ai_agent_framework/dimensions/metrics_dashboard.py

"""
Metrics Dashboard & Optimization System
====================================

Advanced metrics computation and dashboard system for context engineering,
including real-time metrics, optimization recommendations, and performance monitoring.
"""

import asyncio
import json
import logging
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Set, Tuple, Union, Callable
from dataclasses import dataclass, field, asdict
from enum import Enum
import numpy as np
from collections import defaultdict, deque
import threading
from concurrent.futures import ThreadPoolExecutor
import time
import statistics

from ai_agent_framework.core.context_engineering_agent import (
    ContextElement, ContextModality, ContextDimension, ContextEngineeringAgent
)

logger = logging.getLogger(__name__)


class MetricType(Enum):
    """Types of metrics to compute."""
    CONTEXT_RETENTION_ACCURACY = "context_retention_accuracy"
    CONTEXT_RELEVANCE_PRECISION = "context_relevance_precision"
    CONTEXT_ADAPTATION_SPEED = "context_adaptation_speed"
    CONTEXTUAL_REASONING_QUALITY = "contextual_reasoning_quality"
    USER_SATISFACTION = "user_satisfaction"
    CONTEXT_UTILIZATION_EFFICIENCY = "context_utilization_efficiency"
    PROCESSING_LATENCY = "processing_latency"
    MEMORY_USAGE = "memory_usage"
    ERROR_RATE = "error_rate"
    SYSTEM_THROUGHPUT = "system_throughput"


class OptimizationTarget(Enum):
    """Optimization targets."""
    PERFORMANCE = "performance"
    ACCURACY = "accuracy"
    EFFICIENCY = "efficiency"
    USER_EXPERIENCE = "user_experience"
    RESOURCE_USAGE = "resource_usage"
    ADAPTABILITY = "adaptability"


class AlertLevel(Enum):
    """Alert severity levels."""
    INFO = "info"
    WARNING = "warning"
    ERROR = "error"
    CRITICAL = "critical"


@dataclass
class MetricValue:
    """Individual metric value with metadata."""
    metric_type: MetricType
    value: float
    timestamp: datetime
    confidence: float
    sample_size: int
    metadata: Dict[str, Any]
    
    def __post_init__(self):
        if not self.timestamp:
            self.timestamp = datetime.utcnow()
        if not self.metadata:
            self.metadata = {}


@dataclass
class MetricSeries:
    """Time series of metric values."""
    metric_type: MetricType
    values: List[MetricValue]
    window_size: int
    last_updated: datetime
    aggregation_method: str
    
    def __post_init__(self):
        if not self.values:
            self.values = []
        if not self.last_updated:
            self.last_updated = datetime.utcnow()
        if not self.aggregation_method:
            self.aggregation_method = "mean"


@dataclass
class OptimizationRecommendation:
    """System optimization recommendation."""
    recommendation_id: str
    target: OptimizationTarget
    priority: int
    description: str
    expected_impact: float
    implementation_effort: str
    metrics_affected: List[MetricType]
    created_at: datetime
    
    def __post_init__(self):
        if not self.recommendation_id:
            self.recommendation_id = f"opt_rec_{int(time.time())}"
        if not self.created_at:
            self.created_at = datetime.utcnow()


@dataclass
class Alert:
    """System alert."""
    alert_id: str
    level: AlertLevel
    title: str
    description: str
    affected_metrics: List[MetricType]
    threshold_value: float
    current_value: float
    created_at: datetime
    acknowledged: bool = False
    
    def __post_init__(self):
        if not self.alert_id:
            self.alert_id = f"alert_{int(time.time())}"
        if not self.created_at:
            self.created_at = datetime.utcnow()


class MetricsCollector:
    """Advanced metrics collection and computation engine."""
    
    def __init__(self, max_series_length: int = 1000):
        self.max_series_length = max_series_length
        self.metric_series = {}  # MetricType -> MetricSeries
        self.real_time_metrics = {}  # current metric values
        self.computation_functions = {
            MetricType.CONTEXT_RETENTION_ACCURACY: self._compute_context_retention_accuracy,
            MetricType.CONTEXT_RELEVANCE_PRECISION: self._compute_context_relevance_precision,
            MetricType.CONTEXT_ADAPTATION_SPEED: self._compute_context_adaptation_speed,
            MetricType.CONTEXTUAL_REASONING_QUALITY: self._compute_contextual_reasoning_quality,
            MetricType.USER_SATISFACTION: self._compute_user_satisfaction,
            MetricType.CONTEXT_UTILIZATION_EFFICIENCY: self._compute_context_utilization_efficiency,
            MetricType.PROCESSING_LATENCY: self._compute_processing_latency,
            MetricType.MEMORY_USAGE: self._compute_memory_usage,
            MetricType.ERROR_RATE: self._compute_error_rate,
            MetricType.SYSTEM_THROUGHPUT: self._compute_system_throughput
        }
        
        # Performance tracking
        self.performance_history = deque(maxlen=100)
        self.last_computation_time = {}
        
        # Thread safety
        self._lock = threading.RLock()
        
    async def compute_metric(
        self,
        metric_type: MetricType,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any] = None
    ) -> MetricValue:
        """Compute a specific metric."""
        
        if agent_data is None:
            agent_data = {}
        
        try:
            start_time = time.time()
            
            # Get computation function
            computation_func = self.computation_functions.get(metric_type)
            if not computation_func:
                raise ValueError(f"No computation function for {metric_type}")
            
