# Traditional approach - parameters are just values
temperature = 0.7
max_tokens = 1000  
model = "gpt-4"

# External optimizer changes values
optimizer.update_parameter("temperature", 0.8)

# Revolutionary approach - parameters are intelligent agents
{:ok, temperature_agent} = DSPEx.Variables.CognitiveFloat.start_link([
  name: :temperature,
  range: {0.0, 2.0},
  default: 0.7,
  coordination_scope: :cluster,
  affected_agents: [:llm_agent_1, :llm_agent_2, :optimizer_agent],
  adaptation_strategy: :performance_feedback,
  economic_coordination: true,
  mabeam_coordination: %{
    consensus_participation: true,
    reputation_tracking: true,
    economic_mechanisms: [:auction, :reputation]
  }
])

# Variable intelligently coordinates its own changes
Jido.Signal.send(temperature_agent, {:performance_feedback, %{accuracy: 0.95, cost: 0.12}})
# → Variable automatically adapts value and coordinates affected agents
# → Uses MABEAM consensus for cluster-wide coordination
# → Leverages economic mechanisms for cost optimization

defmodule DSPEx.Variables.CognitiveVariable do
  @moduledoc """
  Base implementation for all Cognitive Variables - parameters that are intelligent
  Jido agents capable of autonomous coordination using MABEAM patterns.
  
  Key Innovations:
  1. Variables ARE agents with full Jido capabilities
  2. Active coordination with affected agents via signals
  3. Real-time adaptation based on performance feedback
  4. Economic coordination using MABEAM mechanisms
  5. Cluster-wide consensus for distributed coordination
  """
  
  use Jido.Agent
  
  # Jido Agent Capabilities
  @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,
    DSPEx.Variables.Actions.ParticipateInMABEAMConsensus,
    DSPEx.Variables.Actions.ManageReputationScore
  ]
  
  @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,
    DSPEx.Variables.Sensors.MABEAMRegistrySensor,
    DSPEx.Variables.Sensors.EconomicMarketSensor,
    DSPEx.Variables.Sensors.ReputationTrackingSensor
  ]
  
  @skills [
    DSPEx.Variables.Skills.PerformanceAnalysis,
    DSPEx.Variables.Skills.ConflictResolution,
    DSPEx.Variables.Skills.EconomicNegotiation,
    DSPEx.Variables.Skills.PredictiveOptimization,
    DSPEx.Variables.Skills.MABEAMCoordinationSkill,
    DSPEx.Variables.Skills.DistributedConsensusSkill,
    DSPEx.Variables.Skills.ReputationManagementSkill,
    DSPEx.Variables.Skills.AuctionParticipationSkill
  ]
  
  defstruct [
    # Core Variable Properties
    :name,                          # Variable identifier (atom)
    :type,                          # :float, :integer, :choice, :composite, :agent_team
    :current_value,                 # Current variable value
    :valid_range,                   # Valid range or choice list
    :default_value,                 # Default/fallback value
    
    # Coordination Properties
    :coordination_scope,            # :local, :cluster, :global
    :affected_agents,               # List of agent IDs affected by this variable
    :adaptation_strategy,           # :static, :performance, :economic, :ml_feedback
    :constraint_network,            # Inter-variable constraints and dependencies
    
    # Performance and History
    :performance_history,           # Historical performance data
    :value_history,                 # History of value changes
    :coordination_history,          # History of coordination events
    :adaptation_metrics,            # Metrics for adaptation effectiveness
    
    # MABEAM Integration
    :mabeam_config,                 # MABEAM coordination configuration
    :consensus_participation_history, # History of consensus participation
    :economic_reputation,           # Economic reputation score
    :auction_participation_history, # History of auction participation
    :coordination_performance_score, # Performance in coordination activities
    
    # Economic Coordination
    :economic_config,               # Economic coordination settings
    :cost_sensitivity,              # How sensitive variable is to cost changes
    :bid_strategy,                  # Strategy for auction participation
    :reputation_weights,            # Weights for reputation calculation
    
    # Cluster Synchronization
    :cluster_sync_config,           # Cluster synchronization settings
    :sync_strategy,                 # :consensus, :eventual_consistency, :strong_consistency
    :conflict_resolution_strategy,  # How to resolve value conflicts
    :network_partition_behavior,    # Behavior during network partitions
    
    # Metadata and State
    :last_updated,                  # Timestamp of last update
    :update_generation,             # Generation counter for updates
    :lock_state,                    # Current lock state for coordination
    :metadata                       # Additional variable metadata
  ]
  
  def mount(agent, opts) do
    # Initialize cognitive variable with comprehensive configuration
    initial_state = %__MODULE__{
      name: Keyword.get(opts, :name) || raise("Variable name is required"),
      type: Keyword.get(opts, :type, :float),
      current_value: Keyword.get(opts, :default) || Keyword.get(opts, :initial_value),
      valid_range: Keyword.get(opts, :range) || Keyword.get(opts, :choices),
      default_value: Keyword.get(opts, :default),
      
      coordination_scope: Keyword.get(opts, :coordination_scope, :local),
      affected_agents: Keyword.get(opts, :affected_agents, []),
      adaptation_strategy: Keyword.get(opts, :adaptation_strategy, :performance_feedback),
      constraint_network: Keyword.get(opts, :constraints, %{}),
      
      performance_history: [],
      value_history: [{DateTime.utc_now(), Keyword.get(opts, :default)}],
      coordination_history: [],
      adaptation_metrics: %{},
      
      mabeam_config: %{
        consensus_participation: Keyword.get(opts, :consensus_participation, true),
        economic_coordination: Keyword.get(opts, :economic_coordination, false),
        reputation_tracking: Keyword.get(opts, :reputation_tracking, true),
        coordination_timeout: Keyword.get(opts, :coordination_timeout, 30_000)
      },
      consensus_participation_history: [],
      economic_reputation: 1.0,
      auction_participation_history: [],
      coordination_performance_score: 1.0,
      
      economic_config: %{
        cost_sensitivity: Keyword.get(opts, :cost_sensitivity, 0.5),
        max_bid_ratio: Keyword.get(opts, :max_bid_ratio, 1.2),
        reputation_weight: Keyword.get(opts, :reputation_weight, 0.3)
      },
      cost_sensitivity: Keyword.get(opts, :cost_sensitivity, 0.5),
      bid_strategy: Keyword.get(opts, :bid_strategy, :conservative),
      reputation_weights: Keyword.get(opts, :reputation_weights, %{performance: 0.5, reliability: 0.3, cost: 0.2}),
      
