#!/usr/bin/env nu

# AI Agents for Observability and Infrastructure Intelligence
# Smart agents that analyze, predict, and optimize infrastructure

use collectors.nu *
use ../dataframes/polars_integration.nu *
use ../lib_provisioning/ai/lib.nu *

# Agent types and their capabilities
export def get_agent_types []: nothing -> record {
    {
        pattern_detector: {
            description: "Detects anomalies and patterns in infrastructure data"
            capabilities: ["anomaly_detection", "trend_analysis", "pattern_recognition"]
            data_sources: ["metrics", "logs", "events"]
            frequency: "real_time"
        }
        cost_optimizer: {
            description: "Analyzes costs and provides optimization recommendations"
            capabilities: ["cost_analysis", "rightsizing", "scheduling_optimization"]
            data_sources: ["cost_metrics", "resource_usage", "deployment_patterns"]
            frequency: "daily"
        }
        performance_analyzer: {
            description: "Monitors and optimizes infrastructure performance"
            capabilities: ["bottleneck_detection", "capacity_planning", "performance_tuning"]
            data_sources: ["performance_metrics", "resource_metrics", "application_logs"]
            frequency: "continuous"
        }
        security_monitor: {
            description: "Monitors security events and vulnerabilities"
            capabilities: ["threat_detection", "vulnerability_assessment", "compliance_monitoring"]
            data_sources: ["security_events", "access_logs", "configuration_state"]
            frequency: "real_time"
        }
        predictor: {
            description: "Predicts infrastructure failures and capacity needs"
            capabilities: ["failure_prediction", "capacity_forecasting", "maintenance_scheduling"]
            data_sources: ["historical_metrics", "error_logs", "deployment_history"]
            frequency: "hourly"
        }
        auto_healer: {
            description: "Automatically responds to and fixes infrastructure issues"
            capabilities: ["auto_remediation", "failover", "scaling_actions"]
            data_sources: ["alerts", "health_checks", "performance_metrics"]
            frequency: "real_time"
        }
    }
}

# Start AI agents
export def start_agents [
    --config_file: string = "agents.toml"
    --data_dir: string = "data/observability"
    --agents: list<string> = []
    --debug = false
]: nothing -> nothing {

    print "🤖 Starting AI Observability Agents..."

    # Load configuration
    let config = load_agent_config $config_file

    # Select agents to start
    let selected_agents = if ($agents | is-empty) {
        $config.agents | transpose name settings | where {|agent| $agent.settings.enabled} | get name
    } else {
        $agents
    }

    print $"🚀 Starting agents: ($selected_agents | str join ', ')"

    # Initialize agents
    let active_agents = ($selected_agents | each {|agent_name|
        initialize_agent $agent_name $config $data_dir $debug
    })

    # Start agent processing loops
    start_agent_loops $active_agents $debug
}

def load_agent_config [config_file: string]: string -> record {
    if ($config_file | path exists) {
        open $config_file
    } else {
        # Default agent configuration
        {
            agents: {
                pattern_detector: {
                    enabled: true
                    interval: "60s"
                    sensitivity: 0.8
                    lookback_hours: 24
                    alert_threshold: 0.9
                }
                cost_optimizer: {
                    enabled: true
                    interval: "3600s"  # 1 hour
                    optimization_target: 0.3  # 30% cost reduction target
                    min_savings_threshold: 10  # $10 minimum savings
                }
                performance_analyzer: {
                    enabled: true
                    interval: "300s"   # 5 minutes
                    performance_thresholds: {
                        cpu: 80
                        memory: 85
                        disk: 90
                        response_time: 500  # ms
                    }
                }
                security_monitor: {
                    enabled: true
                    interval: "30s"
                    threat_levels: ["medium", "high", "critical"]
                    auto_response: false
                }
                predictor: {
                    enabled: true
                    interval: "1800s"  # 30 minutes
                    prediction_horizon: "7d"
                    confidence_threshold: 0.75
                }
                auto_healer: {
                    enabled: false  # Disabled by default for safety
                    interval: "60s"
                    auto_actions: ["restart_service", "scale_up", "failover"]
                    max_actions_per_hour: 5
                }
            }
            ai: {
                model: "local"  # local, openai, anthropic
                temperature: 0.3
                max_tokens: 1000
            }
            notifications: {
                enabled: true
                channels: ["console", "webhook"]
                webhook_url: ""
            }
        }
    }
}

def initialize_agent [
    agent_name: string
    config: record
    data_dir: string
    debug: bool
]: nothing -> record {

    print $"🔧 Initializing agent: ($agent_name)"

    let agent_config = $config.agents | get $agent_name
    let agent_types = get_agent_types