            # Compute metric
            result = await computation_func(context_data, agent_data)
            
            # Create metric value
            metric_value = MetricValue(
                metric_type=metric_type,
                value=result["value"],
                timestamp=datetime.utcnow(),
                confidence=result.get("confidence", 0.8),
                sample_size=result.get("sample_size", 1),
                metadata=result.get("metadata", {})
            )
            
            # Store in series
            await self._store_metric_value(metric_value)
            
            # Update real-time metrics
            self.real_time_metrics[metric_type] = metric_value
            
            # Record computation time
            computation_time = time.time() - start_time
            self.last_computation_time[metric_type] = computation_time
            
            return metric_value
            
        except Exception as e:
            logger.error(f"Failed to compute metric {metric_type}: {e}")
            # Return default metric value
            return MetricValue(
                metric_type=metric_type,
                value=0.0,
                timestamp=datetime.utcnow(),
                confidence=0.0,
                sample_size=0,
                metadata={"error": str(e)}
            )
    
    async def compute_all_metrics(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any] = None
    ) -> Dict[MetricType, MetricValue]:
        """Compute all available metrics."""
        
        if agent_data is None:
            agent_data = {}
        
        results = {}
        
        # Compute metrics concurrently
        tasks = []
        for metric_type in MetricType:
            task = self.compute_metric(metric_type, context_data, agent_data)
            tasks.append(task)
        
        computed_metrics = await asyncio.gather(*tasks, return_exceptions=True)
        
        for metric_type, result in zip(MetricType, computed_metrics):
            if isinstance(result, Exception):
                logger.error(f"Error computing {metric_type}: {result}")
                continue
            results[metric_type] = result
        
        return results
    
    async def get_metric_series(
        self,
        metric_type: MetricType,
        time_window: Optional[timedelta] = None,
        aggregation: str = "mean"
    ) -> List[MetricValue]:
        """Get metric time series."""
        
        if metric_type not in self.metric_series:
            return []
        
        series = self.metric_series[metric_type]
        
        if time_window:
            cutoff_time = datetime.utcnow() - time_window
            filtered_values = [
                mv for mv in series.values
                if mv.timestamp >= cutoff_time
            ]
            return filtered_values
        
        return series.values
    
    async def get_real_time_metrics(self) -> Dict[MetricType, MetricValue]:
        """Get current real-time metrics."""
        return self.real_time_metrics.copy()
    
    async def get_metric_statistics(
        self,
        metric_type: MetricType,
        time_window: timedelta = timedelta(hours=24)
    ) -> Dict[str, float]:
        """Get statistical summary of metric."""
        
        series = await self.get_metric_series(metric_type, time_window)
        
        if not series:
            return {}
        
        values = [mv.value for mv in series]
        
        return {
            "count": len(values),
            "mean": np.mean(values),
            "median": np.median(values),
            "std": np.std(values),
            "min": np.min(values),
            "max": np.max(values),
            "p25": np.percentile(values, 25),
            "p75": np.percentile(values, 75),
            "p95": np.percentile(values, 95)
        }
    
    # Individual metric computation functions
    
    async def _compute_context_retention_accuracy(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute context retention accuracy."""
        
        # Context retention measures how well important context is preserved
        contexts = context_data.get("contexts", [])
        if not contexts:
            return {"value": 0.0, "confidence": 0.0, "sample_size": 0}
        
        # Simplified retention calculation
        total_contexts = len(contexts)
        retained_contexts = sum(1 for ctx in contexts if ctx.get("retained", True))
        
        accuracy = retained_contexts / max(total_contexts, 1)
        
        return {
            "value": accuracy,
            "confidence": min(1.0, total_contexts / 10),  # Higher confidence with more data
            "sample_size": total_contexts,
            "metadata": {
                "total_contexts": total_contexts,
                "retained_contexts": retained_contexts
            }
        }
    
    async def _compute_context_relevance_precision(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute context relevance precision."""
        
        contexts = context_data.get("contexts", [])
        if not contexts:
            return {"value": 0.0, "confidence": 0.0, "sample_size": 0}
        
        # Calculate precision based on relevance scores
        relevance_scores = [ctx.get("relevance_score", 0) for ctx in contexts]
        precision_scores = [ctx.get("precision_score", 0) for ctx in contexts]
        
        if not precision_scores:
            return {"value": 0.0, "confidence": 0.0, "sample_size": len(contexts)}
        
        # Weighted precision calculation
        total_precision = sum(precision_scores)
        max_possible_precision = len(contexts)
        
        precision = total_precision / max_possible_precision
        
        # Additional relevance factor
        relevance_factor = np.mean(relevance_scores) if relevance_scores else 0
        
        combined_precision = (precision * 0.7) + (relevance_factor * 0.3)
        
        return {
            "value": combined_precision,
            "confidence": min(1.0, len(contexts) / 5),
            "sample_size": len(contexts),
            "metadata": {
                "avg_relevance": relevance_factor,
                "raw_precision": precision
            }
        }
    
    async def _compute_context_adaptation_speed(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute context adaptation speed."""
        
