defmodule Dspy.AdvancedCoordination do
  @moduledoc """
  Advanced coordination patterns for massive-scale agent collaboration
  including consensus mechanisms, distributed load balancing, emergent
  behavior patterns, and self-organizing agent networks.
  """

  defmodule ConsensusOrchestrator do
    @moduledoc """
    Implements distributed consensus algorithms for coordinating
    decisions across 10,000+ agents using GenStage.
    """

    use GenStage

    defstruct [
      :consensus_algorithm,
      :participant_agents,
      :voting_rounds,
      :decision_history,
      :byzantine_fault_tolerance,
      :quorum_requirements
    ]

    def start_link(opts) do
      GenStage.start_link(__MODULE__, opts, name: __MODULE__)
    end

    @impl true
    def init(opts) do
      state = %__MODULE__{
        consensus_algorithm: Keyword.get(opts, :algorithm, :raft),
        participant_agents: %{},
        voting_rounds: %{},
        decision_history: [],
        byzantine_fault_tolerance: Keyword.get(opts, :byzantine_tolerance, 0.33),
        quorum_requirements: Keyword.get(opts, :quorum, 0.51)
      }

      {:producer_consumer, state}
    end

    @impl true
    def handle_events(events, _from, state) do
      consensus_results =
        Enum.map(events, fn event ->
          process_consensus_request(event, state)
        end)

      {:noreply, consensus_results, state}
    end

    defp process_consensus_request(event, state) do
      case event.type do
        :decision_request -> handle_decision_request(event, state)
        :vote_submission -> handle_vote_submission(event, state)
        :consensus_challenge -> handle_consensus_challenge(event, state)
        :leader_election -> handle_leader_election(event, state)
      end
    end

    defp handle_decision_request(event, state) do
      decision_id = generate_decision_id()

      # Initialize voting round
      voting_round = %{
        decision_id: decision_id,
        proposal: event.proposal,
        initiator: event.agent_id,
        participants: select_voting_participants(event, state),
        votes: %{},
        # 5 minutes
        deadline: DateTime.add(DateTime.utc_now(), 300, :second),
        status: :open,
        consensus_threshold: calculate_consensus_threshold(event, state)
      }

      # Broadcast voting request to selected participants
      voting_requests = create_voting_requests(voting_round)

      %{
        type: :consensus_initiated,
        decision_id: decision_id,
        voting_round: voting_round,
        voting_requests: voting_requests,
        estimated_completion: voting_round.deadline
      }
    end

    defp handle_vote_submission(event, state) do
      case Map.get(state.voting_rounds, event.decision_id) do
        nil ->
          %{type: :vote_rejected, reason: :unknown_decision, vote: event}

        voting_round ->
          updated_votes = Map.put(voting_round.votes, event.agent_id, event.vote_data)
          updated_round = %{voting_round | votes: updated_votes}

          # Check if consensus is reached
          case check_consensus_status(updated_round) do
            {:consensus_reached, result} ->
              %{
                type: :consensus_reached,
                decision_id: event.decision_id,
                result: result,
                final_votes: updated_votes,
                consensus_type: determine_consensus_type(result, updated_votes)
              }

            {:consensus_pending, progress} ->
              %{
                type: :consensus_pending,
                decision_id: event.decision_id,
                progress: progress,
                votes_remaining: calculate_remaining_votes(updated_round)
              }

            {:consensus_failed, reason} ->
              %{
                type: :consensus_failed,
                decision_id: event.decision_id,
                reason: reason,
                partial_votes: updated_votes
              }
          end
      end
    end

    defp select_voting_participants(event, state) do
      # Select appropriate agents based on expertise and availability
      all_agents = Map.keys(state.participant_agents)

      case event.selection_strategy do
        :expertise_based ->
          select_by_expertise(all_agents, event.required_expertise, state)

        :random_sample ->
          Enum.take_random(all_agents, min(1000, length(all_agents)))

        :stake_weighted ->
          select_by_stake(all_agents, event.proposal, state)

        :reputation_based ->
          select_by_reputation(all_agents, event.domain, state)

