Create agent_orchestrator.py

agent_orchestrator.py

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+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 = {}
+    
+    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 > 150:
+            latency_score = min(1.0, (event.latency_p99 - 150) / 500)
+            scores.append(0.4 * latency_score)
+        
+        # Error rate anomaly (weighted 30%)
+        if event.error_rate > 0.05:
+            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 > 0.8:
+            resource_score += 0.15 * min(1.0, (event.cpu_util - 0.8) / 0.2)
+        if event.memory_util and event.memory_util > 0.8:
+            resource_score += 0.15 * min(1.0, (event.memory_util - 0.8) / 0.2)
+        scores.append(resource_score)
+        
+        return min(1.0, sum(scores))
+
+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
+        )
+
+class OrchestrationManager:
+    def __init__(self):
+        self.agents = {
+            AgentSpecialization.DETECTIVE: AnomalyDetectionAgent(),
+            AgentSpecialization.DIAGNOSTICIAN: RootCauseAgent(),
+            # Add more agents as we build them
+        }
+        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
+        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
+                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