        # Adaptation speed measures how quickly the system adapts to new contexts
        adaptations = context_data.get("adaptations", [])
        if not adaptations:
            return {"value": 0.5, "confidence": 0.0, "sample_size": 0}
        
        # Calculate adaptation times
        adaptation_times = []
        for adaptation in adaptations:
            start_time = adaptation.get("start_time")
            end_time = adaptation.get("end_time")
            if start_time and end_time:
                duration = (end_time - start_time).total_seconds()
                adaptation_times.append(duration)
        
        if not adaptation_times:
            return {"value": 0.5, "confidence": 0.0, "sample_size": len(adaptations)}
        
        # Convert to speed score (lower time = higher speed)
        avg_duration = np.mean(adaptation_times)
        speed_score = 1.0 / (1.0 + avg_duration / 60)  # Normalize by 60 seconds
        
        return {
            "value": speed_score,
            "confidence": min(1.0, len(adaptations) / 5),
            "sample_size": len(adaptations),
            "metadata": {
                "avg_duration_seconds": avg_duration,
                "min_duration": min(adaptation_times),
                "max_duration": max(adaptation_times)
            }
        }
    
    async def _compute_contextual_reasoning_quality(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute contextual reasoning quality."""
        
        # Reasoning quality based on decision outcomes and context usage
        reasoning_decisions = context_data.get("reasoning_decisions", [])
        if not reasoning_decisions:
            return {"value": 0.5, "confidence": 0.0, "sample_size": 0}
        
        # Analyze reasoning quality factors
        successful_reasoning = 0
        context_aware_reasoning = 0
        
        for decision in reasoning_decisions:
            # Success factor
            if decision.get("successful", False):
                successful_reasoning += 1
            
            # Context awareness factor
            if decision.get("context_aware", False):
                context_aware_reasoning += 1
        
        # Calculate quality score
        success_rate = successful_reasoning / len(reasoning_decisions)
        context_awareness_rate = context_aware_reasoning / len(reasoning_decisions)
        
        quality_score = (success_rate * 0.6) + (context_awareness_rate * 0.4)
        
        return {
            "value": quality_score,
            "confidence": min(1.0, len(reasoning_decisions) / 10),
            "sample_size": len(reasoning_decisions),
            "metadata": {
                "success_rate": success_rate,
                "context_awareness_rate": context_awareness_rate
            }
        }
    
    async def _compute_user_satisfaction(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute user satisfaction metrics."""
        
        # User satisfaction based on interaction outcomes
        user_interactions = context_data.get("user_interactions", [])
        if not user_interactions:
            return {"value": 0.5, "confidence": 0.0, "sample_size": 0}
        
        satisfaction_scores = []
        completion_rates = []
        
        for interaction in user_interactions:
            # Explicit satisfaction scores
            if "satisfaction_score" in interaction:
                satisfaction_scores.append(interaction["satisfaction_score"])
            
            # Implicit satisfaction indicators
            completion_rate = interaction.get("completion_rate", 0.5)
            completion_rates.append(completion_rate)
        
        # Calculate overall satisfaction
        all_scores = satisfaction_scores + completion_rates
        
        if not all_scores:
            return {"value": 0.5, "confidence": 0.0, "sample_size": len(user_interactions)}
        
        avg_satisfaction = np.mean(all_scores)
        
        return {
            "value": avg_satisfaction,
            "confidence": min(1.0, len(all_scores) / 10),
            "sample_size": len(all_scores),
            "metadata": {
                "explicit_scores": len(satisfaction_scores),
                "implicit_scores": len(completion_rates),
                "satisfaction_variance": np.var(all_scores) if len(all_scores) > 1 else 0
            }
        }
    
    async def _compute_context_utilization_efficiency(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute context utilization efficiency."""
        
        # Efficiency measures how well available context is utilized
        contexts = context_data.get("contexts", [])
        if not contexts:
            return {"value": 0.0, "confidence": 0.0, "sample_size": 0}
        
        utilization_scores = []
        
        for ctx in contexts:
            # Context usage frequency
            usage_count = ctx.get("usage_count", 0)
            available_count = ctx.get("available_count", 1)
            
            utilization = usage_count / max(available_count, 1)
            utilization_scores.append(min(1.0, utilization))
        
        if not utilization_scores:
            return {"value": 0.0, "confidence": 0.0, "sample_size": len(contexts)}
        
        avg_utilization = np.mean(utilization_scores)
        
        return {
            "value": avg_utilization,
            "confidence": min(1.0, len(contexts) / 8),
            "sample_size": len(contexts),
            "metadata": {
                "total_contexts": len(contexts),
                "avg_utilization": avg_utilization,
                "underutilized_contexts": sum(1 for u in utilization_scores if u < 0.3)
            }
        }
    
    async def _compute_processing_latency(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute processing latency metrics."""
        
        # Processing time analysis
        processing_times = context_data.get("processing_times", [])
        if not processing_times:
            return {"value": 0.5, "confidence": 0.0, "sample_size": 0}
        
        avg_processing_time = np.mean(processing_times)
        
        # Convert to performance score (lower latency = higher score)
        max_acceptable_latency = 1000  # 1 second
        latency_score = max(0.0, 1.0 - (avg_processing_time / max_acceptable_latency))
        
        return {
            "value": latency_score,
            "confidence": min(1.0, len(processing_times) / 20),
            "sample_size": len(processing_times),
            "metadata": {
                "avg_processing_time_ms": avg_processing_time,
                "min_processing_time": min(processing_times),
                "max_processing_time": max(processing_times)
            }
        }
    
    async def _compute_memory_usage(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute memory usage efficiency."""
        
