# lib/dspex/agent.ex
defmodule DSPEx.Agent do
  @moduledoc """
  Bridge that converts DSPEx programs into Jido agents.
  Enables DSPEx programs to participate in multi-agent coordination.
  """
  
  defmacro __using__(opts) do
    quote do
      use Jido.Agent
      use DSPEx.Program, unquote(opts)
      
      # Enhanced agent initialization with DSPEx capabilities
      def init(opts) do
        # Initialize Jido agent
        {:ok, jido_state} = super(opts)
        
        # Initialize DSPEx program
        program_config = Keyword.get(opts, :program_config, %{})
        dspex_state = DSPEx.Program.init_state(program_config)
        
        # Merge states
        state = Map.merge(jido_state, %{
          dspex: dspex_state,
          signature: Keyword.get(opts, :signature),
          teleprompter: Keyword.get(opts, :teleprompter),
          ml_variables: extract_ml_variables(opts)
        })
        
        # Register ML variables with MABEAM if enabled
        if opts[:mabeam_coordination] do
          register_ml_variables_with_mabeam(state.ml_variables)
        end
        
        {:ok, state}
      end
      
      # Enhanced predict with agent integration
      def predict(input, context \\ %{}) do
        # Get current variable values from MABEAM if coordinated
        resolved_variables = if context[:use_mabeam_variables] do
          resolve_mabeam_variables(context[:agent_id])
        else
          context[:variables] || %{}
        end
        
        # Execute prediction with current configuration
        enhanced_context = Map.merge(context, %{
          variables: resolved_variables,
          agent_id: context[:agent_id],
          coordination_enabled: true
        })
        
        DSPEx.Program.predict(__MODULE__, input, enhanced_context)
      end
      
      # Jido action for prediction
      def handle_instruction(%Jido.Instruction{action: :predict, params: params}, state) do
        input = params[:input]
        context = Map.merge(params[:context] || %{}, %{agent_id: state.agent_id})
        
        case predict(input, context) do
          {:ok, result} ->
            # Track prediction metrics
            Foundation.Telemetry.AgentTelemetry.track_agent_action(
              state.agent_id,
              :predict,
              result.duration || 0,
              {:ok, result}
            )
            
            # Publish prediction signal
            signal = %JidoSignal{
              id: JidoSignal.ID.generate(),
              type: "dspex.prediction.completed",
              source: state.agent_id,
              data: %{
                input: input,
                output: result.output,
                metadata: result.metadata
              }
            }
            
            JidoSignal.Bus.publish(signal)
            
            {:reply, {:ok, result}, state}
          
          {:error, reason} ->
            Foundation.Telemetry.AgentTelemetry.track_agent_action(
              state.agent_id,
              :predict,
              0,
              {:error, reason}
            )
            
            {:reply, {:error, reason}, state}
        end
      end
      
      # Handle variable updates from MABEAM
      def handle_info({:mabeam_variable_update, variable_id, new_value}, state) do
        # Update DSPEx program configuration
        new_dspex_state = DSPEx.Program.update_variable(
          state.dspex,
          variable_id,
          new_value
        )
        
        new_state = %{state | dspex: new_dspex_state}
        
        # Notify about configuration change
        Foundation.Services.EventStore.publish(%Foundation.Types.Event{
          type: :dspex_variable_updated,
          source: state.agent_id,
          data: %{variable_id: variable_id, new_value: new_value}
        })
        
        {:noreply, new_state}
      end
      
      # Extract ML variables from program configuration
      defp extract_ml_variables(opts) do
        signature = opts[:signature]
        program_variables = opts[:variables] || []
        
        # Combine signature variables with explicit variables
        signature_variables = if signature do
          DSPEx.Signature.extract_variables(signature)
        else
          []
        end
        
        signature_variables ++ program_variables
      end
      
      # Register ML variables with MABEAM orchestrator
      defp register_ml_variables_with_mabeam(variables) do
        Enum.each(variables, fn variable ->
          MABEAM.Orchestrator.register_variable(variable)
        end)
      end
      
      # Resolve current MABEAM variable values
      defp resolve_mabeam_variables(agent_id) do
        agent_variables = MABEAM.Orchestrator.get_agent_variables(agent_id)
        
