Agent Observability Architecture

use chrono::{DateTime, Utc};
use uuid::Uuid;
use anyhow::Result;
 
pub struct DecisionCapture {
    decision_store: DecisionStore,
    analytics_engine: AnalyticsEngine,
}
 
impl DecisionCapture {
    pub fn new() -> Self {
        Self {
            decision_store: DecisionStore::new(),
            analytics_engine: AnalyticsEngine::new(),
        }
    }
    
    pub async fn capture_decision(
        &self, 
        agent_id: &str, 
        decision_context: DecisionContext
    ) -> Result<DecisionRecord> {
        let record = DecisionRecord {
            decision_id: self.generate_decision_id(),
            agent_id: agent_id.to_string(),
            timestamp: Utc::now(),
            context: decision_context.clone(),
            reasoning_chain: self.extract_reasoning_chain(&decision_context)?,
            alternatives_considered: self.extract_alternatives(&decision_context)?,
            confidence_score: decision_context.confidence,
            execution_time: decision_context.processing_time,
        };
        
        // Real-time storage
        self.decision_store.store(&record).await?;
        
        // Trigger analytics
        self.analytics_engine.analyze_decision(&record).await?;
        
        Ok(record)
    }
    
    fn generate_decision_id(&self) -> String {
        Uuid::new_v4().to_string()
    }
}

class DecisionQualityScorer:
    def score_decision(self, decision: DecisionRecord, outcome: DecisionOutcome) -> QualityScore:
        quality_factors = {
            "correctness": self.assess_correctness(decision, outcome),
            "timeliness": self.assess_timeliness(decision, outcome),
            "efficiency": self.assess_efficiency(decision, outcome),
            "consistency": self.assess_consistency(decision),
            "reasoning_quality": self.assess_reasoning_quality(decision),
            "confidence_calibration": self.assess_calibration(decision, outcome)
        }
        
        # Weighted scoring
        weights = self.get_quality_weights(decision.context.domain)
        overall_score = sum(
            score * weights[factor] 
            for factor, score in quality_factors.items()
        )
        
        return QualityScore(
            overall_score=overall_score,
            factor_scores=quality_factors,
            confidence_interval=self.calculate_confidence_interval(quality_factors),
            improvement_suggestions=self.generate_suggestions(quality_factors)
        )

reasoning_analysis:
  chain_elements:
    - premise_identification
    - logical_connections
    - evidence_evaluation
    - assumption_validity
    - conclusion_derivation
    
  quality_metrics:
    - logical_consistency: 0.95
    - evidence_strength: 0.87
    - assumption_validity: 0.92
    - chain_completeness: 0.89
    
  pattern_detection:
    - common_reasoning_patterns
    - error_prone_reasoning_types
    - high_quality_reasoning_templates
    - improvement_opportunities

class LearningProgressTracker:
    def track_learning_event(self, agent_id: str, learning_event: LearningEvent) -> LearningRecord:
        record = LearningRecord(
            event_id=self.generate_event_id(),
            agent_id=agent_id,
            timestamp=datetime.utcnow(),
            learning_type=learning_event.type,  # skill, knowledge, pattern, behavior
            content=learning_event.content,
            source=learning_event.source,
            context=learning_event.context,
            success_metrics=learning_event.metrics
        )
        
        # Update capability tracking
        self.capability_tracker.update(agent_id, record)
        
        # Assess learning effectiveness
        effectiveness = self.assess_learning_effectiveness(record)
        record.effectiveness_score = effectiveness
        
        return record

class CapabilityAssessor:
    def assess_capabilities(self, agent_id: str) -> CapabilityAssessment:
        current_capabilities = self.get_current_capabilities(agent_id)
        