        # Memory usage analysis
        memory_usage = context_data.get("memory_usage", {})
        if not memory_usage:
            return {"value": 0.5, "confidence": 0.0, "sample_size": 0}
        
        current_usage = memory_usage.get("current_mb", 0)
        max_usage = memory_usage.get("max_mb", 1000)
        
        # Usage efficiency (lower usage = higher efficiency)
        usage_ratio = current_usage / max(max_usage, 1)
        efficiency_score = max(0.0, 1.0 - usage_ratio)
        
        return {
            "value": efficiency_score,
            "confidence": 0.8,  # Memory metrics are generally reliable
            "sample_size": 1,
            "metadata": {
                "current_usage_mb": current_usage,
                "max_usage_mb": max_usage,
                "usage_ratio": usage_ratio
            }
        }
    
    async def _compute_error_rate(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute system error rate."""
        
        # Error analysis
        total_operations = context_data.get("total_operations", 0)
        error_count = context_data.get("error_count", 0)
        
        if total_operations == 0:
            return {"value": 1.0, "confidence": 0.0, "sample_size": 0}
        
        error_rate = error_count / total_operations
        reliability_score = max(0.0, 1.0 - error_rate)
        
        return {
            "value": reliability_score,
            "confidence": min(1.0, total_operations / 50),
            "sample_size": total_operations,
            "metadata": {
                "total_operations": total_operations,
                "error_count": error_count,
                "raw_error_rate": error_rate
            }
        }
    
    async def _compute_system_throughput(
        self,
        context_data: Dict[str, Any],
        agent_data: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Compute system throughput."""
        
        # Throughput analysis
        operations_per_minute = context_data.get("operations_per_minute", 0)
        target_throughput = context_data.get("target_throughput", 100)
        
        # Throughput efficiency
        throughput_ratio = operations_per_minute / max(target_throughput, 1)
        efficiency_score = min(1.0, throughput_ratio)
        
        return {
            "value": efficiency_score,
            "confidence": 0.7,
            "sample_size": 1,
            "metadata": {
                "operations_per_minute": operations_per_minute,
                "target_throughput": target_throughput,
                "throughput_ratio": throughput_ratio
            }
        }
    
    # Helper methods
    
    async def _store_metric_value(self, metric_value: MetricValue) -> None:
        """Store metric value in time series."""
        
        with self._lock:
            metric_type = metric_value.metric_type
            
            if metric_type not in self.metric_series:
                self.metric_series[metric_type] = MetricSeries(
                    metric_type=metric_type,
                    values=[],
                    window_size=self.max_series_length,
                    last_updated=datetime.utcnow(),
                    aggregation_method="mean"
                )
            
            series = self.metric_series[metric_type]
            series.values.append(metric_value)
            series.last_updated = datetime.utcnow()
            
            # Maintain window size
            if len(series.values) > self.max_series_length:
                series.values = series.values[-self.max_series_length:]


class OptimizationEngine:
    """Advanced optimization engine for context engineering systems."""
    
    def __init__(self):
        self.optimization_strategies = {
            OptimizationTarget.PERFORMANCE: self._optimize_performance,
            OptimizationTarget.ACCURACY: self._optimize_accuracy,
            OptimizationTarget.EFFICIENCY: self._optimize_efficiency,
            OptimizationTarget.USER_EXPERIENCE: self._optimize_user_experience,
            OptimizationTarget.RESOURCE_USAGE: self._optimize_resource_usage,
            OptimizationTarget.ADAPTABILITY: self._optimize_adaptability
        }
        
        self.current_recommendations = []
        self.optimization_history = deque(maxlen=50)
        
    async def generate_optimization_recommendations(
        self,
        metrics: Dict[MetricType, MetricValue],
        context_data: Dict[str, Any] = None
    ) -> List[OptimizationRecommendation]:
        """Generate optimization recommendations based on current metrics."""
        
        if context_data is None:
            context_data = {}
        
        recommendations = []
        
        # Analyze each metric for optimization opportunities
        for metric_type, metric_value in metrics.items():
            metric_recommendations = await self._analyze_metric_for_optimization(
                metric_type, metric_value, context_data
            )
            recommendations.extend(metric_recommendations)
        
        # Cross-metric analysis
        cross_recommendations = await self._analyze_cross_metric_optimizations(metrics)
        recommendations.extend(cross_recommendations)
        
        # Rank and filter recommendations
        ranked_recommendations = await self._rank_recommendations(recommendations)
        
        self.current_recommendations = ranked_recommendations
        
        return ranked_recommendations
    
    async def apply_optimization(
        self,
        recommendation: OptimizationRecommendation,
        agent_system: Any
    ) -> Dict[str, Any]:
        """Apply an optimization recommendation."""
        
        optimization_target = recommendation.target
        
        if optimization_target in self.optimization_strategies:
            optimization_func = self.optimization_strategies[optimization_target]
            result = await optimization_func(recommendation, agent_system)
            
            # Record optimization attempt
            self.optimization_history.append({
                "recommendation_id": recommendation.recommendation_id,
                "timestamp": datetime.utcnow(),
                "result": result,
                "target": optimization_target.value
            })
            
            return result
        
        return {"success": False, "reason": "unknown_optimization_target"}
    
    async def get_optimization_status(self) -> Dict[str, Any]:
        """Get current optimization status and history."""
        
        recent_optimizations = list(self.optimization_history)[-10:]  # Last 10
        
        return {
            "current_recommendations": len(self.current_recommendations),
            "recent_optimizations": recent_optimizations,
            "total_optimization_attempts": len(self.optimization_history),
            "optimization_success_rate": self._calculate_success_rate()
        }
    
    def _calculate_success_rate(self) -> float:
        """Calculate optimization success rate."""
        
        if not self.optimization_history:
            return 0.0
        
        successful_attempts = sum(
            1 for opt in self.optimization_history
            if opt.get("result", {}).get("success", False)
        )
        
        return successful_attempts / len(self.optimization_history)
    
    # Individual optimization strategies
    
    async def _optimize_performance(
        self,
        recommendation: OptimizationRecommendation,
        agent_system: Any
    ) -> Dict[str, Any]:
        """Optimize for performance."""
        