      cluster_sync_config: %{
        sync_strategy: Keyword.get(opts, :sync_strategy, :consensus),
        conflict_resolution: Keyword.get(opts, :conflict_resolution, :latest_wins),
        partition_behavior: Keyword.get(opts, :partition_behavior, :maintain_last_known)
      },
      sync_strategy: Keyword.get(opts, :sync_strategy, :consensus),
      conflict_resolution_strategy: Keyword.get(opts, :conflict_resolution, :latest_wins),
      network_partition_behavior: Keyword.get(opts, :partition_behavior, :maintain_last_known),
      
      last_updated: DateTime.utc_now(),
      update_generation: 0,
      lock_state: :unlocked,
      metadata: Keyword.get(opts, :metadata, %{})
    }
    
    # Register with MABEAM system for coordination capabilities
    case DSPEx.Foundation.Bridge.register_cognitive_variable(agent.id, initial_state) do
      {:ok, registration_info} ->
        Logger.info("Cognitive variable #{initial_state.name} registered with MABEAM system")
        {:ok, %{initial_state | metadata: Map.put(initial_state.metadata, :mabeam_registration, registration_info)}}
        
      {:error, reason} ->
        Logger.warning("Failed to register cognitive variable with MABEAM: #{inspect(reason)}")
        {:ok, initial_state}  # Continue without MABEAM registration
    end
  end
  
  # === Core Signal Handlers ===
  
  @doc """
  Handle requests to change the variable's value.
  Coordinates with affected agents based on coordination scope.
  """
  def handle_signal({:change_value, new_value, opts}, state) do
    requester = Map.get(opts, :requester, :unknown)
    coordination_id = Map.get(opts, :coordination_id, generate_coordination_id())
    force = Map.get(opts, :force, false)
    
    # Validate new value
    case validate_value(new_value, state) do
      {:ok, validated_value} ->
        if force or state.lock_state == :unlocked do
          coordinate_value_change(validated_value, requester, coordination_id, state)
        else
          {:error, {:variable_locked, state.lock_state}}
        end
        
      {:error, reason} ->
        {:error, {:invalid_value, reason}}
    end
  end
  
  @doc """
  Handle performance feedback that may trigger value adaptation.
  """
  def handle_signal({:performance_feedback, feedback_data}, state) do
    # Record performance feedback
    updated_history = [
      {DateTime.utc_now(), feedback_data} | state.performance_history
    ] |> Enum.take(100)  # Keep last 100 entries
    
    new_state = %{state | performance_history: updated_history}
    
    # Determine if adaptation is needed based on strategy
    case should_adapt?(feedback_data, new_state) do
      {:yes, adaptation_params} ->
        adapt_value_based_on_feedback(feedback_data, adaptation_params, new_state)
        
      :no ->
        {:ok, new_state}
    end
  end
  
  @doc """
  Handle coordination requests from other cognitive variables.
  """
  def handle_signal({:coordination_request, coordination_data}, state) do
    case coordination_data do
      %{type: :value_dependency_check, variable: other_var, proposed_value: value} ->
        handle_dependency_coordination(other_var, value, state)
        
      %{type: :conflict_resolution, conflicting_values: values} ->
        handle_conflict_resolution(values, state)
        
      %{type: :consensus_participation, consensus_ref: ref, proposal: proposal} ->
        handle_consensus_participation(ref, proposal, state)
        
      %{type: :economic_negotiation, auction_ref: ref, auction_data: data} ->
        handle_economic_negotiation(ref, data, state)
        
      _ ->
        Logger.warning("Unknown coordination request: #{inspect(coordination_data)}")
        {:ok, state}
    end
  end
  
  @doc """
  Handle cluster synchronization events.
  """
  def handle_signal({:cluster_sync, sync_data}, state) do
    case sync_data do
      %{type: :value_update, source_node: node, new_value: value, generation: gen} ->
        handle_cluster_value_sync(node, value, gen, state)
        
      %{type: :consensus_result, consensus_ref: ref, result: result} ->
        handle_consensus_result(ref, result, state)
        
      %{type: :network_partition, partition_info: info} ->
        handle_network_partition(info, state)
        
      _ ->
        Logger.warning("Unknown cluster sync event: #{inspect(sync_data)}")
        {:ok, state}
    end
  end
  
  # === Coordination Implementation ===
  
  defp coordinate_value_change(new_value, requester, coordination_id, state) do
    case state.coordination_scope do
      :local ->
        # Simple local update with notification
        coordinate_local_value_change(new_value, requester, coordination_id, state)
        
      :cluster ->
        # Use MABEAM consensus for cluster-wide coordination
        coordinate_cluster_value_change(new_value, requester, coordination_id, state)
        
      :global ->
        # Use MABEAM economic mechanisms for global coordination
        coordinate_global_value_change(new_value, requester, coordination_id, state)
    end
  end
  
  defp coordinate_local_value_change(new_value, requester, coordination_id, state) do
    # Update value locally
    updated_state = update_variable_value(new_value, state)
    
    # Notify affected agents
    notify_affected_agents(new_value, state.current_value, requester, coordination_id, state)
    
    Logger.info("Variable #{state.name} updated locally: #{state.current_value} -> #{new_value}")
    {:ok, updated_state}
  end
  
  defp coordinate_cluster_value_change(new_value, requester, coordination_id, state) do
    # Create consensus proposal using MABEAM patterns
    proposal = %{
      type: :cognitive_variable_change,
      variable_id: state.name,
      current_value: state.current_value,
      proposed_value: new_value,
      requester: requester,
      coordination_id: coordination_id,
      affected_agents: state.affected_agents,
      constraints: extract_relevant_constraints(state),
      timestamp: DateTime.utc_now()
    }
    
    # Find affected agents using MABEAM discovery
    case DSPEx.Foundation.Bridge.find_affected_agents(state.affected_agents) do
      {:ok, agent_list} when length(agent_list) > 0 ->
        participant_ids = Enum.map(agent_list, fn {id, _pid, _metadata} -> id end)
        
        Logger.info("Starting consensus for variable #{state.name} with #{length(participant_ids)} participants")
        