    {
        name: $agent_name
        type: ($agent_types | get $agent_name)
        config: $agent_config
        data_dir: $data_dir
        debug: $debug
        state: {
            last_run: null
            total_runs: 0
            last_findings: []
            performance_stats: {
                avg_runtime: 0
                total_runtime: 0
                success_rate: 1.0
            }
        }
    }
}

def start_agent_loops [agents: list, debug: bool]: nothing -> nothing {
    print $"🔄 Starting ($agents | length) agent processing loops..."

    # Start each agent in its own processing loop
    $agents | each {|agent|
        run_agent_loop $agent $debug
    } | ignore

    # Keep the main process running
    while true {
        sleep 60sec
    }
}

def run_agent_loop [agent: record, debug: bool]: nothing -> nothing {
    let interval_seconds = parse_interval $agent.config.interval

    if $debug {
        print $"🤖 Agent ($agent.name) loop started (interval: ($agent.config.interval))"
    }

    while true {
        do {
            let start_time = (date now)

            # Execute agent logic
            let results = execute_agent $agent

            # Update agent state
            let runtime = ((date now) - $start_time)
            update_agent_performance $agent $runtime $results

            if $debug and ($results | length) > 0 {
                print $"🔍 Agent ($agent.name) found ($results | length) insights"
            }

            # Process results
            process_agent_results $agent $results

        } | complete | if ($in.exit_code != 0) {
            print $"❌ Error in agent ($agent.name): ($in.stderr)"
        }

        sleep ($interval_seconds * 1sec)
    }
}

def execute_agent [agent: record]: nothing -> list {
    match $agent.name {
        "pattern_detector" => (execute_pattern_detector $agent)
        "cost_optimizer" => (execute_cost_optimizer $agent)
        "performance_analyzer" => (execute_performance_analyzer $agent)
        "security_monitor" => (execute_security_monitor $agent)
        "predictor" => (execute_predictor $agent)
        "auto_healer" => (execute_auto_healer $agent)
        _ => {
            print $"⚠️  Unknown agent type: ($agent.name)"
            []
        }
    }
}

# Pattern Detection Agent
def execute_pattern_detector [agent: record]: nothing -> list {
    # Load recent observability data
    let recent_data = query_observability_data --time_range "1h" --data_dir $agent.data_dir

    if ($recent_data | length) == 0 {
        return []
    }

    mut findings = []

    # Detect anomalies in metrics
    let metric_anomalies = detect_metric_anomalies $recent_data $agent.config.sensitivity

    if ($metric_anomalies | length) > 0 {
        $findings = ($findings | append {
            type: "anomaly"
            category: "metrics"
            severity: "medium"
            findings: $metric_anomalies
            agent: $agent.name
            timestamp: (date now)
        })
    }

    # Detect log patterns
    let log_patterns = detect_log_patterns $recent_data

    if ($log_patterns | length) > 0 {
        $findings = ($findings | append {
            type: "pattern"
            category: "logs"
            severity: "info"
            findings: $log_patterns
            agent: $agent.name
            timestamp: (date now)
        })
    }

    $findings
}

def detect_metric_anomalies [data: any, sensitivity: float]: nothing -> list {
    # Simple anomaly detection based on statistical analysis
    # In production, this would use more sophisticated ML algorithms

    let metrics = ($data | where collector == "system_metrics")

    if ($metrics | length) < 10 {
        return []  # Need sufficient data points
    }

    mut anomalies = []

    # Check CPU usage anomalies
    let cpu_metrics = ($metrics | where metric_name == "cpu" | get value)
    let cpu_mean = ($cpu_metrics | math avg)
    let cpu_std = ($cpu_metrics | math stddev)
    let cpu_threshold = $cpu_mean + (2 * $cpu_std * $sensitivity)

    let cpu_anomalies = ($metrics | where metric_name == "cpu" and value > $cpu_threshold)
    if ($cpu_anomalies | length) > 0 {
        $anomalies = ($anomalies | append {
            metric: "cpu"
            type: "high_usage"
            threshold: $cpu_threshold
            current_value: ($cpu_anomalies | get value | math max)
            severity: (if ($cpu_anomalies | get value | math max) > 90 { "high" } else { "medium" })
        })
    }

