# Traditional approach - passive parameters
temperature = 0.7
max_tokens = 1000
model = "gpt-4"

# Revolutionary approach - active coordination primitives
{:ok, temperature} = DSPEx.Variables.CognitiveFloat.start_link(
  name: :temperature,
  range: {0.0, 2.0},
  default: 0.7,
  coordination_scope: :cluster,
  affected_agents: [:llm_agents, :optimizer_agents],
  adaptation_strategy: :performance_feedback,
  economic_coordination: true
)

defmodule DSPEx.Variables.CognitiveVariable do
  @moduledoc """
  Base agent for all cognitive variables with intelligent coordination capabilities
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Variables.Actions.UpdateValue,
    DSPEx.Variables.Actions.CoordinateAffectedAgents,
    DSPEx.Variables.Actions.AdaptBasedOnFeedback,
    DSPEx.Variables.Actions.SyncAcrossCluster,
    DSPEx.Variables.Actions.NegotiateEconomicChange,
    DSPEx.Variables.Actions.ResolveConflicts,
    DSPEx.Variables.Actions.PredictOptimalValue,
    DSPEx.Variables.Actions.AnalyzePerformanceImpact
  ]
  
  @sensors [
    DSPEx.Variables.Sensors.PerformanceFeedbackSensor,
    DSPEx.Variables.Sensors.AgentHealthMonitor,
    DSPEx.Variables.Sensors.ClusterStateSensor,
    DSPEx.Variables.Sensors.CostMonitorSensor,
    DSPEx.Variables.Sensors.ConflictDetectionSensor,
    DSPEx.Variables.Sensors.NetworkLatencySensor
  ]
  
  @skills [
    DSPEx.Variables.Skills.PerformanceAnalysis,
    DSPEx.Variables.Skills.ConflictResolution,
    DSPEx.Variables.Skills.EconomicNegotiation,
    DSPEx.Variables.Skills.PredictiveOptimization
  ]
  
  defstruct [
    :name,                    # Variable identifier
    :type,                    # :float, :choice, :composite, :agent_team
    :current_value,           # Current variable value
    :valid_range,             # Valid value range/choices
    :coordination_scope,      # :local, :cluster, :global
    :affected_agents,         # List of agent IDs affected by this variable
    :adaptation_strategy,     # How the variable adapts (:static, :performance, :economic, :ml)
    :performance_history,     # Historical performance data
    :coordination_history,    # History of coordination events
    :economic_config,         # Economic coordination configuration
    :cluster_sync_config,     # Cluster synchronization configuration
    :constraints,             # Inter-variable constraints
    :last_updated,            # Timestamp of last update
    :metadata                 # Additional variable metadata
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      name: opts[:name] || agent.id,
      type: opts[:type] || :float,
      current_value: opts[:default] || 0.0,
      valid_range: opts[:range] || {0.0, 1.0},
      coordination_scope: opts[:coordination_scope] || :local,
      affected_agents: opts[:affected_agents] || [],
      adaptation_strategy: opts[:adaptation_strategy] || :static,
      performance_history: [],
      coordination_history: [],
      economic_config: initialize_economic_config(opts[:economic_coordination]),
      cluster_sync_config: initialize_cluster_sync(opts[:cluster_sync]),
      constraints: opts[:constraints] || [],
      last_updated: DateTime.utc_now(),
      metadata: opts[:metadata] || %{}
    }
    
    # Register with Variable coordination system
    register_with_coordination_system(initial_state)
    
    # Setup adaptation monitoring if enabled
    setup_adaptation_monitoring(initial_state)
    
    # Subscribe to relevant signals
    subscribe_to_coordination_signals(initial_state)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  # Core signal handling for Variable coordination
  def handle_signal({:performance_feedback, performance_data}, agent) do
    case should_adapt_based_on_performance?(performance_data, agent.state) do
      true ->
        new_value = calculate_optimal_value(performance_data, agent.state)
        coordinate_value_change(new_value, agent.state)
        
      false ->
        {:ok, agent}
    end
  end
  
  def handle_signal({:agent_suggestion, agent_id, suggested_value, rationale}, agent) do
    case evaluate_agent_suggestion(agent_id, suggested_value, rationale, agent.state) do
      {:accept, new_value} ->
        coordinate_value_change(new_value, agent.state)
        
      {:negotiate, counter_proposal} ->
        negotiate_with_agent(agent_id, counter_proposal, agent.state)
        
      {:reject, reason} ->
        notify_suggestion_rejected(agent_id, reason, agent.state)
        {:ok, agent}
    end
  end
  
  def handle_signal({:cluster_sync_request, requesting_node, proposed_value}, agent) do
    case agent.state.cluster_sync_config.strategy do
      :consensus ->
        participate_in_consensus(requesting_node, proposed_value, agent.state)
        