        # Start MABEAM consensus coordination
        case DSPEx.Foundation.Bridge.start_consensus(participant_ids, proposal, state.mabeam_config.coordination_timeout) do
          {:ok, consensus_ref} ->
            # Update state to reflect ongoing consensus
            consensus_state = %{state | 
              lock_state: {:consensus_pending, consensus_ref},
              coordination_history: [
                {DateTime.utc_now(), :consensus_started, consensus_ref} | state.coordination_history
              ]
            }
            
            # Wait for consensus result asynchronously
            Task.start(fn -> 
              monitor_consensus_result(consensus_ref, new_value, self())
            end)
            
            {:ok, consensus_state}
            
          {:error, reason} ->
            Logger.warning("Consensus failed for variable #{state.name}: #{inspect(reason)}")
            
            # Fallback to local change with warning
            fallback_state = coordinate_local_value_change(new_value, requester, coordination_id, state)
            record_coordination_failure(reason, fallback_state)
        end
        
      {:ok, []} ->
        Logger.info("No affected agents found, proceeding with local update")
        coordinate_local_value_change(new_value, requester, coordination_id, state)
        
      {:error, reason} ->
        Logger.warning("Failed to find affected agents: #{inspect(reason)}")
        coordinate_local_value_change(new_value, requester, coordination_id, state)
    end
  end
  
  defp coordinate_global_value_change(new_value, requester, coordination_id, state) do
    # Use MABEAM economic mechanisms for cost-aware coordination
    if state.mabeam_config.economic_coordination do
      auction_proposal = %{
        type: :variable_change_auction,
        variable_id: state.name,
        current_value: state.current_value,
        proposed_value: new_value,
        requester: requester,
        coordination_id: coordination_id,
        current_reputation: state.economic_reputation,
        estimated_cost_impact: calculate_change_cost_impact(state.current_value, new_value, state),
        estimated_performance_impact: calculate_change_performance_impact(state.current_value, new_value, state),
        bid_strategy: state.bid_strategy,
        timestamp: DateTime.utc_now()
      }
      
      case DSPEx.Foundation.Bridge.create_auction(auction_proposal) do
        {:ok, auction_ref} ->
          auction_state = %{state | 
            lock_state: {:auction_pending, auction_ref},
            coordination_history: [
              {DateTime.utc_now(), :auction_started, auction_ref} | state.coordination_history
            ]
          }
          
          # Monitor auction result asynchronously
          Task.start(fn -> 
            monitor_auction_result(auction_ref, new_value, self())
          end)
          
          {:ok, auction_state}
          
        {:error, reason} ->
          Logger.warning("Economic coordination failed: #{inspect(reason)}")
          # Fallback to cluster coordination
          coordinate_cluster_value_change(new_value, requester, coordination_id, state)
      end
    else
      # Fall back to cluster coordination if economic coordination is disabled
      coordinate_cluster_value_change(new_value, requester, coordination_id, state)
    end
  end
  
  # === Value Management ===
  
  defp update_variable_value(new_value, state) do
    %{state |
      current_value: new_value,
      value_history: [
        {DateTime.utc_now(), new_value} | state.value_history
      ] |> Enum.take(100),  # Keep last 100 value changes
      last_updated: DateTime.utc_now(),
      update_generation: state.update_generation + 1,
      lock_state: :unlocked
    }
  end
  
  defp validate_value(value, state) do
    case state.type do
      :float when is_number(value) ->
        validate_float_range(value, state.valid_range)
        
      :integer when is_integer(value) ->
        validate_integer_range(value, state.valid_range)
        
      :choice ->
        validate_choice(value, state.valid_range)
        
      :composite ->
        validate_composite(value, state.valid_range)
        
      :agent_team when is_list(value) ->
        validate_agent_team(value, state.valid_range)
        
      _ ->
        {:error, {:invalid_type, state.type, value}}
    end
  end
  
  defp validate_float_range(value, {min, max}) when value >= min and value <= max do
    {:ok, value}
  end
  
  defp validate_float_range(value, {min, max}) do
    {:error, {:out_of_range, value, {min, max}}}
  end
  
  defp validate_float_range(value, nil) do
    {:ok, value}
  end
  
  defp validate_integer_range(value, {min, max}) when value >= min and value <= max do
    {:ok, value}
  end
  
  defp validate_integer_range(value, {min, max}) do
    {:error, {:out_of_range, value, {min, max}}}
  end
  
  defp validate_integer_range(value, nil) do
    {:ok, value}
  end
  
  defp validate_choice(value, choices) when is_list(choices) do
    if value in choices do
      {:ok, value}
    else
      {:error, {:invalid_choice, value, choices}}
    end
  end
  
  defp validate_choice(value, nil) do
    {:ok, value}
  end
  
  defp validate_composite(value, _constraints) do
    # TODO: Implement composite value validation
    {:ok, value}
  end
  
  defp validate_agent_team(agents, constraints) when is_list(agents) do
    # TODO: Implement agent team validation
    {:ok, agents}
  end
  
  # === Notification and Coordination ===
  
  defp notify_affected_agents(new_value, old_value, requester, coordination_id, state) do
    notification = %{
      type: :variable_changed,
      variable_name: state.name,
      old_value: old_value,
      new_value: new_value,
      requester: requester,
      coordination_id: coordination_id,
      timestamp: DateTime.utc_now()
    }
    
    # Send notifications via Jido signals
    Enum.each(state.affected_agents, fn agent_id ->
      case Jido.Signal.send(agent_id, {:variable_notification, notification}) do
        :ok ->
          :ok
        {:error, reason} ->
          Logger.warning("Failed to notify agent #{agent_id}: #{inspect(reason)}")
      end
    end)
  end
  
  # === Adaptation Logic ===
  
  defp should_adapt?(feedback_data, state) do
    case state.adaptation_strategy do
      :static ->
        :no
        
      :performance_feedback ->
        analyze_performance_adaptation(feedback_data, state)
        
      :economic ->
        analyze_economic_adaptation(feedback_data, state)
        
      :ml_feedback ->
        analyze_ml_adaptation(feedback_data, state)
        
      _ ->
        :no
    end
  end
  
  defp analyze_performance_adaptation(feedback_data, state) do
    # Analyze if performance suggests value adaptation
    current_performance = Map.get(feedback_data, :performance, 0.0)
    cost = Map.get(feedback_data, :cost, 0.0)
    
