Delete agent_orchestrator.py

agent_orchestrator.py

@@ -1,461 +0,0 @@
-import asyncio
-from typing import Dict, List, Any
-from dataclasses import dataclass
-from monitoring_models import AgentSpecialization
-from models import ReliabilityEvent, AnomalyResult
-
-@dataclass
-class AgentResult:
-    specialization: AgentSpecialization
-    confidence: float
-    findings: Dict[str, Any]
-    recommendations: List[str]
-    processing_time: float
-
-class BaseAgent:
-    def __init__(self, specialization: AgentSpecialization):
-        self.specialization = specialization
-        self.performance_metrics = {
-            'processed_events': 0,
-            'successful_analyses': 0,
-            'average_confidence': 0.0
-        }
-
-    async def analyze(self, event: ReliabilityEvent) -> AgentResult:
-        """Base analysis method to be implemented by specialized agents"""
-        raise NotImplementedError
-
-class AnomalyDetectionAgent(BaseAgent):
-    def __init__(self):
-        super().__init__(AgentSpecialization.DETECTIVE)
-        self.adaptive_thresholds = {
-            'latency_p99': 150,
-            'error_rate': 0.05,
-            'cpu_util': 0.8,
-            'memory_util': 0.8
-        }
-
-    async def analyze(self, event: ReliabilityEvent) -> AgentResult:
-        """Enhanced anomaly detection with pattern recognition"""
-        start_time = asyncio.get_event_loop().time()
-
-        # Multi-dimensional anomaly scoring
-        anomaly_score = self._calculate_anomaly_score(event)
-        pattern_match = self._detect_known_patterns(event)
-
-        return AgentResult(
-            specialization=self.specialization,
-            confidence=anomaly_score,
-            findings={
-                'anomaly_score': anomaly_score,
-                'detected_patterns': pattern_match,
-                'affected_metrics': self._identify_affected_metrics(event),
-                'severity_tier': self._classify_severity(anomaly_score)
-            },
-            recommendations=self._generate_detection_recommendations(event, anomaly_score),
-            processing_time=asyncio.get_event_loop().time() - start_time
-        )
-
-    def _calculate_anomaly_score(self, event: ReliabilityEvent) -> float:
-        """Calculate comprehensive anomaly score (0-1)"""
-        scores = []
-
-        # Latency anomaly (weighted 40%)
-        if event.latency_p99 > self.adaptive_thresholds['latency_p99']:
-            latency_score = min(1.0, (event.latency_p99 - self.adaptive_thresholds['latency_p99']) / 500)
-            scores.append(0.4 * latency_score)
-
-        # Error rate anomaly (weighted 30%)
-        if event.error_rate > self.adaptive_thresholds['error_rate']:
-            error_score = min(1.0, event.error_rate / 0.3)
-            scores.append(0.3 * error_score)
-
-        # Resource anomaly (weighted 30%)
-        resource_score = 0
-        if event.cpu_util and event.cpu_util > self.adaptive_thresholds['cpu_util']:
-            resource_score += 0.15 * min(1.0, (event.cpu_util - self.adaptive_thresholds['cpu_util']) / 0.2)
-        if event.memory_util and event.memory_util > self.adaptive_thresholds['memory_util']:
-            resource_score += 0.15 * min(1.0, (event.memory_util - self.adaptive_thresholds['memory_util']) / 0.2)
-        scores.append(resource_score)
-
-        return min(1.0, sum(scores))
-
-    def _detect_known_patterns(self, event: ReliabilityEvent) -> List[str]:
-        """Detect known failure patterns"""
-        patterns = []
-        
-        # Database timeout pattern
-        if event.latency_p99 > 500 and event.error_rate > 0.2:
-            patterns.append("database_timeout")
-        
-        # Resource exhaustion pattern
-        if event.cpu_util and event.cpu_util > 0.9 and event.memory_util and event.memory_util > 0.9:
-            patterns.append("resource_exhaustion")
-        
-        # Cascading failure pattern
-        if event.error_rate > 0.15 and event.latency_p99 > 300:
-            patterns.append("cascading_failure")
-        
-        # Traffic spike pattern