        Enum.into(agent_variables, %{}, fn variable_id ->
          value = MABEAM.Orchestrator.get_variable_value(variable_id)
          {variable_id, value}
        end)
      end
    end
  end
end

# Example usage
defmodule CoderAgent do
  use DSPEx.Agent,
    signature: CoderSignature,
    teleprompter: DSPEx.Teleprompter.SIMBA,
    mabeam_coordination: true,
    variables: [
      ElixirML.Variable.MLTypes.temperature(:temperature),
      ElixirML.Variable.MLTypes.max_tokens(:max_tokens),
      ElixirML.Variable.MLTypes.reasoning_strategy(:reasoning)
    ]
  
  # DSPEx program implementation
  def forward(inputs, context) do
    # Use coordinated variables
    temperature = context.variables[:temperature] || 0.7
    max_tokens = context.variables[:max_tokens] || 1000
    
    # ML logic here
    {:ok, generate_code(inputs, temperature, max_tokens)}
  end
end

# lib/dspex/variable/mabeam_bridge.ex
defmodule DSPEx.Variable.MABEAMBridge do
  @moduledoc """
  Bridge between DSPEx/ElixirML variables and MABEAM orchestration.
  Enables ML variables to participate in multi-agent coordination.
  """
  
  def convert_ml_variable_to_mabeam(ml_variable) do
    # Convert ElixirML.Variable to MABEAM.Variable
    %MABEAM.Variable{
      id: ml_variable.id,
      type: convert_variable_type(ml_variable.type),
      constraints: convert_constraints(ml_variable.constraints),
      coordination_strategy: determine_coordination_strategy(ml_variable),
      agents: [],  # Will be populated when agents register
      metadata: %{
        ml_type: ml_variable.type,
        original_variable: ml_variable,
        optimization_hint: ml_variable.metadata[:optimization_hint]
      }
    }
  end
  
  def create_ml_variable_space_for_agents(agents, ml_variables) do
    # Create a MABEAM multi-agent space for ML variables
    mabeam_variables = Enum.map(ml_variables, &convert_ml_variable_to_mabeam/1)
    
    space = %MABEAM.Variable.MultiAgentSpace{
      id: :ml_coordination_space,
      name: "ML Variable Coordination Space",
      agents: create_agent_configs(agents),
      orchestration_variables: index_by_id(mabeam_variables),
      coordination_graph: build_ml_coordination_graph(agents, mabeam_variables),
      performance_metrics: %{},
      adaptation_history: []
    }
    
    # Register space with MABEAM orchestrator
    MABEAM.Orchestrator.register_space(space)
    
    space
  end
  
  def coordinate_ml_variables(space, coordination_context) do
    # Coordinate ML variables across agents
    results = Enum.map(space.orchestration_variables, fn {var_id, variable} ->
      case MABEAM.Coordination.coordinate_variable(variable, coordination_context) do
        {:ok, coordination_result} ->
          # Apply coordination result to all participating agents
          apply_coordination_result(variable, coordination_result)
          {var_id, {:ok, coordination_result}}
        
        {:error, reason} ->
          {var_id, {:error, reason}}
      end
    end)
    
    # Update space with coordination results
    update_space_with_results(space, results)
  end
  
  defp convert_variable_type(:float), do: :continuous
  defp convert_variable_type(:integer), do: :discrete
  defp convert_variable_type(:choice), do: :categorical
  defp convert_variable_type(:module), do: :agent_selection
  defp convert_variable_type(type), do: type
  
  defp convert_constraints(constraints) do
    Enum.map(constraints, fn
      {:range, {min, max}} -> {:value_range, min, max}
      {:options, options} -> {:allowed_values, options}
      {:min, min} -> {:minimum_value, min}
      {:max, max} -> {:maximum_value, max}
      constraint -> constraint
    end)
  end
  
  defp determine_coordination_strategy(ml_variable) do
    case ml_variable.type do
      :float -> :weighted_consensus
      :integer -> :voting
      :choice -> :auction
      :module -> :capability_based_selection
      _ -> :simple_coordination
    end
  end
  