    # Get recent performance history
    recent_performance = state.performance_history
    |> Enum.take(10)
    |> Enum.map(fn {_time, data} -> Map.get(data, :performance, 0.0) end)
    
    if length(recent_performance) >= 3 do
      avg_recent = Enum.sum(recent_performance) / length(recent_performance)
      
      cond do
        current_performance < avg_recent * 0.8 ->
          # Performance degraded significantly
          {:yes, %{direction: :improve_performance, magnitude: 0.1}}
          
        current_performance > avg_recent * 1.2 and cost < state.cost_sensitivity ->
          # Performance improved significantly and cost is acceptable
          {:yes, %{direction: :optimize_further, magnitude: 0.05}}
          
        true ->
          :no
      end
    else
      :no
    end
  end
  
  defp adapt_value_based_on_feedback(feedback_data, adaptation_params, state) do
    current_value = state.current_value
    
    adapted_value = case state.type do
      :float ->
        adapt_float_value(current_value, adaptation_params, state)
        
      :integer ->
        adapt_integer_value(current_value, adaptation_params, state)
        
      :choice ->
        adapt_choice_value(current_value, adaptation_params, state)
        
      _ ->
        current_value
    end
    
    if adapted_value != current_value do
      Logger.info("Variable #{state.name} adapting value based on feedback: #{current_value} -> #{adapted_value}")
      
      # Trigger coordinated value change
      coordinate_value_change(adapted_value, :adaptation_system, generate_coordination_id(), state)
    else
      {:ok, state}
    end
  end
  
  defp adapt_float_value(current_value, %{direction: direction, magnitude: magnitude}, state) do
    {min_val, max_val} = state.valid_range || {0.0, 1.0}
    
    adjustment = case direction do
      :improve_performance -> magnitude
      :optimize_further -> magnitude * 0.5
      :reduce_cost -> -magnitude
      _ -> 0.0
    end
    
    new_value = current_value + adjustment
    
    # Clamp to valid range
    new_value
    |> max(min_val)
    |> min(max_val)
  end
  
  # === Utility Functions ===
  
  defp generate_coordination_id do
    :crypto.strong_rand_bytes(8) |> Base.encode16()
  end
  
  defp extract_relevant_constraints(state) do
    # Extract constraints relevant to this coordination
    state.constraint_network
  end
  
  defp calculate_change_cost_impact(old_value, new_value, state) do
    # Estimate cost impact of value change
    # This would integrate with actual cost models
    abs(new_value - old_value) * Map.get(state.metadata, :cost_per_unit_change, 1.0)
  end
  
  defp calculate_change_performance_impact(old_value, new_value, state) do
    # Estimate performance impact of value change
    # This would use historical data and ML models
    recent_changes = state.value_history
    |> Enum.take(10)
    |> Enum.map(fn {_time, value} -> value end)
    
    if length(recent_changes) >= 2 do
      # Simple trend analysis
      trend = calculate_trend(recent_changes)
      if (new_value > old_value and trend > 0) or (new_value < old_value and trend < 0) do
        0.1  # Positive impact estimate
      else
        -0.05  # Negative impact estimate
      end
    else
      0.0
    end
  end
  
  defp calculate_trend(values) when length(values) >= 2 do
    # Simple linear trend calculation
    indexed_values = Enum.with_index(values)
    
    n = length(values)
    sum_x = n * (n - 1) / 2
    sum_y = Enum.sum(values)
    sum_xy = Enum.reduce(indexed_values, 0, fn {y, x}, acc -> acc + x * y end)
    sum_x2 = Enum.reduce(0..(n-1), 0, fn x, acc -> acc + x * x end)
    
    # Linear regression slope
    (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x)
  end
  
  defp calculate_trend(_values), do: 0.0
  
  defp record_coordination_failure(reason, state) do
    coordination_event = {DateTime.utc_now(), :coordination_failed, reason}
    
    %{state | 
      coordination_history: [coordination_event | state.coordination_history] |> Enum.take(50)
    }
  end
  
  defp monitor_consensus_result(consensus_ref, proposed_value, variable_pid) do
    # This would be implemented to monitor consensus results asynchronously
    # and send results back to the variable agent
    :ok
  end
  
  defp monitor_auction_result(auction_ref, proposed_value, variable_pid) do
    # This would be implemented to monitor auction results asynchronously
    # and send results back to the variable agent
    :ok
  end
end

defmodule DSPEx.Variables.CognitiveFloat do
  @moduledoc """
  Cognitive variable for continuous floating-point parameters with advanced
  optimization capabilities including gradient-based adaptation.
  """
  
  use DSPEx.Variables.CognitiveVariable
  
  defstruct [
    # Inherit all base fields
    __base__: DSPEx.Variables.CognitiveVariable,
    
    # Float-specific properties
    :precision,                     # Decimal precision for value representation
    :gradient_tracking,             # Whether to track gradient information
    :momentum,                      # Momentum for gradient-based adaptation
    :learning_rate,                 # Learning rate for value adaptation
    :bounds_behavior,               # :clamp, :wrap, :reject for out-of-bounds values
    :optimization_history,          # History of optimization steps
    :gradient_estimate,             # Current gradient estimate
    :velocity,                      # Current velocity for momentum-based updates
    :adaptive_learning_rate,        # Whether to use adaptive learning rate
    :convergence_criteria           # Criteria for detecting convergence
  ]
  
  def mount(agent, opts) do
    # Initialize base cognitive variable
    {:ok, base_state} = super(agent, opts)
    
    # Add float-specific configuration
    float_config = %{
      precision: Keyword.get(opts, :precision, 4),
      gradient_tracking: Keyword.get(opts, :gradient_tracking, true),
      momentum: Keyword.get(opts, :momentum, 0.9),
      learning_rate: Keyword.get(opts, :learning_rate, 0.01),
      bounds_behavior: Keyword.get(opts, :bounds_behavior, :clamp),
      optimization_history: [],
      gradient_estimate: 0.0,
      velocity: 0.0,
      adaptive_learning_rate: Keyword.get(opts, :adaptive_learning_rate, true),
      convergence_criteria: Keyword.get(opts, :convergence_criteria, %{threshold: 0.001, patience: 5})
    }
    
    enhanced_state = Map.merge(base_state, float_config)
    