-        if event.latency_p99 > 200 and event.throughput > 2000:
-            patterns.append("traffic_spike")
-        
-        # Gradual degradation
-        if 150 < event.latency_p99 < 300 and 0.05 < event.error_rate < 0.15:
-            patterns.append("gradual_degradation")
-        
-        return patterns if patterns else ["unknown_pattern"]
-
-    def _identify_affected_metrics(self, event: ReliabilityEvent) -> List[str]:
-        """Identify which metrics are outside normal range"""
-        affected = []
-        
-        if event.latency_p99 > self.adaptive_thresholds['latency_p99']:
-            affected.append("latency")
-        
-        if event.error_rate > self.adaptive_thresholds['error_rate']:
-            affected.append("error_rate")
-        
-        if event.cpu_util and event.cpu_util > self.adaptive_thresholds['cpu_util']:
-            affected.append("cpu")
-        
-        if event.memory_util and event.memory_util > self.adaptive_thresholds['memory_util']:
-            affected.append("memory")
-        
-        if event.throughput < 500:  # Low throughput threshold
-            affected.append("throughput")
-        
-        return affected if affected else ["none"]
-
-    def _classify_severity(self, anomaly_score: float) -> str:
-        """Classify severity based on anomaly score"""
-        if anomaly_score > 0.8:
-            return "CRITICAL"
-        elif anomaly_score > 0.6:
-            return "HIGH"
-        elif anomaly_score > 0.4:
-            return "MEDIUM"
-        return "LOW"
-
-    def _generate_detection_recommendations(self, event: ReliabilityEvent, anomaly_score: float) -> List[str]:
-        """Generate actionable recommendations based on detected anomalies"""
-        recommendations = []
-        
-        # Latency recommendations
-        if event.latency_p99 > 500:
-            recommendations.append("🚨 CRITICAL: Latency >500ms - Check database connections and external APIs immediately")
-        elif event.latency_p99 > 300:
-            recommendations.append("⚠️ HIGH: Latency >300ms - Investigate slow queries and service dependencies")
-        elif event.latency_p99 > 150:
-            recommendations.append("📈 Latency elevated - Monitor trends and consider optimization")
-        
-        # Error rate recommendations
-        if event.error_rate > 0.3:
-            recommendations.append("🚨 CRITICAL: Error rate >30% - Rollback recent deployments or enable circuit breaker")
-        elif event.error_rate > 0.15:
-            recommendations.append("⚠️ HIGH: Error rate >15% - Review application logs for exceptions")
-        elif event.error_rate > 0.05:
-            recommendations.append("📈 Errors increasing - Check for configuration issues")
-        
-        # Resource recommendations
-        if event.cpu_util and event.cpu_util > 0.9:
-            recommendations.append("🔥 CPU CRITICAL: >90% utilization - Scale horizontally or optimize hot paths")
-        elif event.cpu_util and event.cpu_util > 0.8:
-            recommendations.append("⚡ CPU HIGH: >80% utilization - Consider adding capacity")
-        
-        if event.memory_util and event.memory_util > 0.9:
-            recommendations.append("💾 MEMORY CRITICAL: >90% utilization - Check for memory leaks")
-        elif event.memory_util and event.memory_util > 0.8:
-            recommendations.append("💾 MEMORY HIGH: >80% utilization - Monitor for leaks")
-        
-        # Overall severity recommendations
-        if anomaly_score > 0.8:
-            recommendations.append("🎯 IMMEDIATE ACTION REQUIRED: Multiple critical metrics affected")
-        elif anomaly_score > 0.6:
-            recommendations.append("🎯 INVESTIGATE: Significant performance degradation detected")
-        elif anomaly_score > 0.4:
-            recommendations.append("📊 MONITOR: Early warning signs detected")
-        
-        return recommendations[:5]  # Return top 5 recommendations
-
-class RootCauseAgent(BaseAgent):
-    def __init__(self):
-        super().__init__(AgentSpecialization.DIAGNOSTICIAN)
-        self.causal_patterns = self._load_causal_patterns()