  defp create_agent_configs(agents) do
    Enum.into(agents, %{}, fn agent_id ->
      {agent_id, %{
        module: agent_id,
        supervision_strategy: :one_for_one,
        resource_requirements: %{memory: 100, cpu: 0.1},
        communication_interfaces: [:jido_signal],
        local_variable_space: %{},
        role: :ml_worker,
        status: :active
      }}
    end)
  end
  
  defp build_ml_coordination_graph(agents, variables) do
    # Build coordination graph based on variable dependencies
    nodes = agents
    
    edges = for agent1 <- agents,
                agent2 <- agents,
                agent1 != agent2,
                shared_variable?(agent1, agent2, variables) do
      {agent1, agent2, :variable_coordination}
    end
    
    %{
      nodes: nodes,
      edges: edges,
      topology: :mesh  # For ML coordination, mesh is often optimal
    }
  end
  
  defp shared_variable?(agent1, agent2, variables) do
    # Check if agents share any variables
    agent1_vars = get_agent_variables(agent1, variables)
    agent2_vars = get_agent_variables(agent2, variables)
    
    length(agent1_vars -- agent2_vars) != length(agent1_vars)
  end
  
  defp get_agent_variables(agent_id, variables) do
    # Get variables that affect this agent
    Enum.filter(variables, fn variable ->
      agent_id in (variable.metadata[:affecting_agents] || [])
    end)
  end
  
  defp apply_coordination_result(variable, coordination_result) do
    # Apply coordination result to all agents using this variable
    Enum.each(coordination_result.agent_directives, fn directive ->
      case directive.action do
        :update_variable ->
          send_variable_update(directive.agent, variable.id, directive.parameters.new_value)
        
        :reconfigure ->
          send_reconfiguration(directive.agent, directive.parameters)
        
        _ ->
          send_generic_directive(directive.agent, directive)
      end
    end)
  end
  
  defp send_variable_update(agent_id, variable_id, new_value) do
    case Foundation.ProcessRegistry.lookup(agent_id) do
      {:ok, pid} ->
        send(pid, {:mabeam_variable_update, variable_id, new_value})
      
      {:error, :not_found} ->
        {:error, :agent_not_found}
    end
  end
end

# lib/dspex/variable/ml_coordination.ex
defmodule DSPEx.Variable.MLCoordination do
  @moduledoc """
  ML-specific coordination protocols for DSPEx variables.
  """
  
  def coordinate_temperature_across_agents(agents, context) do
    # Negotiate temperature based on task requirements
    preferences = Enum.map(agents, fn agent_id ->
      task_type = get_agent_task_type(agent_id)
      preferred_temp = get_preferred_temperature(task_type)
      weight = get_agent_coordination_weight(agent_id)
      
      {agent_id, preferred_temp, weight}
    end)
    
    # Calculate weighted consensus
    total_weight = Enum.sum(Enum.map(preferences, fn {_, _, weight} -> weight end))
    
    consensus_temp = preferences
    |> Enum.map(fn {_, temp, weight} -> temp * weight end)
    |> Enum.sum()
    |> Kernel./(total_weight)
    
    # Ensure within valid range
    final_temp = max(0.0, min(2.0, consensus_temp))
    
    {:ok, %{
      coordinated_value: final_temp,
      agent_directives: Enum.map(agents, fn agent_id ->
        %{
          agent: agent_id,
          action: :update_variable,
          parameters: %{variable_id: :temperature, new_value: final_temp},
          priority: 1,
          timeout: 5000
        }
      end)
    }}
  end
  
  def coordinate_model_selection(agents, available_models, context) do
    # Coordinate model selection based on task requirements and resource constraints
    agent_requirements = Enum.map(agents, fn agent_id ->
      task_complexity = get_agent_task_complexity(agent_id)
      resource_budget = get_agent_resource_budget(agent_id)
      
      {agent_id, task_complexity, resource_budget}
    end)
    
    # Select optimal model for each agent
    model_assignments = Enum.map(agent_requirements, fn {agent_id, complexity, budget} ->
      optimal_model = select_optimal_model(available_models, complexity, budget)
      {agent_id, optimal_model}
    end)
    