    {:ok, enhanced_state}
  end
  
  # Override adaptation for gradient-based optimization
  def handle_signal({:gradient_feedback, gradient_info}, state) do
    # Update gradient estimate
    new_gradient = gradient_info.gradient
    smoothed_gradient = state.momentum * state.gradient_estimate + (1 - state.momentum) * new_gradient
    
    # Update velocity for momentum-based optimization
    new_velocity = state.momentum * state.velocity - state.learning_rate * smoothed_gradient
    
    # Calculate new value using gradient descent
    proposed_value = state.current_value + new_velocity
    
    # Apply bounds behavior
    bounded_value = apply_bounds_behavior(proposed_value, state.valid_range, state.bounds_behavior)
    
    # Update optimization history
    optimization_step = %{
      timestamp: DateTime.utc_now(),
      gradient: new_gradient,
      smoothed_gradient: smoothed_gradient,
      velocity: new_velocity,
      old_value: state.current_value,
      new_value: bounded_value,
      learning_rate: state.learning_rate
    }
    
    updated_state = %{state |
      gradient_estimate: smoothed_gradient,
      velocity: new_velocity,
      optimization_history: [optimization_step | state.optimization_history] |> Enum.take(100)
    }
    
    # Coordinate value change if significant
    if abs(bounded_value - state.current_value) > (state.learning_rate * 0.1) do
      coordinate_value_change(bounded_value, :gradient_optimization, generate_coordination_id(), updated_state)
    else
      {:ok, updated_state}
    end
  end
  
  defp apply_bounds_behavior(value, {min_val, max_val}, behavior) do
    case behavior do
      :clamp ->
        value |> max(min_val) |> min(max_val)
        
      :wrap ->
        range = max_val - min_val
        if value < min_val do
          max_val - rem(min_val - value, range)
        else if value > max_val do
          min_val + rem(value - max_val, range)
        else
          value
        end
        
      :reject ->
        if value >= min_val and value <= max_val do
          value
        else
          raise "Value #{value} out of bounds [#{min_val}, #{max_val}]"
        end
    end
  end
end

defmodule DSPEx.Variables.CognitiveChoice do
  @moduledoc """
  Cognitive variable for categorical choices with intelligent selection
  using economic mechanisms and multi-criteria optimization.
  """
  
  use DSPEx.Variables.CognitiveVariable
  
  defstruct [
    __base__: DSPEx.Variables.CognitiveVariable,
    
    # Choice-specific properties
    :choice_weights,                # Weights for each choice option
    :selection_history,             # History of selections and their outcomes
    :choice_performance_matrix,     # Performance matrix for each choice
    :multi_criteria_weights,        # Weights for multi-criteria optimization
    :exploration_rate,              # Rate of exploration vs exploitation
    :choice_costs,                  # Costs associated with each choice
    :choice_constraints,            # Constraints between choices
    :reputation_per_choice,         # Reputation score for each choice
    :bandits_state                  # Multi-armed bandits state for optimization
  ]
  
  def mount(agent, opts) do
    {:ok, base_state} = super(agent, opts)
    
    choices = base_state.valid_range || []
    
    choice_config = %{
      choice_weights: initialize_choice_weights(choices, opts),
      selection_history: [],
      choice_performance_matrix: initialize_performance_matrix(choices),
      multi_criteria_weights: Keyword.get(opts, :criteria_weights, %{performance: 0.5, cost: 0.3, reliability: 0.2}),
      exploration_rate: Keyword.get(opts, :exploration_rate, 0.1),
      choice_costs: Keyword.get(opts, :choice_costs, %{}),
      choice_constraints: Keyword.get(opts, :choice_constraints, %{}),
      reputation_per_choice: initialize_choice_reputation(choices),
      bandits_state: initialize_bandits_state(choices)
    }
    
    enhanced_state = Map.merge(base_state, choice_config)
    
    {:ok, enhanced_state}
  end
  
  # Handle choice optimization using multi-armed bandits
  def handle_signal({:choice_feedback, choice, outcome}, state) do
    # Update bandits state with outcome
    updated_bandits = update_bandits_state(state.bandits_state, choice, outcome)
    
    # Update performance matrix
    updated_matrix = update_performance_matrix(state.choice_performance_matrix, choice, outcome)
    
    # Update reputation for the choice
    updated_reputation = update_choice_reputation(state.reputation_per_choice, choice, outcome)
    
    # Record selection history
    selection_record = %{
      timestamp: DateTime.utc_now(),
      choice: choice,
      outcome: outcome,
      exploration: Map.get(outcome, :was_exploration, false)
    }
    
    updated_state = %{state |
      bandits_state: updated_bandits,
      choice_performance_matrix: updated_matrix,
      reputation_per_choice: updated_reputation,
      selection_history: [selection_record | state.selection_history] |> Enum.take(200)
    }
    
    # Determine if we should change the current choice
    optimal_choice = calculate_optimal_choice(updated_state)
    
    if optimal_choice != state.current_value and should_switch_choice?(optimal_choice, state.current_value, updated_state) do
      Logger.info("Cognitive choice #{state.name} switching: #{state.current_value} -> #{optimal_choice}")
      coordinate_value_change(optimal_choice, :optimization_system, generate_coordination_id(), updated_state)
    else
      {:ok, updated_state}
    end
  end
  
  # Economic coordination for choice selection
  def handle_signal({:economic_choice_auction, auction_data}, state) do
    if state.mabeam_config.economic_coordination do
      # Participate in auction for choice selection
      bid = calculate_choice_bid(auction_data, state)
      
      case DSPEx.Foundation.Bridge.submit_auction_bid(auction_data.auction_ref, bid) do
        {:ok, bid_ref} ->
          Logger.info("Submitted bid for choice auction: #{inspect(bid)}")
          {:ok, state}
          
        {:error, reason} ->
          Logger.warning("Failed to submit choice auction bid: #{inspect(reason)}")
          {:ok, state}
      end
    else
      {:ok, state}
    end
  end
  
  defp calculate_optimal_choice(state) do
    choices = state.valid_range
    
    # Calculate multi-criteria scores for each choice
    choice_scores = Enum.map(choices, fn choice ->
      performance_score = get_choice_performance_score(choice, state.choice_performance_matrix)
      cost_score = get_choice_cost_score(choice, state.choice_costs)
      reliability_score = get_choice_reliability_score(choice, state.reputation_per_choice)
      