        # Performance optimization strategies
        strategies = {
            "cache_frequently_used_context": self._optimize_cache_strategy,
            "parallel_processing": self._optimize_parallel_processing,
            "reduce_computation_overhead": self._optimize_computation_overhead,
            "memory_pool_optimization": self._optimize_memory_pools
        }
        
        strategy_func = strategies.get(recommendation.description)
        if strategy_func:
            return await strategy_func(agent_system)
        
        return {"success": False, "reason": "unknown_performance_strategy"}
    
    async def _optimize_accuracy(
        self,
        recommendation: OptimizationRecommendation,
        agent_system: Any
    ) -> Dict[str, Any]:
        """Optimize for accuracy."""
        
        # Accuracy optimization strategies
        strategies = {
            "enhance_context_validation": self._optimize_context_validation,
            "improve_relevance_scoring": self._optimize_relevance_scoring,
            "refine_reasoning_algorithms": self._optimize_reasoning_algorithms
        }
        
        strategy_func = strategies.get(recommendation.description)
        if strategy_func:
            return await strategy_func(agent_system)
        
        return {"success": False, "reason": "unknown_accuracy_strategy"}
    
    async def _optimize_efficiency(
        self,
        recommendation: OptimizationRecommendation,
        agent_system: Any
    ) -> Dict[str, Any]:
        """Optimize for efficiency."""
        
        # Efficiency optimization strategies
        strategies = {
            "context_compression": self._optimize_context_compression,
            "resource_pooling": self._optimize_resource_pooling,
            "batch_processing": self._optimize_batch_processing
        }
        
        strategy_func = strategies.get(recommendation.description)
        if strategy_func:
            return await strategy_func(agent_system)
        
        return {"success": False, "reason": "unknown_efficiency_strategy"}
    
    async def _optimize_user_experience(
        self,
        recommendation: OptimizationRecommendation,
        agent_system: Any
    ) -> Dict[str, Any]:
        """Optimize for user experience."""
        
        # User experience optimization strategies
        strategies = {
            "reduce_response_latency": self._optimize_response_latency,
            "improve_interaction_flow": self._optimize_interaction_flow,
            "enhance_feedback_mechanisms": self._optimize_feedback_mechanisms
        }
        
        strategy_func = strategies.get(recommendation.description)
        if strategy_func:
            return await strategy_func(agent_system)
        
        return {"success": False, "reason": "unknown_ux_strategy"}
    
    async def _optimize_resource_usage(
        self,
        recommendation: OptimizationRecommendation,
        agent_system: Any
    ) -> Dict[str, Any]:
        """Optimize for resource usage."""
        
        # Resource optimization strategies
        strategies = {
            "memory_optimization": self._optimize_memory_usage,
            "cpu_optimization": self._optimize_cpu_usage,
            "storage_optimization": self._optimize_storage_usage
        }
        
        strategy_func = strategies.get(recommendation.description)
        if strategy_func:
            return await strategy_func(agent_system)
        
        return {"success": False, "reason": "unknown_resource_strategy"}
    
    async def _optimize_adaptability(
        self,
        recommendation: OptimizationRecommendation,
        agent_system: Any
    ) -> Dict[str, Any]:
        """Optimize for adaptability."""
        
        # Adaptability optimization strategies
        strategies = {
            "improve_learning_rate": self._optimize_learning_rate,
            "enhance_pattern_recognition": self._optimize_pattern_recognition,
            "adaptive_parameter_tuning": self._optimize_parameter_tuning
        }
        
        strategy_func = strategies.get(recommendation.description)
        if strategy_func:
            return await strategy_func(agent_system)
        
        return {"success": False, "reason": "unknown_adaptability_strategy"}
    
    # Analysis methods
    
    async def _analyze_metric_for_optimization(
        self,
        metric_type: MetricType,
        metric_value: MetricValue,
        context_data: Dict[str, Any]
    ) -> List[OptimizationRecommendation]:
        """Analyze individual metric for optimization opportunities."""
        
        recommendations = []
        
        # Thresholds for optimization
        thresholds = {
            MetricType.CONTEXT_RETENTION_ACCURACY: {"poor": 0.6, "good": 0.8},
            MetricType.CONTEXT_RELEVANCE_PRECISION: {"poor": 0.7, "good": 0.9},
            MetricType.CONTEXT_ADAPTATION_SPEED: {"poor": 0.5, "good": 0.8},
            MetricType.CONTEXTUAL_REASONING_QUALITY: {"poor": 0.6, "good": 0.85},
            MetricType.USER_SATISFACTION: {"poor": 0.7, "good": 0.9},
            MetricType.PROCESSING_LATENCY: {"poor": 0.4, "good": 0.7},
            MetricType.ERROR_RATE: {"poor": 0.8, "good": 0.95}
        }
        
        threshold_data = thresholds.get(metric_type)
        if not threshold_data:
            return recommendations
        
        # Check if optimization is needed
        if metric_value.value < threshold_data["poor"]:
            # Generate optimization recommendation
            recommendation = await self._generate_metric_recommendation(
                metric_type, metric_value, threshold_data
            )
            if recommendation:
                recommendations.append(recommendation)
        
        return recommendations
    
    async def _generate_metric_recommendation(
        self,
        metric_type: MetricType,
        metric_value: MetricValue,
        threshold_data: Dict[str, float]
    ) -> Optional[OptimizationRecommendation]:
        """Generate optimization recommendation for a metric."""
        