    {:ok, %{
      coordinated_assignments: model_assignments,
      agent_directives: Enum.map(model_assignments, fn {agent_id, model} ->
        %{
          agent: agent_id,
          action: :update_variable,
          parameters: %{variable_id: :model, new_value: model},
          priority: 1,
          timeout: 10000
        }
      end)
    }}
  end
  
  def coordinate_reasoning_strategies(agents, context) do
    # Coordinate reasoning strategies to maximize team effectiveness
    task_distribution = analyze_task_distribution(agents, context)
    
    strategy_assignments = case task_distribution.pattern do
      :sequential ->
        # Assign complementary strategies for sequential tasks
        assign_sequential_strategies(agents)
      
      :parallel ->
        # Assign diverse strategies for parallel exploration
        assign_diverse_strategies(agents)
      
      :hierarchical ->
        # Assign strategies based on hierarchy
        assign_hierarchical_strategies(agents, task_distribution.hierarchy)
      
      _ ->
        # Default to balanced assignment
        assign_balanced_strategies(agents)
    end
    
    {:ok, %{
      coordinated_strategies: strategy_assignments,
      agent_directives: create_strategy_directives(strategy_assignments)
    }}
  end
  
  defp get_agent_task_type(agent_id) do
    # Get task type from agent metadata
    case Foundation.ProcessRegistry.get_metadata(agent_id) do
      {:ok, metadata} -> metadata[:task_type] || :general
      _ -> :general
    end
  end
  
  defp get_preferred_temperature(:creative), do: 1.2
  defp get_preferred_temperature(:analytical), do: 0.3
  defp get_preferred_temperature(:balanced), do: 0.7
  defp get_preferred_temperature(_), do: 0.7
  
  defp get_agent_coordination_weight(agent_id) do
    # Get coordination weight from agent configuration
    case Foundation.ProcessRegistry.get_metadata(agent_id) do
      {:ok, metadata} -> metadata[:coordination_weight] || 1.0
      _ -> 1.0
    end
  end
  
  defp select_optimal_model(available_models, complexity, budget) do
    # Select model based on complexity and budget constraints
    suitable_models = Enum.filter(available_models, fn model ->
      model.cost_per_token <= budget.max_cost_per_token &&
      model.capability_level >= complexity_to_capability_level(complexity)
    end)
    
    # Choose the most cost-effective suitable model
    Enum.min_by(suitable_models, & &1.cost_per_token, fn -> List.first(available_models) end)
  end
  
  defp complexity_to_capability_level(:simple), do: 1
  defp complexity_to_capability_level(:medium), do: 2
  defp complexity_to_capability_level(:complex), do: 3
  defp complexity_to_capability_level(:expert), do: 4
  defp complexity_to_capability_level(_), do: 2
end

# lib/dspex/teleprompter/multi_agent.ex
defmodule DSPEx.Teleprompter.MultiAgent do
  @moduledoc """
  Multi-agent teleprompters that optimize entire agent teams.
  Extends SIMBA, BEACON, and other algorithms to work across agent boundaries.
  """
  
  def simba(agent_team, training_data, metric_fn, opts \\ []) do
    # Multi-agent SIMBA optimization
    generations = opts[:generations] || 20
    mutation_strategies = opts[:mutation_strategies] || [:parameter_mutation, :agent_selection]
    
    # Initialize multi-agent configuration
    initial_config = create_initial_team_config(agent_team)
    
    # Track best configuration
    best_config = initial_config
    best_score = evaluate_team_configuration(agent_team, initial_config, training_data, metric_fn)
    
    # Evolution loop
    for generation <- 1..generations do
      # Generate candidate configurations
      candidates = generate_candidate_configurations(
        best_config,
        mutation_strategies,
        opts[:candidate_count] || 10
      )
      
      # Evaluate candidates in parallel
      candidate_scores = candidates
      |> Task.async_stream(fn config ->
        score = evaluate_team_configuration(agent_team, config, training_data, metric_fn)
        {config, score}
      end, max_concurrency: System.schedulers_online())
      |> Enum.map(fn {:ok, result} -> result end)
      
      # Select best candidate
      {candidate_config, candidate_score} = Enum.max_by(candidate_scores, fn {_, score} -> score end)
      
      if candidate_score > best_score do
        best_config = candidate_config
        best_score = candidate_score
        