      # Combine scores using weights
      combined_score = 
        performance_score * state.multi_criteria_weights.performance +
        cost_score * state.multi_criteria_weights.cost +
        reliability_score * state.multi_criteria_weights.reliability
        
      {choice, combined_score}
    end)
    
    # Apply exploration vs exploitation
    if :rand.uniform() < state.exploration_rate do
      # Exploration: choose randomly
      Enum.random(choices)
    else
      # Exploitation: choose best score
      choice_scores
      |> Enum.max_by(fn {_choice, score} -> score end)
      |> elem(0)
    end
  end
  
  defp calculate_choice_bid(auction_data, state) do
    choice = auction_data.choice
    performance_score = get_choice_performance_score(choice, state.choice_performance_matrix)
    reputation_score = get_choice_reliability_score(choice, state.reputation_per_choice)
    
    # Base bid on expected value
    base_value = performance_score * reputation_score
    
    # Adjust based on current reputation
    reputation_multiplier = state.economic_reputation
    
    # Apply bid strategy
    final_bid = case state.bid_strategy do
      :conservative -> base_value * reputation_multiplier * 0.8
      :aggressive -> base_value * reputation_multiplier * 1.2
      :balanced -> base_value * reputation_multiplier
    end
    
    %{
      amount: final_bid,
      choice: choice,
      confidence: performance_score,
      reputation: reputation_score
    }
  end
  
  # Utility functions for choice management
  defp initialize_choice_weights(choices, opts) do
    initial_weights = Keyword.get(opts, :initial_weights, %{})
    
    Enum.reduce(choices, %{}, fn choice, acc ->
      weight = Map.get(initial_weights, choice, 1.0)
      Map.put(acc, choice, weight)
    end)
  end
  
  defp initialize_performance_matrix(choices) do
    Enum.reduce(choices, %{}, fn choice, acc ->
      Map.put(acc, choice, %{
        total_selections: 0,
        successful_outcomes: 0,
        average_performance: 0.0,
        performance_variance: 0.0
      })
    end)
  end
  
  defp initialize_choice_reputation(choices) do
    Enum.reduce(choices, %{}, fn choice, acc ->
      Map.put(acc, choice, 1.0)  # Start with neutral reputation
    end)
  end
  
  defp initialize_bandits_state(choices) do
    # Initialize multi-armed bandits state for each choice
    Enum.reduce(choices, %{}, fn choice, acc ->
      Map.put(acc, choice, %{
        total_selections: 0,
        total_reward: 0.0,
        average_reward: 0.0,
        confidence_bound: Float.max()
      })
    end)
  end
end

defmodule DSPEx.Variables.CognitiveAgentTeam do
  @moduledoc """
  Revolutionary cognitive variable that manages dynamic agent teams,
  automatically optimizing team composition based on performance and
  using MABEAM coordination for distributed team management.
  """
  
  use DSPEx.Variables.CognitiveVariable
  
  defstruct [
    __base__: DSPEx.Variables.CognitiveVariable,
    
    # Team-specific properties
    :available_agents,              # Pool of available agents
    :current_team,                  # Current team composition
    :team_performance_history,      # Performance history for different team compositions
    :agent_compatibility_matrix,    # Compatibility scores between agents
    :team_constraints,              # Constraints on team composition
    :team_roles,                    # Required roles in the team
    :role_requirements,             # Requirements for each role
    :team_optimization_strategy,    # Strategy for team optimization
    :workload_distribution,         # Current workload distribution
    :collaboration_metrics,         # Metrics about team collaboration
    :dynamic_scaling_config         # Configuration for dynamic team scaling
  ]
  
  def mount(agent, opts) do
    {:ok, base_state} = super(agent, opts)
    
    team_config = %{
      available_agents: Keyword.get(opts, :available_agents, []),
      current_team: base_state.current_value || [],
      team_performance_history: [],
      agent_compatibility_matrix: initialize_compatibility_matrix(opts),
      team_constraints: Keyword.get(opts, :team_constraints, %{}),
      team_roles: Keyword.get(opts, :team_roles, []),
      role_requirements: Keyword.get(opts, :role_requirements, %{}),
      team_optimization_strategy: Keyword.get(opts, :optimization_strategy, :performance_based),
      workload_distribution: %{},
      collaboration_metrics: %{},
      dynamic_scaling_config: Keyword.get(opts, :dynamic_scaling, %{enabled: true, min_size: 1, max_size: 10})
    }
    
    enhanced_state = Map.merge(base_state, team_config)
    
    # Register team for coordination
    register_team_for_coordination(enhanced_state)
    
    {:ok, enhanced_state}
  end
  
  # Handle team performance feedback
  def handle_signal({:team_performance_feedback, performance_data}, state) do
    # Record team performance
    performance_record = %{
      timestamp: DateTime.utc_now(),
      team_composition: state.current_team,
      performance_metrics: performance_data,
      workload_distribution: state.workload_distribution,
      collaboration_quality: calculate_collaboration_quality(performance_data)
    }
    
    updated_history = [performance_record | state.team_performance_history] |> Enum.take(50)
    
    updated_state = %{state | team_performance_history: updated_history}
    
    # Analyze if team optimization is needed
    case analyze_team_optimization_need(performance_data, updated_state) do
      {:optimize, optimization_params} ->
        optimize_team_composition(optimization_params, updated_state)
        
      :no_change_needed ->
        {:ok, updated_state}
    end
  end
  
  # Handle dynamic scaling requests
  def handle_signal({:scaling_request, scaling_data}, state) do
    case scaling_data do
      %{type: :scale_up, reason: reason, target_size: target_size} ->
        handle_scale_up(reason, target_size, state)
        
      %{type: :scale_down, reason: reason, target_size: target_size} ->
        handle_scale_down(reason, target_size, state)
        
      %{type: :rebalance, workload_info: workload} ->
        handle_team_rebalance(workload, state)
        
      _ ->
        Logger.warning("Unknown scaling request: #{inspect(scaling_data)}")
        {:ok, state}
    end
  end
  
  # Handle agent availability changes
  def handle_signal({:agent_availability_change, availability_data}, state) do
    case availability_data do
      %{agent_id: agent_id, status: :available} ->
        add_available_agent(agent_id, state)
        