        # Map metrics to optimization targets and strategies
        metric_mappings = {
            MetricType.CONTEXT_RETENTION_ACCURACY: {
                "target": OptimizationTarget.ACCURACY,
                "description": "enhance_context_validation",
                "priority": 8
            },
            MetricType.CONTEXT_RELEVANCE_PRECISION: {
                "target": OptimizationTarget.ACCURACY,
                "description": "improve_relevance_scoring",
                "priority": 7
            },
            MetricType.PROCESSING_LATENCY: {
                "target": OptimizationTarget.PERFORMANCE,
                "description": "cache_frequently_used_context",
                "priority": 9
            },
            MetricType.USER_SATISFACTION: {
                "target": OptimizationTarget.USER_EXPERIENCE,
                "description": "reduce_response_latency",
                "priority": 10
            },
            MetricType.ERROR_RATE: {
                "target": OptimizationTarget.ACCURACY,
                "description": "enhance_error_handling",
                "priority": 6
            }
        }
        
        mapping = metric_mappings.get(metric_type)
        if not mapping:
            return None
        
        # Calculate expected impact
        impact = max(0.1, threshold_data["good"] - metric_value.value)
        
        return OptimizationRecommendation(
            recommendation_id=f"opt_{metric_type.value}_{int(time.time())}",
            target=mapping["target"],
            priority=mapping["priority"],
            description=mapping["description"],
            expected_impact=impact,
            implementation_effort="medium",
            metrics_affected=[metric_type],
            created_at=datetime.utcnow()
        )
    
    async def _analyze_cross_metric_optimizations(
        self,
        metrics: Dict[MetricType, MetricValue]
    ) -> List[OptimizationRecommendation]:
        """Analyze cross-metric optimization opportunities."""
        
        recommendations = []
        
        # Performance-Accuracy trade-offs
        latency = metrics.get(MetricType.PROCESSING_LATUS)
        accuracy = metrics.get(MetricType.CONTEXT_RETENTION_ACCURACY)
        
        if latency and accuracy:
            if latency.value < 0.5 and accuracy.value > 0.8:
                # Good accuracy but poor performance - optimize for performance
                recommendation = OptimizationRecommendation(
                    recommendation_id=f"perf_acc_opt_{int(time.time())}",
                    target=OptimizationTarget.PERFORMANCE,
                    priority=6,
                    description="parallel_processing",
                    expected_impact=0.3,
                    implementation_effort="high",
                    metrics_affected=[MetricType.PROCESSING_LATENCY],
                    created_at=datetime.utcnow()
                )
                recommendations.append(recommendation)
        
        # Efficiency-User Experience balance
        utilization = metrics.get(MetricType.CONTEXT_UTILIZATION_EFFICIENCY)
        satisfaction = metrics.get(MetricType.USER_SATISFACTION)
        
        if utilization and satisfaction:
            if utilization.value > 0.9 and satisfaction.value < 0.7:
                # High efficiency but low satisfaction - improve UX
                recommendation = OptimizationRecommendation(
                    recommendation_id=f"eff_ux_opt_{int(time.time())}",
                    target=OptimizationTarget.USER_EXPERIENCE,
                    priority=7,
                    description="improve_interaction_flow",
                    expected_impact=0.4,
                    implementation_effort="medium",
                    metrics_affected=[MetricType.USER_SATISFACTION],
                    created_at=datetime.utcnow()
                )
                recommendations.append(recommendation)
        
        return recommendations
    
    async def _rank_recommendations(
        self,
        recommendations: List[OptimizationRecommendation]
    ) -> List[OptimizationRecommendation]:
        """Rank recommendations by priority and impact."""
        
        # Score each recommendation
        scored_recommendations = []
        for rec in recommendations:
            # Calculate composite score
            priority_score = rec.priority / 10.0
            impact_score = rec.expected_impact
            effort_penalty = {"low": 0.0, "medium": -0.1, "high": -0.2}.get(
                rec.implementation_effort, -0.1
            )
            
            composite_score = priority_score * 0.4 + impact_score * 0.4 + effort_penalty
            scored_recommendations.append((composite_score, rec))
        
        # Sort by score (highest first)
        scored_recommendations.sort(key=lambda x: x[0], reverse=True)
        
        return [rec for _, rec in scored_recommendations]
    
    # Placeholder optimization implementations
    
    async def _optimize_cache_strategy(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize caching strategy."""
        return {"success": True, "improvement": "cache_hit_rate_increased"}
    
    async def _optimize_parallel_processing(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize parallel processing."""
        return {"success": True, "improvement": "processing_speed_increased"}
    
    async def _optimize_computation_overhead(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize computation overhead."""
        return {"success": True, "improvement": "computation_overhead_reduced"}
    
    async def _optimize_memory_pools(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize memory pools."""
        return {"success": True, "improvement": "memory_efficiency_improved"}
    
    async def _optimize_context_validation(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize context validation."""
        return {"success": True, "improvement": "validation_accuracy_increased"}
    
    async def _optimize_relevance_scoring(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize relevance scoring."""
        return {"success": True, "improvement": "relevance_precision_increased"}
    
    async def _optimize_reasoning_algorithms(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize reasoning algorithms."""
        return {"success": True, "improvement": "reasoning_quality_increased"}
    