      :eventual_consistency ->
        apply_eventual_update(proposed_value, agent.state)
        
      :strong_consistency ->
        coordinate_strong_consistency_update(requesting_node, proposed_value, agent.state)
    end
  end
  
  def handle_signal({:economic_bid, auction_id, bid_amount, bidder_agent}, agent) do
    case should_accept_economic_bid?(auction_id, bid_amount, bidder_agent, agent.state) do
      true ->
        accept_economic_change(auction_id, bid_amount, bidder_agent, agent.state)
        
      false ->
        reject_economic_bid(auction_id, bidder_agent, agent.state)
    end
  end
  
  def handle_signal({:conflict_detected, conflicting_variable, conflict_data}, agent) do
    resolution_strategy = determine_conflict_resolution_strategy(conflict_data, agent.state)
    resolve_variable_conflict(conflicting_variable, resolution_strategy, agent.state)
  end
  
  # Private implementation functions
  defp register_with_coordination_system(state) do
    coordination_spec = %{
      variable_id: state.name,
      coordination_scope: state.coordination_scope,
      affected_agents: state.affected_agents,
      adaptation_strategy: state.adaptation_strategy
    }
    
    Jido.Signal.Bus.broadcast(:variable_coordinator, {
      :register_variable, 
      coordination_spec
    })
  end
  
  defp coordinate_value_change(new_value, state) do
    if new_value != state.current_value do
      coordination_signal = %{
        type: :variable_change_coordination,
        variable_name: state.name,
        old_value: state.current_value,
        new_value: new_value,
        coordination_scope: state.coordination_scope,
        affected_agents: state.affected_agents,
        coordination_id: generate_coordination_id()
      }
      
      case state.coordination_scope do
        :local ->
          coordinate_local_change(coordination_signal, state)
          
        :cluster ->
          coordinate_cluster_change(coordination_signal, state)
          
        :global ->
          coordinate_global_change(coordination_signal, state)
      end
    else
      {:ok, state}
    end
  end
  
  defp coordinate_local_change(coordination_signal, state) do
    # Notify all affected agents locally
    Enum.each(state.affected_agents, fn agent_id ->
      Jido.Signal.Bus.broadcast(agent_id, {
        :variable_changed, 
        coordination_signal
      })
    end)
    
    # Wait for confirmations
    collect_coordination_confirmations(coordination_signal, state)
  end
  
  defp coordinate_cluster_change(coordination_signal, state) do
    # Get cluster nodes
    cluster_nodes = DSPEx.Clustering.get_cluster_nodes()
    
    # Coordinate across cluster based on sync strategy
    case state.cluster_sync_config.strategy do
      :consensus ->
        coordinate_cluster_consensus(coordination_signal, cluster_nodes, state)
        
      :eventual_consistency ->
        broadcast_eventual_change(coordination_signal, cluster_nodes, state)
        
      :strong_consistency ->
        coordinate_strong_consistency(coordination_signal, cluster_nodes, state)
    end
  end
  
  defp should_adapt_based_on_performance?(performance_data, state) do
    case state.adaptation_strategy do
      :static ->
        false
        
      :performance_feedback ->
        analyze_performance_degradation(performance_data, state.performance_history)
        
      :economic ->
        analyze_cost_performance_tradeoff(performance_data, state.economic_config)
        
      :ml ->
        analyze_ml_optimization_opportunity(performance_data, state.metadata)
        
      {:custom, module, function} ->
        apply(module, function, [performance_data, state])
    end
  end
  
  defp calculate_optimal_value(performance_data, state) do
    case state.adaptation_strategy do
      :performance_feedback ->
        optimize_for_performance(performance_data, state)
        
      :economic ->
        optimize_for_cost_performance(performance_data, state)
        
      :ml ->
        optimize_for_ml_metrics(performance_data, state)
        
      {:custom, module, function} ->
        apply(module, function, [performance_data, state])
    end
  end
end

defmodule DSPEx.Variables.CognitiveFloat do
  @moduledoc """
  Intelligent continuous parameter coordination with real-time adaptation
  """
  
  use DSPEx.Variables.CognitiveVariable
  
  @type_specific_actions [
    DSPEx.Variables.Actions.Float.GradientDescent,
    DSPEx.Variables.Actions.Float.BayesianOptimization,
    DSPEx.Variables.Actions.Float.SimulatedAnnealing,
    DSPEx.Variables.Actions.Float.AdaptiveLearningRate
  ]
  
  @type_specific_sensors [
    DSPEx.Variables.Sensors.Float.GradientSensor,
    DSPEx.Variables.Sensors.Float.ConvergenceSensor,
    DSPEx.Variables.Sensors.Float.NoiseDetectionSensor
  ]
  