        # Apply best configuration to team
        apply_team_configuration(agent_team, best_config)
        
        # Track progress
        Foundation.Telemetry.track_optimization_progress(
          :multi_agent_simba,
          generation,
          best_score
        )
      end
    end
    
    {:ok, %{
      optimized_config: best_config,
      final_score: best_score,
      agent_team: agent_team
    }}
  end
  
  def beacon(agent_team, training_data, metric_fn, opts \\ []) do
    # Multi-agent BEACON for rapid team composition optimization
    max_rounds = opts[:max_rounds] || 10
    
    # Start with random team composition
    current_composition = initialize_random_composition(agent_team, opts)
    best_composition = current_composition
    best_score = evaluate_team_composition(current_composition, training_data, metric_fn)
    
    for round <- 1..max_rounds do
      # Generate composition variations
      variations = generate_composition_variations(current_composition, opts)
      
      # Evaluate variations
      variation_scores = Enum.map(variations, fn composition ->
        score = evaluate_team_composition(composition, training_data, metric_fn)
        {composition, score}
      end)
      
      # Select best variation
      {best_variation, variation_score} = Enum.max_by(variation_scores, fn {_, score} -> score end)
      
      if variation_score > best_score do
        best_composition = best_variation
        best_score = variation_score
        current_composition = best_variation
        
        # Apply composition to team
        apply_team_composition(agent_team, best_composition)
      end
    end
    
    {:ok, %{
      optimized_composition: best_composition,
      final_score: best_score,
      optimization_rounds: max_rounds
    }}
  end
  
  def bootstrap_fewshot_team(agent_team, training_data, opts \\ []) do
    # Bootstrap few-shot optimization across multiple specialized agents
    
    # Partition training data by task type
    partitioned_data = partition_training_data_by_type(training_data)
    
    # Assign agents to data partitions based on capabilities
    agent_assignments = assign_agents_to_partitions(agent_team, partitioned_data)
    
    # Optimize each agent on its assigned data
    optimization_results = Enum.map(agent_assignments, fn {agent_id, data_partition} ->
      result = DSPEx.Teleprompter.BootstrapFewShot.optimize(
        agent_id,
        data_partition,
        opts
      )
      {agent_id, result}
    end)
    
    # Coordinate optimized agents
    coordinated_team = coordinate_optimized_agents(optimization_results)
    
    {:ok, %{
      agent_optimizations: optimization_results,
      coordinated_team: coordinated_team,
      team_configuration: extract_team_configuration(coordinated_team)
    }}
  end
  
  defp create_initial_team_config(agent_team) do
    Enum.into(agent_team, %{}, fn agent_id ->
      # Get current agent configuration
      case Foundation.ProcessRegistry.get_metadata(agent_id) do
        {:ok, metadata} ->
          variables = metadata[:coordination_variables] || []
          config = Enum.into(variables, %{}, fn var_id ->
            value = MABEAM.Orchestrator.get_variable_value(var_id)
            {var_id, value}
          end)
          {agent_id, config}
        
        _ ->
          {agent_id, %{}}
      end
    end)
  end
  
  defp generate_candidate_configurations(base_config, mutation_strategies, count) do
    for _ <- 1..count do
      mutate_team_configuration(base_config, mutation_strategies)
    end
  end
  
  defp mutate_team_configuration(config, strategies) do
    strategy = Enum.random(strategies)
    
    case strategy do
      :parameter_mutation ->
        mutate_parameters(config)
      
      :agent_selection ->
        mutate_agent_selection(config)
      
      :topology_mutation ->
        mutate_communication_topology(config)
      
      :resource_reallocation ->
        mutate_resource_allocation(config)
    end
  end
  
  defp evaluate_team_configuration(agent_team, config, training_data, metric_fn) do
    # Apply configuration to team
    apply_team_configuration(agent_team, config)
    
    # Run team on training data
    results = Enum.map(training_data, fn example ->
      team_result = execute_team_on_example(agent_team, example)
      metric_fn.(team_result, example)
    end)
    