    # Check memory usage anomalies
    let memory_metrics = ($metrics | where metric_name == "memory" | get value)
    if ($memory_metrics | length) > 0 {
        let mem_mean = ($memory_metrics | math avg)
        let mem_std = ($memory_metrics | math stddev)
        let mem_threshold = $mem_mean + (2 * $mem_std * $sensitivity)

        let mem_anomalies = ($metrics | where metric_name == "memory" and value > $mem_threshold)
        if ($mem_anomalies | length) > 0 {
            $anomalies = ($anomalies | append {
                metric: "memory"
                type: "high_usage"
                threshold: $mem_threshold
                current_value: ($mem_anomalies | get value | math max)
                severity: (if ($mem_anomalies | get value | math max) > 95 { "high" } else { "medium" })
            })
        }
    }

    $anomalies
}

def detect_log_patterns [data: any]: any -> list {
    let log_data = ($data | where collector == "application_logs")

    if ($log_data | length) == 0 {
        return []
    }

    mut patterns = []

    # Detect error rate spikes
    let error_logs = ($log_data | where level in ["error", "fatal"])
    let total_logs = ($log_data | length)
    let error_rate = if $total_logs > 0 { ($error_logs | length) / $total_logs } else { 0 }

    if $error_rate > 0.05 {  # 5% error rate threshold
        $patterns = ($patterns | append {
            pattern: "high_error_rate"
            value: $error_rate
            threshold: 0.05
            severity: (if $error_rate > 0.10 { "high" } else { "medium" })
        })
    }

    # Detect repeated error messages
    let error_messages = ($error_logs | group-by message | transpose message count | where count > 3)
    if ($error_messages | length) > 0 {
        $patterns = ($patterns | append {
            pattern: "repeated_errors"
            messages: ($error_messages | get message)
            severity: "medium"
        })
    }

    $patterns
}

# Cost Optimization Agent
def execute_cost_optimizer [agent: record]: nothing -> list {
    let cost_data = query_observability_data --collector "cost_metrics" --time_range "24h" --data_dir $agent.data_dir

    if ($cost_data | length) == 0 {
        return []
    }

    # Analyze resource utilization vs cost
    let utilization_analysis = analyze_resource_utilization $cost_data
    let utilization_optimizations = ($utilization_analysis | each {|analysis|
        if $analysis.potential_savings > $agent.config.min_savings_threshold {
            {
                type: "rightsizing"
                resource: $analysis.resource
                current_cost: $analysis.current_cost
                potential_savings: $analysis.potential_savings
                recommendation: $analysis.recommendation
                confidence: $analysis.confidence
            }
        }
    } | compact)

    # Identify unused resources
    let unused_resources = identify_unused_resources $cost_data
    let unused_optimizations = ($unused_resources | each {|resource|
        {
            type: "unused_resource"
            resource: $resource.name
            cost: $resource.cost
            recommendation: "Consider terminating or downsizing"
            confidence: 0.9
        }
    })

    let optimizations = ($utilization_optimizations | append $unused_optimizations)

    $optimizations | each {|opt|
        $opt | upsert agent $agent.name | upsert timestamp (date now)
    }
}

def analyze_resource_utilization [cost_data: any]: any -> list {
    # Mock analysis - in production would use real utilization data
    [
        {
            resource: "ec2-i-12345"
            current_cost: 120.0
            utilization: 0.25
            potential_savings: 60.0
            recommendation: "Downsize from m5.xlarge to m5.large"
            confidence: 0.85
        }
    ]
}

def identify_unused_resources [cost_data: any]: any -> list {
    # Mock analysis for unused resources
    [
        {
            name: "unused-volume-123"
            cost: 15.0
            type: "ebs_volume"
            last_access: "30d"
        }
    ]
}

# Performance Analysis Agent
def execute_performance_analyzer [agent: record]: nothing -> list {
    let perf_data = query_observability_data --collector "performance_metrics" --time_range "1h" --data_dir $agent.data_dir

    if ($perf_data | length) == 0 {
        return []
    }

    mut performance_issues = []

    # Check against performance thresholds
    let thresholds = $agent.config.performance_thresholds

    # CPU performance analysis
    let cpu_issues = ($perf_data | where metric_name == "cpu" and value > $thresholds.cpu)
    if ($cpu_issues | length) > 0 {
        $performance_issues = ($performance_issues | append {
            type: "cpu_bottleneck"
            severity: "high"
            affected_resources: ($cpu_issues | get resource_id | uniq)
            max_value: ($cpu_issues | get value | math max)
            threshold: $thresholds.cpu
        })
    }