  # Specialized float coordination
  def handle_signal({:gradient_feedback, gradient_data}, agent) do
    case agent.state.adaptation_strategy do
      :ml ->
        new_value = apply_gradient_update(gradient_data, agent.state)
        coordinate_value_change(new_value, agent.state)
        
      _ ->
        {:ok, agent}
    end
  end
  
  def handle_signal({:bayesian_optimization_result, optimization_data}, agent) do
    candidate_value = optimization_data.suggested_value
    expected_improvement = optimization_data.expected_improvement
    
    if expected_improvement > agent.state.metadata[:improvement_threshold] do
      coordinate_value_change(candidate_value, agent.state)
    else
      {:ok, agent}
    end
  end
  
  defp apply_gradient_update(gradient_data, state) do
    learning_rate = state.metadata[:learning_rate] || 0.01
    momentum = state.metadata[:momentum] || 0.9
    
    gradient = gradient_data.gradient
    previous_update = state.metadata[:previous_update] || 0.0
    
    # Momentum-based gradient descent
    update = momentum * previous_update + learning_rate * gradient
    new_value = state.current_value - update
    
    # Clip to valid range
    clip_to_range(new_value, state.valid_range)
  end
end

defmodule DSPEx.Variables.CognitiveChoice do
  @moduledoc """
  Intelligent categorical parameter coordination with economic mechanisms
  """
  
  use DSPEx.Variables.CognitiveVariable
  
  @type_specific_actions [
    DSPEx.Variables.Actions.Choice.MultiArmedBandit,
    DSPEx.Variables.Actions.Choice.ThompsonSampling,
    DSPEx.Variables.Actions.Choice.UpperConfidenceBound,
    DSPEx.Variables.Actions.Choice.EconomicAuction
  ]
  
  @type_specific_sensors [
    DSPEx.Variables.Sensors.Choice.RewardSensor,
    DSPEx.Variables.Sensors.Choice.ExplorationSensor,
    DSPEx.Variables.Sensors.Choice.CostBenefitSensor
  ]
  
  defstruct [
    # Base CognitiveVariable fields
    # Plus choice-specific fields
    :choices,                 # Available choices
    :choice_rewards,         # Reward history for each choice
    :choice_costs,           # Cost data for each choice
    :exploration_rate,       # Exploration vs exploitation balance
    :auction_config,         # Economic choice coordination config
    :voting_strategy,        # How agents vote on choices
    :consensus_threshold     # Threshold for choice changes
  ]
  
  # Economic choice coordination
  def handle_signal({:choice_auction_request, auction_spec}, agent) do
    case agent.state.economic_config.enabled do
      true ->
        participate_in_choice_auction(auction_spec, agent.state)
        
      false ->
        {:ok, agent}
    end
  end
  
  def handle_signal({:agent_vote, agent_id, preferred_choice, weight}, agent) do
    case update_choice_voting(agent_id, preferred_choice, weight, agent.state) do
      {:consensus_reached, winning_choice} ->
        coordinate_value_change(winning_choice, agent.state)
        
      {:voting_continues, updated_state} ->
        {:ok, %{agent | state: updated_state}}
    end
  end
  
  defp participate_in_choice_auction(auction_spec, state) do
    # Evaluate each choice option
    choice_evaluations = Enum.map(state.choices, fn choice ->
      {choice, evaluate_choice_value(choice, state)}
    end)
    
    # Submit bids for preferred choices
    submit_choice_bids(choice_evaluations, auction_spec, state)
  end
  
  defp evaluate_choice_value(choice, state) do
    reward_history = Map.get(state.choice_rewards, choice, [])
    cost_history = Map.get(state.choice_costs, choice, [])
    
    expected_reward = calculate_expected_reward(reward_history)
    expected_cost = calculate_expected_cost(cost_history)
    
    # Value = Expected Reward - Expected Cost
    expected_reward - expected_cost
  end
end

defmodule DSPEx.Variables.CognitiveAgentTeam do
  @moduledoc """
  Revolutionary Variables that intelligently select and coordinate agent teams
  """
  
  use DSPEx.Variables.CognitiveVariable
  
  @type_specific_actions [
    DSPEx.Variables.Actions.AgentTeam.SelectTeamMembers,
    DSPEx.Variables.Actions.AgentTeam.EvaluateTeamPerformance,
    DSPEx.Variables.Actions.AgentTeam.RebalanceTeam,
    DSPEx.Variables.Actions.AgentTeam.NegotiateTeamChanges,
    DSPEx.Variables.Actions.AgentTeam.OptimizeTeamComposition
  ]
  