    async def _optimize_context_compression(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize context compression."""
        return {"success": True, "improvement": "compression_efficiency_increased"}
    
    async def _optimize_resource_pooling(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize resource pooling."""
        return {"success": True, "improvement": "resource_utilization_improved"}
    
    async def _optimize_batch_processing(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize batch processing."""
        return {"success": True, "improvement": "batch_efficiency_increased"}
    
    async def _optimize_response_latency(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize response latency."""
        return {"success": True, "improvement": "response_time_reduced"}
    
    async def _optimize_interaction_flow(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize interaction flow."""
        return {"success": True, "improvement": "interaction_ux_improved"}
    
    async def _optimize_feedback_mechanisms(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize feedback mechanisms."""
        return {"success": True, "improvement": "feedback_quality_increased"}
    
    async def _optimize_memory_usage(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize memory usage."""
        return {"success": True, "improvement": "memory_usage_optimized"}
    
    async def _optimize_cpu_usage(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize CPU usage."""
        return {"success": True, "improvement": "cpu_efficiency_improved"}
    
    async def _optimize_storage_usage(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize storage usage."""
        return {"success": True, "improvement": "storage_efficiency_increased"}
    
    async def _optimize_learning_rate(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize learning rate."""
        return {"success": True, "improvement": "learning_speed_increased"}
    
    async def _optimize_pattern_recognition(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize pattern recognition."""
        return {"success": True, "improvement": "pattern_recognition_improved"}
    
    async def _optimize_parameter_tuning(self, agent_system: Any) -> Dict[str, Any]:
        """Optimize parameter tuning."""
        return {"success": True, "improvement": "parameter_optimization_increased"}


class MetricsDashboard:
    """Comprehensive metrics dashboard and monitoring system."""
    
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.optimization_engine = OptimizationEngine()
        self.alerts = {}  # Alert ID -> Alert
        self.dashboard_config = {
            "refresh_interval": 30,  # seconds
            "alert_thresholds": {
                MetricType.ERROR_RATE: {"warning": 0.1, "critical": 0.2},
                MetricType.PROCESSING_LATENCY: {"warning": 0.3, "critical": 0.2},
                MetricType.USER_SATISFACTION: {"warning": 0.6, "critical": 0.4}
            },
            "display_preferences": {
                "show_real_time_metrics": True,
                "show_historical_trends": True,
                "show_optimization_recommendations": True
            }
        }
        
        self._lock = threading.RLock()
    
    async def get_dashboard_data(
        self,
        include_recommendations: bool = True,
        include_alerts: bool = True,
        time_window: timedelta = timedelta(hours=24)
    ) -> Dict[str, Any]:
        """Get comprehensive dashboard data."""
        
        # Get real-time metrics
        real_time_metrics = await self.metrics_collector.get_real_time_metrics()
        
        # Get metric statistics
        metric_statistics = {}
        for metric_type in MetricType:
            stats = await self.metrics_collector.get_metric_statistics(metric_type, time_window)
            if stats:
                metric_statistics[metric_type.value] = stats
        
        # Generate optimization recommendations
        recommendations = []
        if include_recommendations:
            recommendations = await self.optimization_engine.generate_optimization_recommendations(
                real_time_metrics
            )
        
        # Check for alerts
        current_alerts = []
        if include_alerts:
            current_alerts = await self._check_metric_alerts(real_time_metrics)
        
        return {
            "timestamp": datetime.utcnow().isoformat(),
            "real_time_metrics": {mt.value: mv.value for mt, mv in real_time_metrics.items()},
            "metric_statistics": metric_statistics,
            "optimization_recommendations": [asdict(rec) for rec in recommendations],
            "current_alerts": [asdict(alert) for alert in current_alerts],
            "dashboard_status": "healthy" if len(current_alerts) == 0 else "degraded",
            "total_metrics_tracked": len(real_time_metrics),
            "optimization_status": self.optimization_engine.get_optimization_status()
        }
    
    async def get_metric_trend(
        self,
        metric_type: MetricType,
        time_window: timedelta = timedelta(hours=24),
        aggregation: str = "hourly"
    ) -> Dict[str, Any]:
        """Get metric trend data."""
        
        series = await self.metrics_collector.get_metric_series(metric_type, time_window)
        
        if not series:
            return {"metric_type": metric_type.value, "data": [], "trend": "insufficient_data"}
        
        # Aggregate data based on requested aggregation
        aggregated_data = await self._aggregate_metric_data(series, aggregation)
        
        # Calculate trend
        trend = await self._calculate_trend(series)
        
        return {
            "metric_type": metric_type.value,
            "aggregation": aggregation,
            "data": aggregated_data,
            "trend": trend,
            "data_points": len(series),
            "time_window_hours": time_window.total_seconds() / 3600
        }
    
    async def acknowledge_alert(self, alert_id: str) -> Dict[str, Any]:
        """Acknowledge an alert."""
        
        if alert_id in self.alerts:
            self.alerts[alert_id].acknowledged = True
            
            return {
                "success": True,
                "alert_id": alert_id,
                "acknowledged_at": datetime.utcnow().isoformat()
            }
        else:
            return {
                "success": False,
                "reason": "alert_not_found"
            }
    
    async def trigger_optimization(
        self,
        recommendation_id: str,
        agent_system: Any = None
    ) -> Dict[str, Any]:
        """Trigger an optimization recommendation."""
        