        assessments = {}
        for capability_name, capability in current_capabilities.items():
            assessment = CapabilityLevel(
                name=capability_name,
                proficiency_level=self.measure_proficiency(capability),
                confidence_level=self.measure_confidence(capability),
                learning_rate=self.calculate_learning_rate(capability),
                mastery_indicators=self.identify_mastery_indicators(capability),
                improvement_areas=self.identify_improvement_areas(capability)
            )
            assessments[capability_name] = assessment
            
        return CapabilityAssessment(
            agent_id=agent_id,
            assessment_timestamp=datetime.utcnow(),
            capability_levels=assessments,
            overall_learning_velocity=self.calculate_overall_velocity(assessments),
            next_learning_recommendations=self.recommend_learning_paths(assessments)
        )

knowledge_tracking:
  acquisition_types:
    - explicit_knowledge: "Facts, procedures, rules"
    - tacit_knowledge: "Patterns, intuitions, experiences"
    - procedural_knowledge: "How-to knowledge"
    - conditional_knowledge: "When and why knowledge"
    
  measurement_metrics:
    - retention_rate: "Knowledge persistence over time"
    - application_rate: "Frequency of knowledge usage"
    - transfer_rate: "Knowledge application to new contexts"
    - integration_rate: "Connection with existing knowledge"
    
  learning_velocity:
    - concepts_per_day: 15
    - patterns_per_week: 8
    - skills_per_month: 3
    - expertise_development_time: "3-6 months"

class PerformanceTracker:
    def track_performance(self, agent_id: str, task: Task, execution: TaskExecution) -> PerformanceRecord:
        performance_metrics = {
            "accuracy": self.measure_accuracy(task, execution),
            "speed": self.measure_speed(task, execution),
            "efficiency": self.measure_efficiency(task, execution),
            "reliability": self.measure_reliability(task, execution),
            "adaptability": self.measure_adaptability(task, execution),
            "creativity": self.measure_creativity(task, execution),
            "collaboration": self.measure_collaboration(task, execution)
        }
        
        # Contextual adjustments
        context_factors = self.analyze_context_factors(task.context)
        adjusted_metrics = self.adjust_for_context(performance_metrics, context_factors)
        
        return PerformanceRecord(
            agent_id=agent_id,
            task_id=task.id,
            timestamp=datetime.utcnow(),
            raw_metrics=performance_metrics,
            adjusted_metrics=adjusted_metrics,
            context_factors=context_factors,
            performance_trends=self.calculate_trends(agent_id, adjusted_metrics)
        )

class GoalAchievementAnalyzer:
    def analyze_goal_achievement(self, agent_id: str, period: timedelta) -> GoalAnalysis:
        goals = self.get_agent_goals(agent_id, period)
        
        analysis = {}
        for goal in goals:
            achievement_data = GoalAchievement(
                goal_id=goal.id,
                target_metrics=goal.target_metrics,
                actual_metrics=self.measure_actual_metrics(goal),
                achievement_rate=self.calculate_achievement_rate(goal),
                progress_trajectory=self.analyze_progress_trajectory(goal),
                obstacles_encountered=self.identify_obstacles(goal),
                success_factors=self.identify_success_factors(goal)
            )
            analysis[goal.id] = achievement_data
            
        return GoalAnalysis(
            agent_id=agent_id,
            analysis_period=period,
            goal_achievements=analysis,
            overall_achievement_rate=self.calculate_overall_rate(analysis),
            improvement_recommendations=self.generate_recommendations(analysis)
        )

class BehaviorPatternDetector:
    def detect_patterns(self, agent_id: str, observation_period: timedelta) -> BehaviorPatterns:
        behavior_data = self.collect_behavior_data(agent_id, observation_period)
        
        patterns = {
            "decision_patterns": self.detect_decision_patterns(behavior_data),
            "interaction_patterns": self.detect_interaction_patterns(behavior_data),
            "learning_patterns": self.detect_learning_patterns(behavior_data),
            "adaptation_patterns": self.detect_adaptation_patterns(behavior_data),
            "communication_patterns": self.detect_communication_patterns(behavior_data)
        }
        