    # Memory performance analysis
    let memory_issues = ($perf_data | where metric_name == "memory" and value > $thresholds.memory)
    if ($memory_issues | length) > 0 {
        $performance_issues = ($performance_issues | append {
            type: "memory_bottleneck"
            severity: "high"
            affected_resources: ($memory_issues | get resource_id | uniq)
            max_value: ($memory_issues | get value | math max)
            threshold: $thresholds.memory
        })
    }

    $performance_issues | each {|issue|
        $issue | upsert agent $agent.name | upsert timestamp (date now)
    }
}

# Security Monitor Agent
def execute_security_monitor [agent: record]: nothing -> list {
    let security_data = query_observability_data --collector "security_events" --time_range "5m" --data_dir $agent.data_dir

    if ($security_data | length) == 0 {
        return []
    }

    mut security_alerts = []

    # Analyze authentication failures
    let auth_failures = ($security_data | where event_type == "auth_failure")
    if ($auth_failures | length) > 5 {  # More than 5 failures in 5 minutes
        $security_alerts = ($security_alerts | append {
            type: "brute_force_attempt"
            severity: "high"
            event_count: ($auth_failures | length)
            timeframe: "5m"
            recommendation: "Consider blocking source IPs"
        })
    }

    # Check for privilege escalation attempts
    let escalation_events = ($security_data | where event_type == "privilege_escalation")
    if ($escalation_events | length) > 0 {
        $security_alerts = ($security_alerts | append {
            type: "privilege_escalation"
            severity: "critical"
            event_count: ($escalation_events | length)
            recommendation: "Immediate investigation required"
        })
    }

    $security_alerts | each {|alert|
        $alert | upsert agent $agent.name | upsert timestamp (date now)
    }
}

# Predictor Agent
def execute_predictor [agent: record]: nothing -> list {
    let historical_data = query_observability_data --time_range $"($agent.config.prediction_horizon)" --data_dir $agent.data_dir

    if ($historical_data | length) < 100 {
        return []  # Need sufficient historical data
    }

    mut predictions = []

    # Predict capacity needs
    let capacity_prediction = predict_capacity_needs $historical_data $agent.config

    if $capacity_prediction.confidence > $agent.config.confidence_threshold {
        $predictions = ($predictions | append {
            type: "capacity_forecast"
            forecast_horizon: $agent.config.prediction_horizon
            prediction: $capacity_prediction.prediction
            confidence: $capacity_prediction.confidence
            recommendation: $capacity_prediction.recommendation
        })
    }

    # Predict potential failures
    let failure_prediction = predict_failures $historical_data $agent.config

    if $failure_prediction.risk_score > 0.8 {
        $predictions = ($predictions | append {
            type: "failure_prediction"
            risk_score: $failure_prediction.risk_score
            predicted_failure_time: $failure_prediction.estimated_time
            affected_components: $failure_prediction.components
            recommendation: $failure_prediction.recommendation
        })
    }

    $predictions | each {|pred|
        $pred | upsert agent $agent.name | upsert timestamp (date now)
    }
}

def predict_capacity_needs [data: any, config: record]: nothing -> record {
    # Simple trend-based prediction
    # In production, would use time series forecasting models

    let cpu_trend = analyze_metric_trend $data "cpu"
    let memory_trend = analyze_metric_trend $data "memory"

    {
        prediction: {
            cpu_growth_rate: $cpu_trend.growth_rate
            memory_growth_rate: $memory_trend.growth_rate
            estimated_capacity_date: ((date now) + 30day)
        }
        confidence: 0.75
        recommendation: (if $cpu_trend.growth_rate > 0.1 { "Consider adding CPU capacity" } else { "Current capacity sufficient" })
    }
}

def analyze_metric_trend [data: any, metric: string]: nothing -> record {
    let metric_data = ($data | where metric_name == $metric | sort-by timestamp)

    if ($metric_data | length) < 10 {
        return { growth_rate: 0, trend: "insufficient_data" }
    }