  @type_specific_sensors [
    DSPEx.Variables.Sensors.AgentTeam.TeamPerformanceSensor,
    DSPEx.Variables.Sensors.AgentTeam.TeamCohesionSensor,
    DSPEx.Variables.Sensors.AgentTeam.WorkloadBalanceSensor,
    DSPEx.Variables.Sensors.AgentTeam.CostEfficiencySensor
  ]
  
  defstruct [
    # Base CognitiveVariable fields
    # Plus team-specific fields
    :agent_pool,             # Available agents for team selection
    :current_team,           # Currently selected team members
    :team_size_range,        # {min_size, max_size}
    :role_requirements,      # Required roles in team
    :team_performance_history, # Historical team performance data
    :agent_performance_data, # Individual agent performance tracking
    :team_coordination_config, # How team coordinates
    :selection_strategy,     # Team selection algorithm
    :rebalancing_frequency   # How often to evaluate team changes
  ]
  
  # Revolutionary team coordination
  def handle_signal({:team_performance_feedback, performance_data}, agent) do
    case analyze_team_performance(performance_data, agent.state) do
      {:needs_rebalancing, rebalancing_plan} ->
        coordinate_team_rebalancing(rebalancing_plan, agent.state)
        
      {:add_agent, agent_requirements} ->
        coordinate_agent_addition(agent_requirements, agent.state)
        
      {:remove_agent, agent_to_remove} ->
        coordinate_agent_removal(agent_to_remove, agent.state)
        
      {:optimal, _} ->
        {:ok, agent}
    end
  end
  
  def handle_signal({:agent_availability_changed, agent_id, availability_status}, agent) do
    case availability_status do
      :unavailable ->
        if agent_id in agent.state.current_team do
          find_replacement_agent(agent_id, agent.state)
        else
          {:ok, agent}
        end
        
      :available ->
        evaluate_agent_for_team_addition(agent_id, agent.state)
    end
  end
  
  def handle_signal({:new_agent_registered, agent_spec}, agent) do
    case evaluate_agent_capabilities(agent_spec, agent.state) do
      {:better_than_current, replacement_target} ->
        propose_agent_replacement(agent_spec, replacement_target, agent.state)
        
      {:valuable_addition, _} ->
        propose_team_expansion(agent_spec, agent.state)
        
      {:not_suitable, _} ->
        {:ok, agent}
    end
  end
  
  defp coordinate_team_rebalancing(rebalancing_plan, state) do
    # Create coordination plan for team changes
    coordination_spec = %{
      type: :team_rebalancing,
      current_team: state.current_team,
      planned_changes: rebalancing_plan,
      coordination_strategy: state.team_coordination_config.strategy
    }
    
    case state.team_coordination_config.strategy do
      :consensus ->
        coordinate_team_consensus(coordination_spec, state)
        
      :democratic ->
        coordinate_team_voting(coordination_spec, state)
        
      :performance_based ->
        coordinate_performance_based_changes(coordination_spec, state)
        
      :economic ->
        coordinate_economic_team_changes(coordination_spec, state)
    end
  end
  
  defp analyze_team_performance(performance_data, state) do
    current_performance = performance_data.overall_performance
    performance_trend = calculate_performance_trend(state.team_performance_history)
    team_efficiency = calculate_team_efficiency(performance_data, state.current_team)
    
    cond do
      current_performance < state.metadata[:performance_threshold] ->
        generate_rebalancing_plan(performance_data, state)
        
      team_efficiency < state.metadata[:efficiency_threshold] ->
        {:needs_rebalancing, %{type: :efficiency_optimization}}
        
      length(state.current_team) < elem(state.team_size_range, 0) ->
        {:add_agent, determine_needed_capabilities(performance_data, state)}
        
      performance_trend < -0.1 ->
        identify_underperforming_agent(performance_data, state)
        
      true ->
        {:optimal, performance_data}
    end
  end
end

defmodule DSPEx.Variables.VariableCoordinator do
  @moduledoc """
  Central coordinator for all Variables with global optimization capabilities
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Variables.Actions.Coordinator.RegisterVariable,
    DSPEx.Variables.Actions.Coordinator.CoordinateGlobalOptimization,
    DSPEx.Variables.Actions.Coordinator.ResolveVariableConflicts,
    DSPEx.Variables.Actions.Coordinator.ManageVariableDependencies,
    DSPEx.Variables.Actions.Coordinator.OptimizeVariableSpace
  ]
  