        # Analyze pattern evolution
        pattern_evolution = self.analyze_pattern_evolution(patterns, agent_id)
        
        # Identify anomalies
        anomalies = self.detect_behavioral_anomalies(patterns, agent_id)
        
        return BehaviorPatterns(
            agent_id=agent_id,
            observation_period=observation_period,
            detected_patterns=patterns,
            pattern_evolution=pattern_evolution,
            anomalies=anomalies,
            confidence_scores=self.calculate_pattern_confidence(patterns)
        )

class DecisionAnalytics:
    def analyze_decision_patterns(self, agent_id: str) -> DecisionAnalysis:
        decisions = self.get_agent_decisions(agent_id)
        
        return DecisionAnalysis(
            decision_frequency=self.analyze_frequency(decisions),
            decision_types=self.categorize_decisions(decisions),
            decision_quality_trends=self.analyze_quality_trends(decisions),
            reasoning_patterns=self.analyze_reasoning_patterns(decisions),
            bias_detection=self.detect_biases(decisions),
            improvement_opportunities=self.identify_improvements(decisions)
        )
        
    def generate_decision_insights(self, analysis: DecisionAnalysis) -> List[Insight]:
        insights = []
        
        # Quality insights
        if analysis.decision_quality_trends.is_declining():
            insights.append(Insight(
                type="quality_decline",
                severity="medium",
                description="Decision quality showing declining trend",
                recommendations=["review_decision_criteria", "additional_training"]
            ))
        
        # Bias insights
        for bias in analysis.bias_detection.detected_biases:
            insights.append(Insight(
                type="bias_detection",
                severity="high",
                description=f"Detected {bias.type} bias in decisions",
                recommendations=bias.mitigation_strategies
            ))
        
        return insights

class LearningAnalytics:
    def analyze_learning_effectiveness(self, agent_id: str) -> LearningAnalysis:
        learning_data = self.get_learning_data(agent_id)
        
        return LearningAnalysis(
            learning_velocity=self.calculate_learning_velocity(learning_data),
            retention_rates=self.analyze_retention(learning_data),
            transfer_effectiveness=self.analyze_transfer(learning_data),
            learning_preferences=self.identify_preferences(learning_data),
            knowledge_gaps=self.identify_gaps(learning_data),
            optimization_opportunities=self.identify_optimizations(learning_data)
        )
        
    def predict_learning_outcomes(self, agent_id: str, learning_plan: LearningPlan) -> LearningPrediction:
        historical_performance = self.get_learning_performance(agent_id)
        
        # Machine learning model for prediction
        prediction_model = self.get_prediction_model()
        
        features = self.extract_features(agent_id, learning_plan, historical_performance)
        prediction = prediction_model.predict(features)
        
        return LearningPrediction(
            predicted_success_rate=prediction.success_rate,
            estimated_completion_time=prediction.completion_time,
            confidence_interval=prediction.confidence_interval,
            risk_factors=prediction.risk_factors,
            optimization_suggestions=prediction.optimizations
        )

class QualityAnalytics:
    def analyze_overall_quality(self, agent_id: str) -> QualityAnalysis:
        quality_data = self.collect_quality_data(agent_id)
        
        quality_dimensions = {
            "decision_quality": self.analyze_decision_quality(quality_data),
            "output_quality": self.analyze_output_quality(quality_data),
            "interaction_quality": self.analyze_interaction_quality(quality_data),
            "learning_quality": self.analyze_learning_quality(quality_data),
            "adaptation_quality": self.analyze_adaptation_quality(quality_data)
        }
        
        return QualityAnalysis(
            agent_id=agent_id,
            quality_dimensions=quality_dimensions,
            overall_quality_score=self.calculate_overall_quality(quality_dimensions),
            quality_trends=self.analyze_quality_trends(quality_dimensions),
            benchmark_comparison=self.compare_to_benchmarks(quality_dimensions),
            improvement_roadmap=self.generate_improvement_roadmap(quality_dimensions)
        )