    # Calculate average performance
    Enum.sum(results) / length(results)
  end
  
  defp apply_team_configuration(agent_team, config) do
    Enum.each(config, fn {agent_id, agent_config} ->
      Enum.each(agent_config, fn {variable_id, value} ->
        MABEAM.Orchestrator.update_variable_for_agent(agent_id, variable_id, value)
      end)
    end)
  end
  
  defp execute_team_on_example(agent_team, example) do
    # Coordinate team execution on a single example
    coordinator = List.first(agent_team)  # Use first agent as coordinator
    
    instruction = %Jido.Instruction{
      action: :coordinate_team_prediction,
      params: %{
        team: agent_team,
        input: example.input,
        expected_output: example.expected_output
      }
    }
    
    case Jido.Agent.cmd(coordinator, instruction) do
      {:ok, result} -> result
      {:error, _} -> %{success: false, output: nil}
    end
  end
end

# lib/dspex/teleprompter/coordination_aware.ex
defmodule DSPEx.Teleprompter.CoordinationAware do
  @moduledoc """
  Base teleprompter that is aware of agent coordination and variables.
  """
  
  defmacro __using__(_opts) do
    quote do
      # Get coordinated variables for optimization
      def get_coordination_variables(agent_id) do
        case Foundation.ProcessRegistry.get_metadata(agent_id) do
          {:ok, metadata} -> metadata[:coordination_variables] || []
          _ -> []
        end
      end
      
      # Update coordinated variables
      def update_coordination_variables(agent_id, variable_updates) do
        Enum.each(variable_updates, fn {variable_id, value} ->
          MABEAM.Orchestrator.update_variable_for_agent(agent_id, variable_id, value)
        end)
      end
      
      # Get team performance metrics
      def get_team_performance_metrics(agent_team) do
        Enum.into(agent_team, %{}, fn agent_id ->
          metrics = Foundation.Telemetry.AgentTelemetry.get_agent_metrics(agent_id)
          {agent_id, metrics}
        end)
      end
      
      # Coordinate optimization across team
      def coordinate_team_optimization(agent_team, optimization_fn) do
        # Create coordination context
        context = %{
          team: agent_team,
          coordination_variables: get_all_coordination_variables(agent_team),
          performance_baseline: get_team_performance_metrics(agent_team)
        }
        
        # Execute optimization with coordination
        result = optimization_fn.(context)
        
        # Apply coordination results
        if result.coordination_updates do
          apply_coordination_updates(agent_team, result.coordination_updates)
        end
        
        result
      end
      
      defp get_all_coordination_variables(agent_team) do
        agent_team
        |> Enum.flat_map(&get_coordination_variables/1)
        |> Enum.uniq()
      end
      
      defp apply_coordination_updates(agent_team, updates) do
        Enum.each(updates, fn {variable_id, value} ->
          Enum.each(agent_team, fn agent_id ->
            if variable_id in get_coordination_variables(agent_id) do
              update_coordination_variables(agent_id, [{variable_id, value}])
            end
          end)
        end)
      end
    end
  end
end

# lib/dspex/schema/signal_integration.ex
defmodule DSPEx.Schema.SignalIntegration do
  @moduledoc """
  Integration between DSPEx schemas and Jido signal system.
  Enables ML validation events and schema-driven signal routing.
  """
  
  def validate_with_signals(schema_module, data, context \\ %{}) do
    # Start validation with signal tracking
    validation_id = generate_validation_id()
    
    # Publish validation start signal
    publish_validation_signal(validation_id, :started, %{
      schema: schema_module,
      data_type: get_data_type(data),
      context: context
    })
    
    # Perform validation
    start_time = System.monotonic_time(:millisecond)
    
    result = case schema_module.validate(data) do
      {:ok, validated_data} ->
        duration = System.monotonic_time(:millisecond) - start_time
        
        # Publish success signal
        publish_validation_signal(validation_id, :success, %{
          schema: schema_module,
          duration: duration,
          validated_data: validated_data
        })
        
        {:ok, validated_data}
      
      {:error, errors} ->
        duration = System.monotonic_time(:millisecond) - start_time
        
        # Publish error signal
        publish_validation_signal(validation_id, :error, %{
          schema: schema_module,
          duration: duration,
          errors: errors
        })
        
