(current_vector, incident_vector) / ( np.linalg.norm(current_vector) * np.linalg.norm(incident_vector) ) # Get outcome if available outcome = self.outcomes.get(incident.id, {}) success_rate = outcome.get("success_rate", 0.0) resolution_time = outcome.get("resolution_time_minutes", 0.0) similarities.append({ "incident": incident, "similarity": float(similarity), "success_rate": success_rate, "resolution_time_minutes": resolution_time, "index": idx }) # Sort by similarity (descending) similarities.sort(key=lambda x: x["similarity"], reverse=True) # Convert to simplified format results = [] for sim in similarities[:k]: incident = sim["incident"] results.append({ "incident_id": incident.id, "component": incident.component, "severity": incident.anomaly.severity, "similarity_score": sim["similarity"], "success_rate": sim["success_rate"], "resolution_time_minutes": sim["resolution_time_minutes"], "timestamp": incident.timestamp, "anomaly_metric": incident.anomaly.metric, "anomaly_value": incident.anomaly.value, }) # Cache results self.similarity_cache[cache_key] = results logger.info(f"Recall Agent: Found {len(results)} similar incidents for {current_incident.component}") return results async def add_outcome(self, incident_id: str, success: bool, resolution_action: str, resolution_time_minutes: float): """ Add resolution outcome to incident Args: incident_id: ID of the incident success: Whether the resolution was successful resolution_action: Action taken to resolve resolution_time_minutes: Time taken to resolve """ # Find incident incident_idx = -1 for idx, incident in enumerate(self.incidents): if incident.id == incident_id: incident_idx = idx break if incident_idx == -1: logger.warning(f"Recall Agent: Incident {incident_id} not found for outcome") return # Update incident self.incidents[incident_idx].resolved = True self.incidents[incident_idx].resolution = resolution_action # Store outcome if incident_id not in self.outcomes: self.outcomes[incident_id] = { "successes": 0, "attempts": 0, "actions": [], "resolution_times": [] } self.outcomes[incident_id]["attempts"] += 1 if success: self.outcomes[incident_id]["successes"] += 1 self.outcomes[incident_id]["actions"].append(resolution_action) self.outcomes[incident_id]["resolution_times"].append(resolution_time_minutes) # Update success rate attempts = self.outcomes[incident_id]["attempts"] successes = self.outcomes[incident_id]["successes"] self.outcomes[incident_id]["success_rate"] = successes / attempts if attempts > 0 else 0.0 # Update average resolution time times = self.outcomes[incident_id]["resolution_times"] self.outcomes[incident_id]["resolution_time_minutes"] = sum(times) / len(times) logger.info(f"Recall Agent: Added outcome for incident {incident_id} (success: {success})") def get_memory_stats(self) -> Dict[str, Any]: """Get memory statistics""" return { "total_incidents": len(self.incidents), "resolved_incidents": len([i for i in self.incidents if i.resolved]), "outcomes_tracked": len(self.outcomes), "cache_size": len(self.similarity_cache), "vector_dimension": len(self.incident_vectors[0]) if self.incident_vectors else 0, } # ============================================================================ # DECISION AGENT # ============================================================================ class DecisionAgent: """ Decision Agent - Generates healing intents based on analysis Features: - Confidence scoring - Action selection - Parameter optimization - Safety validation """ def __init__(self, config: Optional[Dict[str, Any]] = None): self.config = config or {} # Action success rates (learned from history) self.action_success_rates = { "restart_container": 0.95, "scale_out": 0.87, "circuit_breaker": 0.92, "traffic_shift": 0.85, "rollback": 0.78, "alert_team": 0.99, } # Action recommendations based on anomaly type self.anomaly_to_action = { "cpu_util": ["scale_out", "traffic_shift"], "memory_util": ["scale_out", "restart_container"], "error_rate": ["circuit_breaker", "rollback", "alert_team"], "latency_p99": ["scale_out", "traffic_shift", "circuit_breaker"], "throughput": ["scale_out", "traffic_shift"], "correlated": ["alert_team", "scale_out", "restart_container"], } logger.info("Decision Agent initialized") async def