class CognitiveInsights:
    def generate_cognitive_insights(self, agent_id: str) -> CognitiveReport:
        cognitive_data = self.collect_cognitive_data(agent_id)
        
        insights = {
            "thinking_patterns": self.analyze_thinking_patterns(cognitive_data),
            "problem_solving_approaches": self.analyze_problem_solving(cognitive_data),
            "memory_utilization": self.analyze_memory_usage(cognitive_data),
            "attention_patterns": self.analyze_attention_patterns(cognitive_data),
            "creativity_indicators": self.analyze_creativity(cognitive_data),
            "reasoning_strengths": self.identify_reasoning_strengths(cognitive_data),
            "cognitive_biases": self.detect_cognitive_biases(cognitive_data)
        }
        
        return CognitiveReport(
            agent_id=agent_id,
            cognitive_insights=insights,
            cognitive_profile=self.build_cognitive_profile(insights),
            development_recommendations=self.recommend_cognitive_development(insights)
        )

class PredictiveIntelligence:
    def predict_performance(self, agent_id: str, future_horizon: timedelta) -> PerformancePrediction:
        historical_data = self.get_historical_performance(agent_id)
        current_trends = self.analyze_current_trends(agent_id)
        environmental_factors = self.get_environmental_factors()
        
        # ML-based prediction
        prediction_model = self.get_performance_prediction_model()
        features = self.prepare_features(historical_data, current_trends, environmental_factors)
        
        prediction = prediction_model.predict(features, horizon=future_horizon)
        
        return PerformancePrediction(
            agent_id=agent_id,
            prediction_horizon=future_horizon,
            predicted_metrics=prediction.metrics,
            confidence_intervals=prediction.confidence,
            risk_factors=prediction.risks,
            intervention_recommendations=prediction.interventions
        )
        
    def predict_learning_needs(self, agent_id: str) -> LearningNeeds:
        capability_gaps = self.identify_capability_gaps(agent_id)
        performance_trends = self.analyze_performance_trends(agent_id)
        upcoming_challenges = self.predict_challenges(agent_id)
        
        return LearningNeeds(
            immediate_needs=self.prioritize_immediate_needs(capability_gaps),
            strategic_needs=self.identify_strategic_needs(performance_trends),
            challenge_preparation=self.prepare_for_challenges(upcoming_challenges),
            learning_timeline=self.create_learning_timeline(capability_gaps, upcoming_challenges)
        )

dashboard_layout:
  overview_panel:
    - agent_health_status
    - current_performance_score
    - active_learning_sessions
    - recent_decisions_summary
    
  performance_panel:
    - performance_trends_chart
    - goal_achievement_progress
    - quality_metrics_gauge
    - efficiency_indicators
    
  learning_panel:
    - learning_velocity_chart
    - capability_development_radar
    - knowledge_acquisition_timeline
    - skill_mastery_progress
    
  behavior_panel:
    - decision_pattern_visualization
    - interaction_network_graph
    - adaptation_timeline
    - behavioral_anomaly_alerts
    
  insights_panel:
    - key_insights_summary
    - improvement_recommendations
    - risk_alerts
    - optimization_opportunities

class VisualizationEngine:
    def create_cognitive_map(self, agent_id: str) -> CognitiveVisualization:
        cognitive_data = self.get_cognitive_data(agent_id)
        
        return CognitiveVisualization(
            decision_flow_diagram=self.create_decision_flow(cognitive_data),
            knowledge_network_graph=self.create_knowledge_network(cognitive_data),
            learning_pathway_map=self.create_learning_pathways(cognitive_data),
            performance_heatmap=self.create_performance_heatmap(cognitive_data),
            behavior_pattern_timeline=self.create_behavior_timeline(cognitive_data)
        )
        
    def create_performance_dashboard(self, agent_id: str) -> PerformanceDashboard:
        performance_data = self.get_performance_data(agent_id)
        