        {:error, errors}
    end
    
    # Track validation metrics
    Foundation.Telemetry.track_schema_validation(
      schema_module,
      result,
      System.monotonic_time(:millisecond) - start_time
    )
    
    result
  end
  
  def setup_schema_signal_routing(schema_module, routing_rules) do
    # Setup signal routing based on validation outcomes
    Enum.each(routing_rules, fn {outcome, routing_config} ->
      JidoSignal.Router.add_route(
        "schema.validation.#{outcome}",
        create_schema_filter(schema_module),
        routing_config
      )
    end)
  end
  
  def create_ml_validation_pipeline(schemas, signal_handlers) do
    # Create a validation pipeline with signal-driven flow control
    pipeline_id = generate_pipeline_id()
    
    # Setup pipeline stages
    stages = Enum.with_index(schemas, fn schema, index ->
      stage_id = "#{pipeline_id}_stage_#{index}"
      
      %{
        id: stage_id,
        schema: schema,
        signal_handler: Map.get(signal_handlers, schema),
        next_stage: get_next_stage_id(schemas, index, pipeline_id)
      }
    end)
    
    # Register pipeline with signal router
    JidoSignal.Router.register_pipeline(pipeline_id, stages)
    
    {:ok, pipeline_id}
  end
  
  defp publish_validation_signal(validation_id, event_type, data) do
    signal = %JidoSignal{
      id: JidoSignal.ID.generate(),
      type: "schema.validation.#{event_type}",
      source: :dspex_schema,
      data: Map.merge(data, %{validation_id: validation_id}),
      timestamp: DateTime.utc_now()
    }
    
    JidoSignal.Bus.publish(signal)
  end
  
  defp generate_validation_id() do
    :crypto.strong_rand_bytes(8) |> Base.encode16(case: :lower)
  end
  
  defp generate_pipeline_id() do
    "ml_pipeline_" <> (:crypto.strong_rand_bytes(8) |> Base.encode16(case: :lower))
  end
  
  defp get_data_type(data) when is_map(data), do: :map
  defp get_data_type(data) when is_list(data), do: :list
  defp get_data_type(data) when is_binary(data), do: :string
  defp get_data_type(data) when is_number(data), do: :number
  defp get_data_type(_), do: :unknown
  
  defp create_schema_filter(schema_module) do
    fn signal ->
      signal.data[:schema] == schema_module
    end
  end
  
  defp get_next_stage_id(schemas, current_index, pipeline_id) do
    if current_index + 1 < length(schemas) do
      "#{pipeline_id}_stage_#{current_index + 1}"
    else
      :pipeline_complete
    end
  end
end

# lib/dspex/schema/ml_aware_validation.ex
defmodule DSPEx.Schema.MLAwareValidation do
  @moduledoc """
  ML-aware validation with agent coordination and variable integration.
  """
  
  def validate_with_coordination(schema_module, data, coordination_context) do
    # Get coordination variables that might affect validation
    coordination_vars = coordination_context[:variables] || %{}
    
    # Adjust validation parameters based on coordination
    validation_params = adjust_validation_params(schema_module, coordination_vars)
    
    # Perform coordinated validation
    case schema_module.validate(data, validation_params) do
      {:ok, validated_data} ->
        # Coordinate successful validation results
        coordinate_validation_success(
          schema_module,
          validated_data,
          coordination_context
        )
        
        {:ok, validated_data}
      
      {:error, errors} ->
        # Coordinate validation failures for team learning
        coordinate_validation_failure(
          schema_module,
          errors,
          coordination_context
        )
        
        {:error, errors}
    end
  end
  
  def setup_ml_validation_coordination(agent_team, schemas) do
    # Setup coordination for ML validation across agent team
    coordination_config = %{
      team: agent_team,
      schemas: schemas,
      coordination_variables: extract_validation_variables(schemas),
      error_sharing: true,
      success_sharing: true
    }
    
    # Register with MABEAM
    MABEAM.Orchestrator.register_coordination(
      :ml_validation_coordination,
      coordination_config
    )
    