        # Find the recommendation
        recommendation = None
        for rec in self.optimization_engine.current_recommendations:
            if rec.recommendation_id == recommendation_id:
                recommendation = rec
                break
        
        if not recommendation:
            return {
                "success": False,
                "reason": "recommendation_not_found"
            }
        
        # Apply optimization
        result = await self.optimization_engine.apply_optimization(recommendation, agent_system)
        
        return {
            "success": result.get("success", False),
            "recommendation_id": recommendation_id,
            "optimization_result": result,
            "applied_at": datetime.utcnow().isoformat()
        }
    
    async def _check_metric_alerts(
        self,
        real_time_metrics: Dict[MetricType, MetricValue]
    ) -> List[Alert]:
        """Check metrics against alert thresholds."""
        
        alerts = []
        thresholds = self.dashboard_config["alert_thresholds"]
        
        for metric_type, metric_value in real_time_metrics.items():
            metric_thresholds = thresholds.get(metric_type)
            if not metric_thresholds:
                continue
            
            current_value = metric_value.value
            
            # Check warning threshold
            if current_value < metric_thresholds.get("warning", 0):
                alert_level = AlertLevel.WARNING
                title = f"Warning: {metric_type.value} below threshold"
                description = f"Metric {metric_type.value} is {current_value:.3f}, below warning threshold {metric_thresholds['warning']}"
                
                alert = Alert(
                    alert_id=f"alert_{metric_type.value}_{int(time.time())}",
                    level=alert_level,
                    title=title,
                    description=description,
                    affected_metrics=[metric_type],
                    threshold_value=metric_thresholds["warning"],
                    current_value=current_value
                )
                alerts.append(alert)
            
            # Check critical threshold
            if current_value < metric_thresholds.get("critical", 0):
                alert_level = AlertLevel.CRITICAL
                title = f"Critical: {metric_type.value} severely below threshold"
                description = f"Metric {metric_type.value} is {current_value:.3f}, below critical threshold {metric_thresholds['critical']}"
                
                alert = Alert(
                    alert_id=f"alert_critical_{metric_type.value}_{int(time.time())}",
                    level=alert_level,
                    title=title,
                    description=description,
                    affected_metrics=[metric_type],
                    threshold_value=metric_thresholds["critical"],
                    current_value=current_value
                )
                alerts.append(alert)
        
        # Store alerts
        for alert in alerts:
            self.alerts[alert.alert_id] = alert
        
        return [alert for alert in alerts if not alert.acknowledged]
    
    async def _aggregate_metric_data(
        self,
        series: List[MetricValue],
        aggregation: str
    ) -> List[Dict[str, Any]]:
        """Aggregate metric data based on time period."""
        
        if aggregation == "hourly":
            # Group by hour
            hourly_groups = defaultdict(list)
            for mv in series:
                hour_key = mv.timestamp.replace(minute=0, second=0, microsecond=0)
                hourly_groups[hour_key].append(mv.value)
            
            aggregated = []
            for hour, values in sorted(hour_groups.items()):
                aggregated.append({
                    "timestamp": hour.isoformat(),
                    "value": np.mean(values),
                    "count": len(values)
                })
            
            return aggregated
        
        elif aggregation == "daily":
            # Group by day
            daily_groups = defaultdict(list)
            for mv in series:
                day_key = mv.timestamp.replace(hour=0, minute=0, second=0, microsecond=0)
                daily_groups[day_key].append(mv.value)
            
            aggregated = []
            for day, values in sorted(daily_groups.items()):
                aggregated.append({
                    "timestamp": day.isoformat(),
                    "value": np.mean(values),
                    "count": len(values)
                })
            
            return aggregated
        
        else:
            # Return raw data points
            return [
                {
                    "timestamp": mv.timestamp.isoformat(),
                    "value": mv.value,
                    "confidence": mv.confidence
                }
                for mv in series
            ]
    
    async def _calculate_trend(self, series: List[MetricValue]) -> str:
        """Calculate trend direction for metric series."""
        
        if len(series) < 3:
            return "insufficient_data"
        
        # Simple trend calculation using first and last thirds
        first_third = series[:len(series)//3]
        last_third = series[-len(series)//3:]
        
        first_avg = np.mean([mv.value for mv in first_third])
        last_avg = np.mean([mv.value for mv in last_third])
        
        difference = last_avg - first_avg
        relative_change = abs(difference) / max(first_avg, 0.1)
        
        if relative_change < 0.05:  # Less than 5% change
            return "stable"
        elif difference > 0:
            return "improving"
        else:
            return "declining"
    
    # Dashboard configuration methods
    
    async def update_alert_threshold(
        self,
        metric_type: MetricType,
        warning_threshold: float,
        critical_threshold: float
    ) -> Dict[str, Any]:
        """Update alert thresholds for a metric."""
        
        with self._lock:
            self.dashboard_config["alert_thresholds"][metric_type] = {
                "warning": warning_threshold,
                "critical": critical_threshold
            }
            
            return {
                "success": True,
                "metric_type": metric_type.value,
                "new_thresholds": {
                    "warning": warning_threshold,
                    "critical": critical_threshold
                }
            }
    
    async def update_display_preferences(
        self,
        preferences: Dict[str, bool]
    ) -> Dict[str, Any]:
        """Update dashboard display preferences."""
        
        with self._lock:
            self.dashboard_config["display_preferences"].update(preferences)
            
            return {
                "success": True,
                "new_preferences": self.dashboard_config["display_preferences"]
            }


if __name__ == "__main__":
    print("Metrics Dashboard & Optimization System Initialized")
    print("=" * 60)
    dashboard = MetricsDashboard()
    print("Ready for comprehensive metrics monitoring and optimization!")