  @sensors [
    DSPEx.Variables.Sensors.Coordinator.GlobalPerformanceSensor,
    DSPEx.Variables.Sensors.Coordinator.ConflictDetectionSensor,
    DSPEx.Variables.Sensors.Coordinator.DependencyMonitorSensor
  ]
  
  defstruct [
    :registered_variables,    # Map of all registered Variables
    :variable_dependencies,   # Inter-variable dependency graph
    :global_constraints,      # System-wide constraints
    :optimization_history,    # Global optimization history
    :conflict_resolution_strategy, # How to resolve Variable conflicts
    :coordination_patterns,   # Learned coordination patterns
    :performance_targets,     # Global performance targets
    :economic_budget,         # Budget for economic coordination
    :cluster_topology        # Current cluster state for distributed coordination
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      registered_variables: %{},
      variable_dependencies: :digraph.new(),
      global_constraints: opts[:global_constraints] || [],
      optimization_history: [],
      conflict_resolution_strategy: opts[:conflict_resolution] || :performance_based,
      coordination_patterns: %{},
      performance_targets: opts[:performance_targets] || %{},
      economic_budget: opts[:economic_budget] || %{total: 100.0, allocated: 0.0},
      cluster_topology: %{nodes: [node()], replication_factor: 1}
    }
    
    # Subscribe to Variable coordination signals
    Jido.Signal.Bus.subscribe(self(), :variable_coordination)
    
    # Start global optimization cycle
    :timer.send_interval(30_000, :global_optimization_cycle)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_signal({:register_variable, variable_spec}, agent) do
    updated_state = register_variable(variable_spec, agent.state)
    {:ok, %{agent | state: updated_state}}
  end
  
  def handle_signal({:variable_change_coordination, coordination_spec}, agent) do
    case validate_variable_change(coordination_spec, agent.state) do
      {:ok, :approved} ->
        coordinate_variable_change(coordination_spec, agent.state)
        
      {:conflict, conflicting_variables} ->
        initiate_conflict_resolution(coordination_spec, conflicting_variables, agent.state)
        
      {:constraint_violation, violated_constraints} ->
        handle_constraint_violation(coordination_spec, violated_constraints, agent.state)
    end
  end
  
  def handle_signal({:global_performance_feedback, performance_data}, agent) do
    case analyze_global_performance(performance_data, agent.state) do
      {:optimization_opportunity, optimization_plan} ->
        execute_global_optimization(optimization_plan, agent.state)
        
      {:satisfactory, _} ->
        {:ok, agent}
        
      {:degradation, problem_variables} ->
        investigate_performance_degradation(problem_variables, agent.state)
    end
  end
  
  def handle_info(:global_optimization_cycle, agent) do
    case should_run_global_optimization?(agent.state) do
      true ->
        optimization_plan = generate_global_optimization_plan(agent.state)
        execute_global_optimization(optimization_plan, agent.state)
        
      false ->
        {:ok, agent}
    end
  end
  
  defp register_variable(variable_spec, state) do
    variable_id = variable_spec.variable_id
    
    # Add to registered variables
    updated_variables = Map.put(state.registered_variables, variable_id, variable_spec)
    
    # Update dependency graph if dependencies exist
    updated_dependencies = case variable_spec[:dependencies] do
      nil -> state.variable_dependencies
      deps -> add_variable_dependencies(variable_id, deps, state.variable_dependencies)
    end
    
    # Update global constraints if variable adds constraints
    updated_constraints = case variable_spec[:global_constraints] do
      nil -> state.global_constraints
      constraints -> state.global_constraints ++ constraints
    end
    
    %{state |
      registered_variables: updated_variables,
      variable_dependencies: updated_dependencies,
      global_constraints: updated_constraints
    }
  end
  
  defp generate_global_optimization_plan(state) do
    # Analyze current variable values and performance
    current_configuration = extract_current_configuration(state.registered_variables)
    performance_data = get_global_performance_data()
    
    # Generate optimization candidates
    optimization_candidates = generate_optimization_candidates(
      current_configuration, 
      performance_data, 
      state
    )
    
    # Rank candidates by expected improvement
    ranked_candidates = rank_optimization_candidates(optimization_candidates, state)
    
    # Create execution plan
    %{
      type: :global_optimization,
      candidates: ranked_candidates,
      execution_strategy: determine_execution_strategy(ranked_candidates, state),
      constraints: state.global_constraints,
      budget: calculate_optimization_budget(state.economic_budget)
    }
  end
end

defmodule DSPEx.Variables.PerformanceAdapter do
  @moduledoc """
  Real-time performance adaptation engine for Variables
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Variables.Actions.Performance.CollectMetrics,
    DSPEx.Variables.Actions.Performance.AnalyzeTrends,
    DSPEx.Variables.Actions.Performance.GenerateAdaptations,
    DSPEx.Variables.Actions.Performance.ValidateAdaptations
  ]
  