    # Setup signal routing for coordination
    setup_validation_coordination_signals(agent_team, schemas)
    
    {:ok, coordination_config}
  end
  
  defp adjust_validation_params(schema_module, coordination_vars) do
    # Adjust validation based on coordination variables
    base_params = %{strict: true, detailed_errors: true}
    
    # Example adjustments based on common ML variables
    adjustments = %{
      confidence_threshold: coordination_vars[:confidence_threshold],
      tolerance: coordination_vars[:tolerance],
      max_iterations: coordination_vars[:max_validation_iterations]
    }
    
    Map.merge(base_params, adjustments)
  end
  
  defp coordinate_validation_success(schema_module, validated_data, context) do
    # Share successful validation patterns with team
    success_signal = %JidoSignal{
      id: JidoSignal.ID.generate(),
      type: "validation.success.shared",
      source: context[:agent_id],
      data: %{
        schema: schema_module,
        pattern: extract_validation_pattern(validated_data),
        context: context
      }
    }
    
    JidoSignal.Bus.publish(success_signal)
  end
  
  defp coordinate_validation_failure(schema_module, errors, context) do
    # Share validation failures for team learning
    failure_signal = %JidoSignal{
      id: JidoSignal.ID.generate(),
      type: "validation.failure.shared",
      source: context[:agent_id],
      data: %{
        schema: schema_module,
        errors: errors,
        context: context
      }
    }
    
    JidoSignal.Bus.publish(failure_signal)
  end
  
  defp extract_validation_variables(schemas) do
    # Extract variables that affect validation from schemas
    Enum.flat_map(schemas, fn schema ->
      case schema.__info__(:attributes) do
        attributes when is_list(attributes) ->
          Keyword.get(attributes, :validation_variables, [])
        _ ->
          []
      end
    end)
    |> Enum.uniq()
  end
  
  defp setup_validation_coordination_signals(agent_team, schemas) do
    # Setup signal routing for validation coordination
    Enum.each(schemas, fn schema ->
      # Route validation success signals
      JidoSignal.Router.add_route(
        "validation.success.shared",
        fn signal -> signal.data.schema == schema end,
        %{dispatch: {:broadcast, agent_team}}
      )
      
      # Route validation failure signals
      JidoSignal.Router.add_route(
        "validation.failure.shared",
        fn signal -> signal.data.schema == schema end,
        %{dispatch: {:broadcast, agent_team}}
      )
    end)
  end
  
  defp extract_validation_pattern(validated_data) do
    # Extract patterns from successfully validated data
    %{
      data_shape: analyze_data_shape(validated_data),
      field_types: analyze_field_types(validated_data),
      value_ranges: analyze_value_ranges(validated_data)
    }
  end
  
  defp analyze_data_shape(data) when is_map(data) do
    %{type: :map, keys: Map.keys(data), size: map_size(data)}
  end
  
  defp analyze_data_shape(data) when is_list(data) do
    %{type: :list, length: length(data), element_types: analyze_list_types(data)}
  end
  
  defp analyze_data_shape(_), do: %{type: :scalar}
  
  defp analyze_field_types(data) when is_map(data) do
    Enum.into(data, %{}, fn {key, value} ->
      {key, get_value_type(value)}
    end)
  end
  
  defp analyze_field_types(_), do: %{}
  
  defp analyze_value_ranges(data) when is_map(data) do
    Enum.into(data, %{}, fn {key, value} ->
      {key, get_value_range(value)}
    end)
    |> Enum.filter(fn {_, range} -> range != nil end)
    |> Enum.into(%{})
  end
  
  defp analyze_value_ranges(_), do: %{}
  
  defp get_value_type(value) when is_number(value), do: :number
  defp get_value_type(value) when is_binary(value), do: :string
  defp get_value_type(value) when is_boolean(value), do: :boolean
  defp get_value_type(value) when is_list(value), do: :list
  defp get_value_type(value) when is_map(value), do: :map
  defp get_value_type(_), do: :unknown
  
  defp get_value_range(value) when is_number(value), do: {value, value}
  defp get_value_range(value) when is_binary(value), do: {String.length(value), String.length(value)}
  defp get_value_range(_), do: nil
  
  defp analyze_list_types(list) do
    list
    |> Enum.map(&get_value_type/1)
    |> Enum.uniq()
  end
end