-
-    async def analyze(self, event: ReliabilityEvent) -> AgentResult:
-        """AI-powered root cause analysis"""
-        start_time = asyncio.get_event_loop().time()
-
-        root_cause_analysis = self._perform_causal_analysis(event)
-
-        return AgentResult(
-            specialization=self.specialization,
-            confidence=root_cause_analysis['confidence'],
-            findings={
-                'likely_root_causes': root_cause_analysis['causes'],
-                'evidence_patterns': root_cause_analysis['evidence'],
-                'dependency_analysis': self._analyze_dependencies(event),
-                'timeline_correlation': self._check_temporal_patterns(event)
-            },
-            recommendations=root_cause_analysis['investigation_steps'],
-            processing_time=asyncio.get_event_loop().time() - start_time
-        )
-
-    def _load_causal_patterns(self) -> Dict[str, Any]:
-        """Load known causal patterns for root cause analysis"""
-        return {
-            'high_latency_high_errors': {
-                'pattern': ['latency > 500', 'error_rate > 0.2'],
-                'cause': 'Database or external dependency failure',
-                'confidence': 0.85
-            },
-            'high_cpu_high_memory': {
-                'pattern': ['cpu > 0.9', 'memory > 0.9'],
-                'cause': 'Resource exhaustion or memory leak',
-                'confidence': 0.90
-            },
-            'high_errors_normal_latency': {
-                'pattern': ['error_rate > 0.3', 'latency < 200'],
-                'cause': 'Application bug or configuration issue',
-                'confidence': 0.75
-            },
-            'gradual_degradation': {
-                'pattern': ['200 < latency < 400', '0.05 < error_rate < 0.15'],
-                'cause': 'Resource saturation or dependency degradation',
-                'confidence': 0.65
-            }
-        }
-
-    def _perform_causal_analysis(self, event: ReliabilityEvent) -> Dict[str, Any]:
-        """Analyze likely root causes based on event patterns"""
-        causes = []
-        evidence = []
-        confidence = 0.5
-        
-        # Pattern 1: Database/External Dependency Failure
-        if event.latency_p99 > 500 and event.error_rate > 0.2:
-            causes.append({
-                "cause": "Database/External Dependency Failure",
-                "confidence": 0.85,
-                "evidence": f"Extreme latency ({event.latency_p99:.0f}ms) with high errors ({event.error_rate*100:.1f}%)",
-                "investigation": "Check database connection pool, external API health, network connectivity"
-            })
-            evidence.append("extreme_latency_with_errors")
-            confidence = 0.85
-        
-        # Pattern 2: Resource Exhaustion
-        if event.cpu_util and event.cpu_util > 0.9 and event.memory_util and event.memory_util > 0.9:
-            causes.append({
-                "cause": "Resource Exhaustion",
-                "confidence": 0.90,
-                "evidence": f"CPU ({event.cpu_util*100:.1f}%) and Memory ({event.memory_util*100:.1f}%) critically high",
-                "investigation": "Check for memory leaks, infinite loops, insufficient resource allocation"
-            })
-            evidence.append("correlated_resource_exhaustion")
-            confidence = max(confidence, 0.90)
-        
-        # Pattern 3: Application Bug / Configuration Issue
-        if event.error_rate > 0.3 and event.latency_p99 < 200:
-            causes.append({
-                "cause": "Application Bug / Configuration Issue",
-                "confidence": 0.75,
-                "evidence": f"High error rate ({event.error_rate*100:.1f}%) without latency impact",
-                "investigation": "Review recent deployments, configuration changes, application logs, and error traces"
-            })
-            evidence.append("errors_without_latency")
-            confidence = max(confidence, 0.75)
-        
-        # Pattern 4: Gradual Performance Degradation