  @sensors [
    DSPEx.Variables.Sensors.Performance.LatencySensor,
    DSPEx.Variables.Sensors.Performance.ThroughputSensor,
    DSPEx.Variables.Sensors.Performance.AccuracySensor,
    DSPEx.Variables.Sensors.Performance.CostSensor,
    DSPEx.Variables.Sensors.Performance.ResourceUtilizationSensor
  ]
  
  defstruct [
    :performance_metrics,     # Current performance metrics
    :metric_history,          # Historical performance data
    :adaptation_thresholds,   # Thresholds for triggering adaptations
    :adaptation_strategies,   # Available adaptation strategies
    :learning_models,         # ML models for performance prediction
    :adaptation_frequency,    # How often to check for adaptations
    :validation_period       # How long to validate adaptations
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      performance_metrics: %{},
      metric_history: [],
      adaptation_thresholds: opts[:thresholds] || default_thresholds(),
      adaptation_strategies: opts[:strategies] || default_strategies(),
      learning_models: initialize_learning_models(),
      adaptation_frequency: opts[:frequency] || 5_000,
      validation_period: opts[:validation_period] || 30_000
    }
    
    # Start performance monitoring cycle
    :timer.send_interval(initial_state.adaptation_frequency, :collect_performance_metrics)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_info(:collect_performance_metrics, agent) do
    # Collect current performance metrics
    current_metrics = collect_system_performance_metrics()
    
    # Update metric history
    updated_history = [current_metrics | Enum.take(agent.state.metric_history, 99)]
    
    # Analyze for adaptation opportunities
    case analyze_adaptation_opportunities(current_metrics, updated_history, agent.state) do
      {:adaptations_needed, adaptations} ->
        execute_adaptations(adaptations, agent.state)
        
      {:no_adaptations_needed, _} ->
        {:ok, %{agent | state: %{agent.state | 
          performance_metrics: current_metrics,
          metric_history: updated_history
        }}}
    end
  end
  
  defp collect_system_performance_metrics do
    %{
      # System performance metrics
      latency: measure_system_latency(),
      throughput: measure_system_throughput(),
      accuracy: measure_system_accuracy(),
      cost_per_operation: measure_cost_per_operation(),
      resource_utilization: measure_resource_utilization(),
      
      # Agent performance metrics
      agent_response_times: measure_agent_response_times(),
      agent_success_rates: measure_agent_success_rates(),
      agent_coordination_efficiency: measure_coordination_efficiency(),
      
      # Variable-specific metrics
      variable_adaptation_frequency: measure_variable_adaptations(),
      variable_coordination_latency: measure_variable_coordination(),
      variable_conflict_rate: measure_variable_conflicts(),
      
      timestamp: DateTime.utc_now()
    }
  end
  
  defp analyze_adaptation_opportunities(current_metrics, history, state) do
    # Detect performance trends
    trends = analyze_performance_trends(history)
    
    # Identify problematic areas
    problems = identify_performance_problems(current_metrics, state.adaptation_thresholds)
    
    # Generate adaptation recommendations
    adaptations = generate_adaptations(trends, problems, state)
    
    case adaptations do
      [] -> {:no_adaptations_needed, current_metrics}
      adaptations -> {:adaptations_needed, adaptations}
    end
  end
  
  defp generate_adaptations(trends, problems, state) do
    # Combine trend analysis with problem identification
    Enum.flat_map(problems, fn problem ->
      generate_adaptations_for_problem(problem, trends, state)
    end)
    |> Enum.uniq()
    |> rank_adaptations_by_impact()
  end
  
  defp generate_adaptations_for_problem(problem, trends, state) do
    case problem.type do
      :high_latency ->
        [
          %{type: :reduce_coordination_scope, target_variables: problem.related_variables},
          %{type: :increase_cache_size, target_variables: problem.related_variables},
          %{type: :optimize_algorithm_parameters, target_variables: problem.related_variables}
        ]
        
      :low_accuracy ->
        [
          %{type: :increase_model_complexity, target_variables: problem.related_variables},
          %{type: :adjust_hyperparameters, target_variables: problem.related_variables},
          %{type: :expand_agent_team, target_variables: problem.related_variables}
        ]
        
      :high_cost ->
        [
          %{type: :switch_to_cheaper_model, target_variables: problem.related_variables},
          %{type: :reduce_request_frequency, target_variables: problem.related_variables},
          %{type: :optimize_resource_allocation, target_variables: problem.related_variables}
        ]
        
      :resource_bottleneck ->
        [
          %{type: :scale_horizontally, target_variables: problem.related_variables},
          %{type: :optimize_resource_usage, target_variables: problem.related_variables},
          %{type: :rebalance_workload, target_variables: problem.related_variables}
        ]
    end
  end
end

defmodule DSPEx.Variables.EconomicCoordinator do
  @moduledoc """
  Economic coordination mechanisms for intelligent Variable resource allocation
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Variables.Actions.Economic.CreateVariableAuction,
    DSPEx.Variables.Actions.Economic.EvaluateBids,
    DSPEx.Variables.Actions.Economic.AllocateResources,
    DSPEx.Variables.Actions.Economic.TrackCosts,
    DSPEx.Variables.Actions.Economic.OptimizeBudgets
  ]
  