        return PerformanceDashboard(
            real_time_metrics=self.create_real_time_metrics(performance_data),
            trend_charts=self.create_trend_charts(performance_data),
            comparison_charts=self.create_comparison_charts(performance_data),
            predictive_charts=self.create_predictive_charts(performance_data),
            interactive_explorer=self.create_interactive_explorer(performance_data)
        )

class ObservabilityMemoryIntegration:
    def correlate_with_memory(self, observation: Observation, agent_id: str) -> CorrelatedObservation:
        # Retrieve relevant memories
        related_memories = self.memory_system.find_related_memories(
            observation.context,
            agent_id,
            memory_types=["episodic", "semantic", "procedural"]
        )
        
        # Analyze memory influence on current observation
        memory_influence = self.analyze_memory_influence(observation, related_memories)
        
        return CorrelatedObservation(
            original_observation=observation,
            related_memories=related_memories,
            memory_influence_score=memory_influence.score,
            learning_indicators=memory_influence.learning_indicators,
            memory_utilization_effectiveness=memory_influence.effectiveness
        )

class ObservabilityContextIntegration:
    def enrich_with_context(self, observation: Observation, context: Context) -> EnrichedObservation:
        context_factors = {
            "environmental_context": self.analyze_environmental_influence(observation, context),
            "task_context": self.analyze_task_influence(observation, context),
            "social_context": self.analyze_social_influence(observation, context),
            "temporal_context": self.analyze_temporal_influence(observation, context)
        }
        
        return EnrichedObservation(
            observation=observation,
            context_factors=context_factors,
            context_influence_score=self.calculate_context_influence(context_factors),
            contextual_insights=self.generate_contextual_insights(context_factors)
        )

observability_metrics:
  cognitive_metrics:
    - decision_quality_score: "Average: 0.87"
    - reasoning_consistency: "95%"
    - learning_velocity: "15 concepts/day"
    - adaptation_speed: "< 24 hours"
    - problem_solving_efficiency: "2.3x baseline"
    
  behavioral_metrics:
    - pattern_stability: "92%"
    - interaction_quality: "4.6/5.0"
    - collaboration_effectiveness: "89%"
    - communication_clarity: "91%"
    - behavioral_predictability: "87%"
    
  performance_metrics:
    - goal_achievement_rate: "84%"
    - task_completion_accuracy: "96%"
    - response_time_optimization: "67% improvement"
    - resource_utilization_efficiency: "89%"
    - continuous_improvement_rate: "12% monthly"
    
  system_metrics:
    - observability_coverage: "98%"
    - data_collection_accuracy: "99.2%"
    - analysis_latency: "< 5 seconds"
    - insight_generation_speed: "< 30 seconds"
    - dashboard_response_time: "< 2 seconds"

class ObservabilityQA:
    def validate_observation_quality(self, observation: Observation) -> QualityValidation:
        quality_checks = {
            "completeness": self.check_completeness(observation),
            "accuracy": self.check_accuracy(observation),
            "consistency": self.check_consistency(observation),
            "timeliness": self.check_timeliness(observation),
            "relevance": self.check_relevance(observation)
        }
        
        overall_quality = self.calculate_overall_quality(quality_checks)
        
        return QualityValidation(
            observation_id=observation.id,
            quality_checks=quality_checks,
            overall_quality_score=overall_quality,
            quality_issues=self.identify_issues(quality_checks),
            improvement_suggestions=self.suggest_improvements(quality_checks)
        )

# Check observability system health
sindhan-cli observability health --agent-id=<agent_id>
 
# Validate observation data quality
sindhan-cli observability validate --period=24h
 
# Test insight generation
sindhan-cli observability test-insights --agent-id=<agent_id>
 
# Export observability data
sindhan-cli observability export --format=json --period=30d
 
# Performance diagnostics
sindhan-cli observability diagnose --component=analytics