generate_healing_intent( self, anomaly: Anomaly, similar_incidents: List[Dict[str, Any]], context: Dict[str, Any] ) -> Dict[str, Any]: """ Generate healing intent based on anomaly and similar incidents Args: anomaly: Detected anomaly similar_incidents: Similar historical incidents context: Additional context Returns: Healing intent dictionary """ # Step 1: Select appropriate action action = await self._select_action(anomaly, similar_incidents) # Step 2: Calculate confidence confidence = await self._calculate_confidence(anomaly, similar_incidents, action) # Step 3: Determine parameters parameters = await self._determine_parameters(anomaly, action, context) # Step 4: Generate justification justification = await self._generate_justification(anomaly, similar_incidents, action, confidence) # Step 5: Create healing intent healing_intent = { "action": action, "component": anomaly.component, "parameters": parameters, "confidence": confidence, "justification": justification, "anomaly_id": anomaly.id, "anomaly_severity": anomaly.severity, "similar_incidents_count": len(similar_incidents), "similar_incidents_success_rate": self._calculate_average_success_rate(similar_incidents), "requires_enterprise": True, # OSS boundary "oss_advisory": True, "timestamp": time.time(), "arf_version": "3.3.7", } logger.info(f"Decision Agent: Generated {action} intent for {anomaly.component} (confidence: {confidence:.2f})") return healing_intent async def _select_action(self, anomaly: Anomaly, similar_incidents: List[Dict[str, Any]]) -> str: """Select the most appropriate healing action""" # Check similar incidents for successful actions if similar_incidents: # Group by action and calculate success rates action_successes = {} for incident in similar_incidents: # Extract action from resolution (simplified) resolution = incident.get("resolution", "") success = incident.get("success_rate", 0.5) > 0.5 if resolution: if resolution not in action_successes: action_successes[resolution] = {"successes": 0, "total": 0} action_successes[resolution]["total"] += 1 if success: action_successes[resolution]["successes"] += 1 # Calculate success rates for action, stats in action_successes.items(): success_rate = stats["successes"] / stats["total"] if stats["total"] > 0 else 0.0 action_successes[action]["rate"] = success_rate # Select action with highest success rate if action_successes: best_action = max(action_successes.items(), key=lambda x: x[1]["rate"]) return best_action[0] # Fallback: Use anomaly-to-action mapping candidate_actions = self.anomaly_to_action.get(anomaly.metric, ["alert_team"]) # Filter by severity if anomaly.severity in ["critical", "high"]: # Prefer more aggressive actions for severe anomalies preferred_actions = ["scale_out", "circuit_breaker", "restart_container"] candidate_actions = [a for a in candidate_actions if a in preferred_actions] # Select action with highest success rate if candidate_actions: action_rates = [(a, self.action_success_rates.get(a, 0.5)) for a in candidate_actions] return max(action_rates, key=lambda x: x[1])[0] return "alert_team" # Default safe action async def _calculate_confidence(self, anomaly: Anomaly, similar_incidents: List[Dict[str, Any]], selected_action: str) -> float: """Calculate confidence score for the selected action""" base_confidence = anomaly.confidence * 0.8 # Start with detection confidence # Boost for similar incidents if similar_incidents: avg_similarity = np.mean([i.get("similarity_score", 0.0) for i in similar_incidents]) similarity_boost = avg_similarity * 0.3 base_confidence += similarity_boost # Boost for successful similar incidents avg_success = self._calculate_average_success_rate(similar_incidents) success_boost = avg_success * 0.2 base_confidence += success_boost # Adjust for action success rate action_rate = self.action_success_rates.get(selected_action, 0.5) action_factor = 0.5 + action_rate * 0.5 # Map 0-1 success rate to 0.5-1.0 factor base_confidence *= action_factor # Cap at 0.99 (never 100% certain) return min(base_confidence, 0.99) async def _determine_parameters(self, anomaly: Anomaly, action: str, context: Dict[str, Any]) -> Dict[str, Any]: """Determine parameters for the healing action""" parameters = {} if action == "scale_out": # Scale factor based on severity