  @sensors [
    DSPEx.Variables.Sensors.Economic.CostMonitorSensor,
    DSPEx.Variables.Sensors.Economic.BidActivitySensor,
    DSPEx.Variables.Sensors.Economic.MarketConditionsSensor,
    DSPEx.Variables.Sensors.Economic.ResourcePricingSensor
  ]
  
  defstruct [
    :active_auctions,         # Currently running auctions
    :budget_allocations,      # Budget allocated to each Variable
    :cost_history,            # Historical cost data
    :market_prices,           # Current market prices for resources
    :economic_policies,       # Economic coordination policies
    :auction_strategies,      # Available auction mechanisms
    :reputation_scores,       # Agent reputation for economic coordination
    :budget_constraints      # System-wide budget constraints
  ]
  
  def handle_signal({:variable_resource_request, variable_id, resource_spec, urgency}, agent) do
    case determine_allocation_strategy(resource_spec, urgency, agent.state) do
      :direct_allocation ->
        allocate_resources_directly(variable_id, resource_spec, agent.state)
        
      :auction_required ->
        create_resource_auction(variable_id, resource_spec, urgency, agent.state)
        
      :budget_exceeded ->
        handle_budget_constraint(variable_id, resource_spec, agent.state)
    end
  end
  
  def handle_signal({:auction_bid, auction_id, bidder_variable, bid_spec}, agent) do
    case validate_economic_bid(auction_id, bidder_variable, bid_spec, agent.state) do
      {:valid, validated_bid} ->
        process_auction_bid(auction_id, bidder_variable, validated_bid, agent.state)
        
      {:invalid, reason} ->
        reject_auction_bid(auction_id, bidder_variable, reason, agent.state)
    end
  end
  
  defp create_resource_auction(variable_id, resource_spec, urgency, state) do
    auction_spec = %{
      id: generate_auction_id(),
      resource: resource_spec,
      initiator: variable_id,
      urgency_level: urgency,
      auction_type: determine_auction_type(resource_spec, urgency),
      starting_price: calculate_starting_price(resource_spec, state.market_prices),
      duration: calculate_auction_duration(urgency),
      participants: identify_potential_participants(resource_spec, state)
    }
    
    # Start auction
    start_auction(auction_spec, state)
    
    # Notify potential participants
    notify_auction_participants(auction_spec, state)
  end
  
  defp determine_auction_type(resource_spec, urgency) do
    case urgency do
      :critical -> :english  # Fast ascending price auction
      :high -> :sealed_bid   # Private bidding for strategic resources
      :medium -> :dutch      # Descending price for efficiency
      :low -> :combinatorial # Complex bundling for optimization
    end
  end
end

defmodule DSPEx.Variables.ClusterSync do
  @moduledoc """
  Cluster-wide Variable synchronization with conflict resolution
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Variables.Actions.Cluster.SyncVariableAcrossNodes,
    DSPEx.Variables.Actions.Cluster.ResolveConflicts,
    DSPEx.Variables.Actions.Cluster.ManageNodeFailures,
    DSPEx.Variables.Actions.Cluster.OptimizeReplication
  ]
  
  @sensors [
    DSPEx.Variables.Sensors.Cluster.NodeHealthSensor,
    DSPEx.Variables.Sensors.Cluster.NetworkLatencySensor,
    DSPEx.Variables.Sensors.Cluster.SyncStatusSensor,
    DSPEx.Variables.Sensors.Cluster.ConflictDetectionSensor
  ]
  
  defstruct [
    :cluster_topology,        # Current cluster state
    :sync_strategies,         # Available synchronization strategies
    :conflict_resolution,     # Conflict resolution configuration
    :replication_config,      # Variable replication settings
    :network_partitions,      # Detected network partitions
    :sync_history,           # Synchronization history
    :node_preferences        # Node-specific Variable preferences
  ]
  
  def handle_signal({:variable_sync_request, variable_id, new_value, source_node}, agent) do
    case agent.state.sync_strategies[variable_id] || :consensus do
      :consensus ->
        coordinate_consensus_sync(variable_id, new_value, source_node, agent.state)
        
      :eventual_consistency ->
        apply_eventual_consistency_sync(variable_id, new_value, source_node, agent.state)
        
      :strong_consistency ->
        coordinate_strong_consistency_sync(variable_id, new_value, source_node, agent.state)
        