      %{agent_id: agent_id, status: :unavailable} ->
        remove_agent_from_team(agent_id, state)
        
      %{agent_id: agent_id, status: :degraded, performance_impact: impact} ->
        handle_agent_degradation(agent_id, impact, state)
        
      _ ->
        {:ok, state}
    end
  end
  
  defp optimize_team_composition(optimization_params, state) do
    current_team = state.current_team
    
    # Calculate optimal team using different strategies
    optimal_team = case state.team_optimization_strategy do
      :performance_based ->
        optimize_for_performance(optimization_params, state)
        
      :cost_based ->
        optimize_for_cost(optimization_params, state)
        
      :balanced ->
        optimize_balanced(optimization_params, state)
        
      :capability_based ->
        optimize_for_capabilities(optimization_params, state)
    end
    
    if optimal_team != current_team do
      Logger.info("Team #{state.name} optimizing composition: #{inspect(current_team)} -> #{inspect(optimal_team)}")
      
      # Use MABEAM coordination for team transitions
      coordinate_team_transition(current_team, optimal_team, state)
    else
      {:ok, state}
    end
  end
  
  defp coordinate_team_transition(old_team, new_team, state) do
    # Create transition plan
    transition_plan = %{
      type: :team_composition_change,
      variable_id: state.name,
      old_team: old_team,
      new_team: new_team,
      agents_to_add: new_team -- old_team,
      agents_to_remove: old_team -- new_team,
      transition_strategy: :gradual,  # or :immediate
      coordination_timeout: 45_000
    }
    
    # Use MABEAM consensus for team transition coordination
    all_affected_agents = Enum.uniq(old_team ++ new_team)
    
    case DSPEx.Foundation.Bridge.start_consensus(all_affected_agents, transition_plan, 45_000) do
      {:ok, consensus_ref} ->
        transition_state = %{state | 
          lock_state: {:team_transition_pending, consensus_ref},
          coordination_history: [
            {DateTime.utc_now(), :team_transition_started, consensus_ref} | state.coordination_history
          ]
        }
        
        # Monitor transition result
        Task.start(fn -> 
          monitor_team_transition_result(consensus_ref, new_team, self())
        end)
        
        {:ok, transition_state}
        
      {:error, reason} ->
        Logger.warning("Team transition consensus failed: #{inspect(reason)}")
        
        # Fallback to immediate transition
        execute_immediate_team_transition(old_team, new_team, state)
    end
  end
  
  defp optimize_for_performance(optimization_params, state) do
    # Use historical performance data to optimize team composition
    performance_history = state.team_performance_history
    
    if length(performance_history) >= 3 do
      # Analyze which agents contribute most to performance
      agent_performance_contributions = calculate_agent_performance_contributions(performance_history)
      
      # Select top performing agents
      sorted_agents = Enum.sort_by(agent_performance_contributions, fn {_agent, contribution} -> 
        -contribution 
      end)
      
      # Build optimal team within constraints
      build_optimal_team(sorted_agents, state.team_constraints, state.team_roles)
    else
      # Not enough data, keep current team
      state.current_team
    end
  end
  
  defp optimize_for_cost(optimization_params, state) do
    # Optimize team for cost efficiency while maintaining minimum performance
    available_agents = state.available_agents
    min_performance = Map.get(optimization_params, :min_performance, 0.8)
    
    # Calculate cost-performance ratio for each agent
    agent_efficiency = Enum.map(available_agents, fn agent_id ->
      cost = get_agent_cost(agent_id)
      performance = get_agent_performance(agent_id, state.team_performance_history)
      efficiency = if cost > 0, do: performance / cost, else: 0.0
      
      {agent_id, efficiency}
    end)
    
    # Select most efficient agents that meet performance requirements
    select_efficient_team(agent_efficiency, min_performance, state)
  end
  
  defp handle_scale_up(reason, target_size, state) do
    current_size = length(state.current_team)
    
    if target_size > current_size and state.dynamic_scaling_config.enabled do
      agents_needed = target_size - current_size
      max_size = state.dynamic_scaling_config.max_size
      
      actual_target = min(target_size, max_size)
      actual_agents_needed = actual_target - current_size
      
      if actual_agents_needed > 0 do
        # Find best available agents to add
        candidates = state.available_agents -- state.current_team
        
        selected_agents = select_agents_for_scaling(candidates, actual_agents_needed, state)
        
        new_team = state.current_team ++ selected_agents
        
        Logger.info("Scaling up team #{state.name}: #{current_size} -> #{length(new_team)} (reason: #{reason})")
        
        coordinate_team_transition(state.current_team, new_team, state)
      else
        Logger.info("Scale up requested but already at maximum size")
        {:ok, state}
      end
    else
      {:ok, state}
    end
  end
  
  defp handle_scale_down(reason, target_size, state) do
    current_size = length(state.current_team)
    
    if target_size < current_size and state.dynamic_scaling_config.enabled do
      min_size = state.dynamic_scaling_config.min_size
      actual_target = max(target_size, min_size)
      agents_to_remove = current_size - actual_target
      
      if agents_to_remove > 0 do
        # Select agents to remove (least performing or most expensive)
        agents_to_keep = select_agents_to_keep(state.current_team, actual_target, state)
        
        Logger.info("Scaling down team #{state.name}: #{current_size} -> #{length(agents_to_keep)} (reason: #{reason})")
        
        coordinate_team_transition(state.current_team, agents_to_keep, state)
      else
        Logger.info("Scale down requested but already at minimum size")
        {:ok, state}
      end
    else
      {:ok, state}
    end
  end
  
  # Utility functions for team management
  defp calculate_agent_performance_contributions(performance_history) do
    # Analyze historical data to determine each agent's contribution to team performance
    agent_contributions = %{}
    
    Enum.reduce(performance_history, agent_contributions, fn record, acc ->
      team = record.team_composition
      performance = Map.get(record.performance_metrics, :overall_performance, 0.0)
      