    # Simple linear trend analysis
    let first_half = ($metric_data | first (($metric_data | length) // 2) | get value | math avg)
    let second_half = ($metric_data | last (($metric_data | length) // 2) | get value | math avg)

    let growth_rate = ($second_half - $first_half) / $first_half

    {
        growth_rate: $growth_rate
        trend: (if $growth_rate > 0.05 { "increasing" } else if $growth_rate < -0.05 { "decreasing" } else { "stable" })
    }
}

def predict_failures [data: any, config: record]: nothing -> record {
    # Analyze patterns that typically precede failures
    let error_rate = calculate_error_rate $data
    let resource_stress = calculate_resource_stress $data

    let risk_score = ($error_rate * 0.6) + ($resource_stress * 0.4)

    {
        risk_score: $risk_score
        estimated_time: (if $risk_score > 0.9 { ((date now) + 2hr) } else { ((date now) + 1day) })
        components: ["cpu", "memory", "application"]
        recommendation: (if $risk_score > 0.8 { "Immediate attention required" } else { "Monitor closely" })
    }
}

def calculate_error_rate [data: any]: any -> float {
    let total_logs = ($data | where collector == "application_logs" | length)
    if $total_logs == 0 { return 0.0 }

    let error_logs = ($data | where collector == "application_logs" and level in ["error", "fatal"] | length)
    $error_logs / $total_logs
}

def calculate_resource_stress [data: any]: any -> float {
    let cpu_stress = ($data | where metric_name == "cpu" | get value | math avg) / 100
    let memory_stress = ($data | where metric_name == "memory" | get value | math avg) / 100

    ($cpu_stress + $memory_stress) / 2
}

# Auto Healer Agent (requires careful configuration)
def execute_auto_healer [agent: record]: nothing -> list {
    if not $agent.config.auto_response {
        return []  # Safety check
    }

    let alerts = query_observability_data --collector "alerts" --time_range "5m" --data_dir $agent.data_dir

    if ($alerts | length) == 0 {
        return []
    }

    # Only process critical alerts for auto-healing
    let critical_alerts = ($alerts | where severity == "critical")

    let actions = ($critical_alerts | each {|alert|
        let action = determine_healing_action $alert $agent.config

        if ($action | is-not-empty) {
            {
                alert_id: $alert.id
                action_type: $action.type
                action_details: $action.details
                risk_level: $action.risk
                auto_executed: false  # Manual approval required by default
            }
        }
    } | compact)

    $actions
}

def determine_healing_action [alert: record, config: record]: nothing -> record {
    match $alert.type {
        "service_down" => {
            {
                type: "restart_service"
                details: { service: $alert.service, method: "systemctl_restart" }
                risk: "low"
            }
        }
        "high_cpu" => {
            {
                type: "scale_up"
                details: { resource: $alert.resource, scale_factor: 1.5 }
                risk: "medium"
            }
        }
        _ => {}
    }
}

# Utility functions
def parse_interval [interval: string]: string -> int {
    match $interval {
        $i if ($i | str ends-with "s") => ($i | str replace "s" "" | into int)
        $i if ($i | str ends-with "m") => (($i | str replace "m" "" | into int) * 60)
        $i if ($i | str ends-with "h") => (($i | str replace "h" "" | into int) * 3600)
        _ => 60
    }
}

def update_agent_performance [agent: record, runtime: duration, results: list]: nothing -> nothing {
    # Update agent performance statistics
    # This would modify agent state in a real implementation
}

def process_agent_results [agent: record, results: list]: nothing -> nothing {
    if ($results | length) > 0 {
        print $"🔍 Agent ($agent.name) generated ($results | length) insights:"
        $results | each {|result|
            print $"  - ($result.type): ($result | get description? | default 'No description')"
        } | ignore

        # Send notifications if configured
        send_agent_notifications $agent $results
    }
}

def send_agent_notifications [agent: record, results: list]: nothing -> nothing {
    # Send notifications for agent findings
    $results | each {|result|
        if $result.severity? in ["high", "critical"] {
            print $"🚨 ALERT: ($result.type) - ($result | get message? | default 'Critical finding')"
        }
    } | ignore
}

# Agent management commands
export def list_running_agents []: nothing -> list {
    # List currently running agents
    # This would query actual running processes in production
    []
}

export def stop_agent [agent_name: string]: string -> nothing {
    print $"🛑 Stopping agent: ($agent_name)"
    # Implementation would stop the specific agent process
}

export def get_agent_status [agent_name?: string]: nothing -> any {
    if ($agent_name | is-empty) {
        print "📊 All agents status:"
        # Return status of all agents
        []
    } else {
        print $"📊 Status of agent: ($agent_name)"
        # Return status of specific agent
        {}
    }
}