      :conflict_free_replicated_data_type ->
        apply_crdt_sync(variable_id, new_value, source_node, agent.state)
    end
  end
  
  def handle_signal({:sync_conflict_detected, variable_id, conflicting_values}, agent) do
    resolution_strategy = agent.state.conflict_resolution[variable_id] || :timestamp_based
    
    case resolution_strategy do
      :timestamp_based ->
        resolve_by_timestamp(variable_id, conflicting_values, agent.state)
        
      :performance_based ->
        resolve_by_performance(variable_id, conflicting_values, agent.state)
        
      :consensus ->
        initiate_conflict_consensus(variable_id, conflicting_values, agent.state)
        
      :custom_resolver ->
        apply_custom_conflict_resolution(variable_id, conflicting_values, agent.state)
    end
  end
  
  defp coordinate_consensus_sync(variable_id, new_value, source_node, state) do
    cluster_nodes = get_active_cluster_nodes(state.cluster_topology)
    
    # Create consensus proposal
    consensus_proposal = %{
      type: :variable_sync,
      variable_id: variable_id,
      proposed_value: new_value,
      proposer: source_node,
      timestamp: DateTime.utc_now(),
      consensus_id: generate_consensus_id()
    }
    
    # Send consensus request to all nodes
    consensus_responses = Enum.map(cluster_nodes, fn node ->
      send_consensus_request(node, consensus_proposal)
    end)
    
    # Collect responses
    case collect_consensus_responses(consensus_proposal.consensus_id, cluster_nodes) do
      {:consensus_achieved, :accept} ->
        apply_variable_sync(variable_id, new_value, state)
        
      {:consensus_achieved, :reject} ->
        reject_variable_sync(variable_id, new_value, source_node, state)
        
      {:consensus_timeout, partial_responses} ->
        handle_consensus_timeout(consensus_proposal, partial_responses, state)
    end
  end
  
  defp apply_crdt_sync(variable_id, new_value, source_node, state) do
    # Use Conflict-free Replicated Data Types for automatic conflict resolution
    current_crdt = get_variable_crdt(variable_id, state)
    incoming_crdt = %{value: new_value, timestamp: DateTime.utc_now(), node: source_node}
    
    # Merge CRDTs
    merged_crdt = merge_variable_crdts(current_crdt, incoming_crdt)
    
    # Apply merged value
    merged_value = extract_value_from_crdt(merged_crdt)
    apply_variable_sync(variable_id, merged_value, state)
  end
end

defmodule DSPEx.Variables.Telemetry do
  @moduledoc """
  Comprehensive telemetry and monitoring for Cognitive Variables
  """
  
  def setup_variable_telemetry do
    :telemetry.attach_many(
      "dspex-variables-telemetry",
      [
        [:dspex, :variables, :value_changed],
        [:dspex, :variables, :coordination_completed],
        [:dspex, :variables, :adaptation_triggered],
        [:dspex, :variables, :conflict_resolved],
        [:dspex, :variables, :economic_transaction],
        [:dspex, :variables, :cluster_sync],
        [:dspex, :variables, :performance_impact]
      ],
      &handle_variable_telemetry_event/4,
      nil
    )
  end
  
  def handle_variable_telemetry_event(event, measurements, metadata, _config) do
    case event do
      [:dspex, :variables, :value_changed] ->
        record_variable_change(measurements, metadata)
        
      [:dspex, :variables, :coordination_completed] ->
        record_coordination_performance(measurements, metadata)
        
      [:dspex, :variables, :adaptation_triggered] ->
        record_adaptation_event(measurements, metadata)
        
      [:dspex, :variables, :economic_transaction] ->
        record_economic_activity(measurements, metadata)
        
      [:dspex, :variables, :cluster_sync] ->
        record_cluster_synchronization(measurements, metadata)
    end
  end
  
  defp record_variable_change(measurements, metadata) do
    # Prometheus metrics
    :prometheus_counter.inc(
      :dspex_variable_changes_total,
      [variable: metadata.variable_name, type: metadata.variable_type]
    )
    
    :prometheus_histogram.observe(
      :dspex_variable_coordination_duration,
      [scope: metadata.coordination_scope],
      measurements.coordination_duration_ms
    )
    
    # Custom metrics for Variable intelligence
    :prometheus_gauge.set(
      :dspex_variable_adaptation_score,
      [variable: metadata.variable_name],
      measurements.adaptation_score
    )
  end
  
  defp record_coordination_performance(measurements, metadata) do
    :prometheus_histogram.observe(
      :dspex_variable_coordination_latency,
      [type: metadata.coordination_type, scope: metadata.coordination_scope],
      measurements.coordination_latency_ms
    )
    
    :prometheus_counter.inc(
      :dspex_variable_coordination_total,
      [type: metadata.coordination_type, result: metadata.result]
    )
  end
end