severity_factor = {"low": 1, "medium": 2, "high": 3, "critical": 4} scale_factor = severity_factor.get(anomaly.severity, 2) parameters = { "scale_factor": scale_factor, "resource_profile": "standard", "strategy": "gradual" if anomaly.severity in ["low", "medium"] else "immediate" } elif action == "restart_container": parameters = { "grace_period": 30, "force": anomaly.severity == "critical" } elif action == "circuit_breaker": parameters = { "threshold": 0.5, "timeout": 60, "half_open_after": 300 } elif action == "rollback": parameters = { "revision": "previous", "verify": True } elif action == "traffic_shift": parameters = { "percentage": 50, "target": "canary" if anomaly.severity in ["low", "medium"] else "stable" } elif action == "alert_team": parameters = { "severity": anomaly.severity, "channels": ["slack", "email"], "escalate_after_minutes": 5 if anomaly.severity == "critical" else 15 } # Add context-specific parameters if "environment" in context: parameters["environment"] = context["environment"] return parameters async def _generate_justification(self, anomaly: Anomaly, similar_incidents: List[Dict[str, Any]], action: str, confidence: float) -> str: """Generate human-readable justification""" if similar_incidents: similar_count = len(similar_incidents) avg_success = self._calculate_average_success_rate(similar_incidents) return ( f"Detected {anomaly.severity} anomaly in {anomaly.component} ({anomaly.metric}: {anomaly.value:.2f}). " f"Found {similar_count} similar historical incidents with {avg_success:.0%} average success rate. " f"Recommended action '{action}' with {confidence:.0%} confidence based on pattern matching." ) else: return ( f"Detected {anomaly.severity} anomaly in {anomaly.component} ({anomaly.metric}: {anomaly.value:.2f}). " f"No similar historical incidents found. " f"Recommended action '{action}' with {confidence:.0%} confidence based on anomaly characteristics." ) def _calculate_average_success_rate(self, similar_incidents: List[Dict[str, Any]]) -> float: """Calculate average success rate from similar incidents""" if not similar_incidents: return 0.0 success_rates = [inc.get("success_rate", 0.0) for inc in similar_incidents] return sum(success_rates) / len(success_rates) def update_success_rate(self, action: str, success: bool): """Update action success rate based on outcome""" if action not in self.action_success_rates: self.action_success_rates[action] = 0.5 current_rate = self.action_success_rates[action] # Simple moving average update if success: new_rate = current_rate * 0.9 + 0.1 else: new_rate = current_rate * 0.9 self.action_success_rates[action] = new_rate logger.info(f"Decision Agent: Updated {action} success rate to {new_rate:.2f}") # ============================================================================ # TRUE ARF OSS INTEGRATION # ============================================================================ class TrueARFOSS: """ True ARF OSS v3.3.7 - Complete integration of all agents This is the class that TrueARF337Orchestrator expects to import. Provides real ARF OSS functionality for the demo. """ def __init__(self, config: Optional[Dict[str, Any]] = None): self.config = config or {} self.detection_agent = DetectionAgent(config) self.recall_agent = RecallAgent(config) self.decision_agent = DecisionAgent(config) self.oss_available = True logger.info("True ARF OSS v3.3.7 initialized") async def analyze_scenario(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]: """ Complete ARF analysis for a scenario Args: scenario_name: Name of the scenario scenario_data: Scenario data including telemetry and context Returns: Complete analysis result """ start_time = time.time() try: # Extract component and telemetry from scenario component = scenario_data.get("component", "unknown") telemetry_data = scenario_data.get("telemetry", []) context = scenario_data.get("context", {}) # Convert telemetry data to TelemetryPoint objects telemetry = [] for point in telemetry_data: telemetry.append(TelemetryPoint( timestamp=point.get("timestamp", time.time()), metric=point.get("metric", "unknown"), value=point.get("value", 0.0), component=component )) # Step 1: Detection Agent - Find anomalies logger.info(f"True ARF OSS: Running detection for {scenario_name}") anomalies = await self.detection_agent.analyze_telemetry(component, telemetry) if not anomalies: # No anomalies detected return { "status": "success", "scenario": scenario_name, "result": "no_anomalies_detected", "analysis_time_ms": (time.time() - start_time) * 1000, "arf_version": "3.3.7", "oss_edition": True } # Use the most severe anomaly anomaly = max(anomalies, key=lambda a: a.confidence) # Create incident for RAG memory incident = Incident( id=str(uuid.uuid4()), component=component, anomaly=anomaly, telemetry=telemetry[-10:], # Last 10 telemetry points context=context ) # Step 2: Recall Agent - Find similar incidents logger.info(f"True ARF OSS: Searching for similar incidents for {scenario_name}") similar_incidents = await self.recall_agent.find_similar(incident, k=5) # Add incident to memory await self.recall_agent.add_incident(incident) # Step 3: Decision Agent - Generate healing intent logger.info(f"True ARF OSS: Generating healing intent for {scenario_name}") healing_intent = await self.decision_agent.generate_healing_intent( anomaly, similar_incidents, context ) # Calculate analysis metrics analysis_time_ms = (time.time() - start_time) * 1000 # Create comprehensive result result = { "status": "success", "scenario": scenario_name, "analysis": { "detection": { "anomaly_found": True, "anomaly_id": anomaly.id, "metric": anomaly.metric, "value": anomaly.value, "confidence": anomaly.confidence, "severity": anomaly.severity, "detection_time_ms": analysis_time_ms * 0.3, # Estimated }, "recall": similar_incidents, "decision": healing_intent, }, "capabilities": { "execution_allowed": False, # OSS boundary "mcp_modes": ["advisory"], "oss_boundary": "advisory_only", "requires_enterprise": True, }, "agents_used": ["Detection", "Recall", "Decision"], "analysis_time_ms": analysis_time_ms, "arf_version": "3.3.7", "oss_edition": True, "demo_display": { "real_arf_version": "3.3.7", "true_oss_used": True, "enterprise_simulated": False, "agent_details": { "detection_confidence": anomaly.confidence, "similar_incidents_count": len(similar_incidents), "decision_confidence": healing_intent["confidence"], "healing_action": healing_intent["action"], } } } logger.info(f"True ARF OSS: Analysis complete for {scenario_name} " f"({analysis_time_ms:.1f}ms)") return result except Exception as e: logger.error(f"True ARF OSS analysis failed: {e}", exc_info=True) return { "status": "error", "error": str(e), "scenario": scenario_name, "analysis_time_ms": (time.time() - start_time) * 1000, "arf_version": "3.3.7", "oss_edition": True, "demo_display": { "real_arf_version": "3.3.7", "true_oss_used": True, "error": str(e)[:100] } } def get_agent_stats(self) -> Dict[str, Any]: """Get statistics from all agents""" return { "detection": self.detection_agent.get_detection_stats(), "recall": self.recall_agent.get_memory_stats(), "decision": { "action_success_rates": self.decision_agent.action_success_rates }, "oss_available": self.oss_available, "arf_version": "3.3.7", } # ============================================================================ # FACTORY FUNCTION # ============================================================================ async def get_true_arf_oss(config: Optional[Dict[str, Any]] = None) -> TrueARFOSS: """ Factory function for TrueARFOSS This is the function that TrueARF337Orchestrator expects to call. Args: config: Optional configuration Returns: TrueARFOSS instance """ return TrueARFOSS(config) # ============================================================================ # SIMPLE MOCK FOR BACKWARDS COMPATIBILITY # ============================================================================ async def get_mock_true_arf_oss(config: Optional[Dict[str, Any]] = None) -> TrueARFOSS: """ Mock version for when dependencies are missing """ logger.warning("Using mock TrueARFOSS - real implementation not available") class MockTrueARFOSS: def __init__(self, config): self.config = config or {} self.oss_available = False async def analyze_scenario(self, scenario_name, scenario_data): return { "status": "mock", "scenario": scenario_name, "message": "Mock analysis - install true ARF OSS v3.3.7 for real analysis", "demo_display": { "real_arf_version": "mock", "true_oss_used": False, "enterprise_simulated": False, } } return MockTrue