      # Distribute performance credit among team members
      credit_per_agent = performance / length(team)
      
      Enum.reduce(team, acc, fn agent_id, agent_acc ->
        current_contribution = Map.get(agent_acc, agent_id, 0.0)
        Map.put(agent_acc, agent_id, current_contribution + credit_per_agent)
      end)
    end)
  end
  
  defp build_optimal_team(sorted_agents, constraints, required_roles) do
    # Build team respecting constraints and role requirements
    # This is a simplified implementation - production would use more sophisticated optimization
    
    max_size = Map.get(constraints, :max_size, 10)
    min_size = Map.get(constraints, :min_size, 1)
    
    # Ensure role coverage first
    team_with_roles = ensure_role_coverage(sorted_agents, required_roles)
    
    # Add additional agents up to max size
    remaining_slots = max_size - length(team_with_roles)
    
    additional_agents = sorted_agents
    |> Enum.map(fn {agent_id, _contribution} -> agent_id end)
    |> Enum.reject(fn agent_id -> agent_id in team_with_roles end)
    |> Enum.take(remaining_slots)
    
    final_team = team_with_roles ++ additional_agents
    
    # Ensure minimum size
    if length(final_team) >= min_size do
      final_team
    else
      # Add more agents to reach minimum size
      all_available = sorted_agents |> Enum.map(fn {agent_id, _} -> agent_id end)
      needed = min_size - length(final_team)
      
      extra_agents = all_available
      |> Enum.reject(fn agent_id -> agent_id in final_team end)
      |> Enum.take(needed)
      
      final_team ++ extra_agents
    end
  end
  
  defp ensure_role_coverage(sorted_agents, required_roles) do
    # Ensure each required role is covered by at least one agent
    # This would integrate with agent capability discovery
    
    Enum.reduce(required_roles, [], fn role, team_acc ->
      case find_agent_for_role(sorted_agents, role, team_acc) do
        {:ok, agent_id} -> [agent_id | team_acc]
        :not_found -> team_acc  # Role cannot be filled
      end
    end)
    |> Enum.uniq()
  end
  
  defp find_agent_for_role(sorted_agents, role, existing_team) do
    # Find an agent capable of fulfilling the role that's not already on the team
    candidate = sorted_agents
    |> Enum.find(fn {agent_id, _contribution} ->
      agent_id not in existing_team and agent_has_capability(agent_id, role)
    end)
    
    case candidate do
      {agent_id, _contribution} -> {:ok, agent_id}
      nil -> :not_found
    end
  end
  
  defp agent_has_capability(agent_id, role) do
    # This would integrate with the MABEAM capability discovery system
    case DSPEx.Foundation.Bridge.get_agent_capabilities(agent_id) do
      {:ok, capabilities} -> role in capabilities
      {:error, _reason} -> false
    end
  end
end

defmodule DSPEx.Program do
  @moduledoc """
  DSPEx program with revolutionary Cognitive Variables integration
  """
  
  use ElixirML.Resource
  
  defstruct [
    :signature,
    :cognitive_variables,           # Cognitive Variables coordination
    :variable_coordinator,          # Agent that coordinates all variables
    :optimization_agent,            # Agent for program optimization
    :performance_monitor,           # Agent for performance monitoring
    :config,
    :metadata
  ]
  
  def new(signature, opts \\ []) do
    # Create cognitive variables for program parameters
    cognitive_variables = create_program_variables(signature, opts)
    
    # Start variable coordinator agent
    {:ok, coordinator} = DSPEx.Agents.VariableCoordinator.start_link([
      variables: cognitive_variables,
      coordination_scope: :cluster
    ])
    
    # Start optimization agent
    {:ok, optimizer} = DSPEx.Agents.OptimizationAgent.start_link([
      variables: cognitive_variables,
      coordinator: coordinator
    ])
    
    # Start performance monitor
    {:ok, monitor} = DSPEx.Agents.PerformanceMonitor.start_link([
      coordinator: coordinator,
      optimizer: optimizer
    ])
    
    %__MODULE__{
      signature: signature,
      cognitive_variables: cognitive_variables,
      variable_coordinator: coordinator,
      optimization_agent: optimizer,
      performance_monitor: monitor,
      config: Keyword.get(opts, :config, %{}),
      metadata: %{created_at: DateTime.utc_now()}
    }
  end
  
  def optimize(program, training_data, opts \\ []) do
    # Revolutionary optimization using cognitive variables
    optimization_request = %{
      type: :program_optimization,
      training_data: training_data,
      optimization_strategy: Keyword.get(opts, :strategy, :cognitive_variables),
      target_metrics: Keyword.get(opts, :target_metrics, [:accuracy, :cost]),
      max_iterations: Keyword.get(opts, :max_iterations, 100)
    }
    
    # Send optimization request to optimization agent
    case Jido.Signal.send(program.optimization_agent, {:optimize_program, optimization_request}) do
      :ok ->
        # Monitor optimization progress
        monitor_optimization_progress(program, opts)
        
      {:error, reason} ->
        {:error, {:optimization_failed, reason}}
    end
  end
  
  defp create_program_variables(signature, opts) do
    base_variables = [
      # LLM parameters as cognitive variables
      {:temperature, DSPEx.Variables.CognitiveFloat, [
        range: {0.0, 2.0},
        default: 0.7,
        adaptation_strategy: :performance_feedback,
        coordination_scope: :cluster
      ]},
      
      {:max_tokens, DSPEx.Variables.CognitiveChoice, [
        choices: [100, 250, 500, 1000, 2000, 4000],
        default: 1000,
        adaptation_strategy: :economic,
        coordination_scope: :global
      ]},
      
      {:model_selection, DSPEx.Variables.CognitiveChoice, [
        choices: [:gpt_4, :claude_3, :gemini_pro],
        default: :gpt_4,
        adaptation_strategy: :performance_feedback,
        economic_coordination: true
      ]},
      
      {:reasoning_agents, DSPEx.Variables.CognitiveAgentTeam, [
        available_agents: discover_available_reasoning_agents(),
        team_roles: [:chain_of_thought, :critic, :synthesizer],
        optimization_strategy: :balanced,
        dynamic_scaling: %{enabled: true, min_size: 2, max_size: 6}
      ]}
    ]
    
    # Add signature-specific variables
    signature_variables = extract_variables_from_signature(signature, opts)
    
    # Start all cognitive variable agents
    Enum.map(base_variables ++ signature_variables, fn {name, module, config} ->
      {:ok, agent_pid} = module.start_link([name: name] ++ config)
      {name, agent_pid}
    end)
    |> Map.new()
  end
end