-        if 200 <= event.latency_p99 <= 400 and 0.05 <= event.error_rate <= 0.15:
-            causes.append({
-                "cause": "Gradual Performance Degradation",
-                "confidence": 0.65,
-                "evidence": f"Moderate latency ({event.latency_p99:.0f}ms) and errors ({event.error_rate*100:.1f}%)",
-                "investigation": "Check resource trends, dependency performance, capacity planning, and scaling policies"
-            })
-            evidence.append("gradual_degradation")
-            confidence = max(confidence, 0.65)
-        
-        # Pattern 5: Traffic Spike
-        if event.latency_p99 > 200 and event.throughput > 2000:
-            causes.append({
-                "cause": "Traffic Spike / Capacity Issue",
-                "confidence": 0.70,
-                "evidence": f"Elevated latency ({event.latency_p99:.0f}ms) with high throughput ({event.throughput:.0f} req/s)",
-                "investigation": "Check autoscaling configuration, rate limiting, and load balancer health"
-            })
-            evidence.append("traffic_spike")
-            confidence = max(confidence, 0.70)
-        
-        # Default: Unknown pattern
-        if not causes:
-            causes.append({
-                "cause": "Unknown - Requires Investigation",
-                "confidence": 0.3,
-                "evidence": "Pattern does not match known failure modes",
-                "investigation": "Complete system review needed - check logs, metrics, and recent changes"
-            })
-            evidence.append("unknown_pattern")
-            confidence = 0.3
-        
-        # Generate investigation steps
-        investigation_steps = [cause['investigation'] for cause in causes[:3]]
-        
-        return {
-            'confidence': confidence,
-            'causes': causes,
-            'evidence': evidence,
-            'investigation_steps': investigation_steps
-        }
-
-    def _analyze_dependencies(self, event: ReliabilityEvent) -> Dict[str, Any]:
-        """Analyze dependency health and potential cascade effects"""
-        analysis = {
-            'has_upstream_deps': len(event.upstream_deps) > 0,
-            'upstream_services': event.upstream_deps,
-            'potential_cascade': False,
-            'cascade_risk_score': 0.0
-        }
-        
-        # Calculate cascade risk
-        if event.error_rate > 0.2:
-            analysis['potential_cascade'] = True
-            analysis['cascade_risk_score'] = min(1.0, event.error_rate * 2)
-        
-        if event.latency_p99 > 500:
-            analysis['potential_cascade'] = True
-            analysis['cascade_risk_score'] = max(
-                analysis['cascade_risk_score'],
-                min(1.0, event.latency_p99 / 1000)
-            )
-        
-        return analysis
-
-    def _check_temporal_patterns(self, event: ReliabilityEvent) -> Dict[str, Any]:
-        """Check for time-based correlations"""
-        import datetime
-        
-        current_time = datetime.datetime.now()
-        hour = current_time.hour
-        
-        # Check for typical patterns
-        patterns = {
-            'time_of_day_correlation': False,
-            'is_peak_hours': 9 <= hour <= 17,  # Business hours
-            'is_off_hours': hour < 6 or hour > 22,
-            'deployment_window': 14 <= hour <= 16,  # Typical deployment window
-            'weekend': current_time.weekday() >= 5
-        }
-        
-        # Flag potential correlations
-        if patterns['is_peak_hours'] and event.latency_p99 > 200:
-            patterns['time_of_day_correlation'] = True
-        
-        return patterns
-
-class OrchestrationManager:
-    def __init__(self):
-        self.agents = {
-            AgentSpecialization.DETECTIVE: AnomalyDetectionAgent(),
-            AgentSpecialization.DIAGNOSTICIAN: RootCauseAgent(),
-        }
-        self.incident_history = []
-
-    async def orchestrate_analysis(self, event: ReliabilityEvent) -> Dict[str, Any]:
-        """Coordinate multiple agents for comprehensive analysis"""
-        agent_tasks = {
-            spec: agent.analyze(event)
-            for spec, agent in self.agents.items()
-        }
-
-        # Parallel agent execution with error handling
-        agent_results = {}
-        for specialization, task in agent_tasks.items():
-            try:
-                result = await asyncio.wait_for(task, timeout=10.0)
-                agent_results[specialization.value] = result
-            except asyncio.TimeoutError:
-                # Agent timeout - continue with others
-                print(f"Agent {specialization.value} timed out")
-                continue
-            except Exception as e:
-                # Agent error - log and continue
-                print(f"Agent {specialization.value} error: {e}")
-                continue
-
-        # Synthesize results
-        return self._synthesize_agent_findings(event, agent_results)
-
-    def _synthesize_agent_findings(self, event: ReliabilityEvent, agent_results: Dict) -> Dict[str, Any]:
-        """Combine insights from all specialized agents"""
-        detective_result = agent_results.get(AgentSpecialization.DETECTIVE.value)
-        diagnostician_result = agent_results.get(AgentSpecialization.DIAGNOSTICIAN.value)
-
-        if not detective_result:
-            return {'error': 'No agent results available'}
-
-        # Build comprehensive analysis
-        synthesis = {
-            'incident_summary': {
-                'severity': detective_result.findings.get('severity_tier', 'UNKNOWN'),
-                'anomaly_confidence': detective_result.confidence,
-                'primary_metrics_affected': detective_result.findings.get('affected_metrics', [])
-            },
-            'root_cause_insights': diagnostician_result.findings if diagnostician_result else {},
-            'recommended_actions': self._prioritize_actions(
-                detective_result.recommendations,
-                diagnostician_result.recommendations if diagnostician_result else []
-            ),
-            'business_context': self._add_business_context(event, detective_result.confidence),
-            'agent_metadata': {
-                'participating_agents': list(agent_results.keys()),
-                'processing_times': {k: v.processing_time for k, v in agent_results.items()}
-            }
-        }
-
-        return synthesis
-
-    def _prioritize_actions(self, detection_actions: List[str], diagnosis_actions: List[str]) -> List[str]:
-        """Combine and prioritize actions from multiple agents"""
-        all_actions = []
-        
-        # Add critical actions first (those with 🚨)
-        critical = [a for a in detection_actions + diagnosis_actions if '🚨' in a]
-        all_actions.extend(critical)
-        
-        # Add high priority actions (those with ⚠️)
-        high = [a for a in detection_actions + diagnosis_actions if '⚠️' in a and a not in all_actions]
-        all_actions.extend(high)
-        
-        # Add remaining actions
-        remaining = [a for a in detection_actions + diagnosis_actions if a not in all_actions]
-        all_actions.extend(remaining)
-        
-        # Remove duplicates while preserving order
-        seen = set()
-        unique_actions = []
-        for action in all_actions:
-            if action not in seen:
-                seen.add(action)
-                unique_actions.append(action)
-        
-        return unique_actions[:5]  # Return top 5 actions
-
-    def _add_business_context(self, event: ReliabilityEvent, confidence: float) -> Dict[str, Any]:
-        """Add business impact context to the analysis"""
-        # Calculate business severity
-        if confidence > 0.8:
-            business_severity = "CRITICAL"
-        elif confidence > 0.6:
-            business_severity = "HIGH"
-        elif confidence > 0.4:
-            business_severity = "MEDIUM"
-        else:
-            business_severity = "LOW"
-        
-        return {
-            'business_severity': business_severity,
-            'estimated_impact': f"{confidence * 100:.0f}% confidence of incident",
-            'recommended_escalation': confidence > 0.7
-        }