        _ ->
          Enum.take_random(all_agents, 100)
      end
    end

    defp check_consensus_status(voting_round) do
      total_votes = map_size(voting_round.votes)
      required_votes = round(length(voting_round.participants) * voting_round.consensus_threshold)

      if total_votes >= required_votes do
        analyze_vote_results(voting_round)
      else
        progress = total_votes / required_votes
        {:consensus_pending, progress}
      end
    end

    defp analyze_vote_results(voting_round) do
      vote_counts =
        voting_round.votes
        |> Map.values()
        |> Enum.group_by(& &1.decision)
        |> Enum.map(fn {decision, votes} -> {decision, length(votes)} end)
        |> Map.new()

      total_votes = map_size(voting_round.votes)

      case Enum.max_by(vote_counts, fn {_decision, count} -> count end) do
        {winning_decision, count} when count / total_votes >= voting_round.consensus_threshold ->
          {:consensus_reached,
           %{
             decision: winning_decision,
             vote_count: count,
             vote_percentage: count / total_votes,
             unanimous: count == total_votes
           }}

        _ ->
          {:consensus_failed, :insufficient_agreement}
      end
    end

    # Simplified helper functions
    defp generate_decision_id, do: "decision_#{System.unique_integer([:positive])}"
    defp calculate_consensus_threshold(_event, _state), do: 0.67

    defp create_voting_requests(voting_round) do
      Enum.map(voting_round.participants, fn participant_id ->
        %{
          type: :voting_request,
          decision_id: voting_round.decision_id,
          participant_id: participant_id,
          proposal: voting_round.proposal,
          deadline: voting_round.deadline
        }
      end)
    end

    defp determine_consensus_type(_result, _votes), do: :majority

    defp calculate_remaining_votes(voting_round) do
      length(voting_round.participants) - map_size(voting_round.votes)
    end

    defp select_by_expertise(agents, _expertise, _state), do: Enum.take(agents, 100)
    defp select_by_stake(agents, _proposal, _state), do: Enum.take(agents, 100)
    defp select_by_reputation(agents, _domain, _state), do: Enum.take(agents, 100)

    defp handle_consensus_challenge(event, _state),
      do: %{type: :challenge_processed, event: event}

    defp handle_leader_election(event, _state), do: %{type: :leader_elected, event: event}
  end

  defmodule EmergentBehaviorDetector do
    @moduledoc """
    Detects and analyzes emergent behaviors in large-scale agent collaborations
    using GenStage for real-time pattern recognition.
    """

    use GenStage

    defstruct [
      :behavior_patterns,
      :pattern_history,
      :anomaly_detector,
      :emergence_thresholds,
      :collaboration_metrics,
      :network_topology
    ]

    def start_link(opts) do
      GenStage.start_link(__MODULE__, opts, name: __MODULE__)
    end

    @impl true
    def init(opts) do
      state = %__MODULE__{
        behavior_patterns: initialize_pattern_library(),
        pattern_history: [],
        anomaly_detector: initialize_anomaly_detector(),
        emergence_thresholds: Keyword.get(opts, :thresholds, default_emergence_thresholds()),
        collaboration_metrics: %{},
        network_topology: %{nodes: %{}, edges: %{}}
      }

      {:consumer, state}
    end

    @impl true
    def handle_events(events, _from, state) do
      # Analyze events for emergent behaviors
      {detected_patterns, updated_state} = analyze_emergent_behaviors(events, state)

      # Update collaboration metrics
      final_state = update_collaboration_metrics(updated_state, events)

      # Emit detected patterns
      if length(detected_patterns) > 0 do
        notify_emergent_behaviors(detected_patterns)
      end

      {:noreply, [], final_state}
    end

    defp analyze_emergent_behaviors(events, state) do
      # Group events by time windows and analyze patterns
      # 1-minute windows
      time_windows = group_events_by_time_window(events, 60_000)

      detected_patterns =
        Enum.flat_map(time_windows, fn {window, window_events} ->
          detect_patterns_in_window(window_events, window, state)
        end)

      # Update pattern history
      updated_history = [
        %{
          timestamp: DateTime.utc_now(),
          events_analyzed: length(events),
          patterns_detected: length(detected_patterns),
          patterns: detected_patterns
        }
        | Enum.take(state.pattern_history, 99)
      ]

      updated_state = %{state | pattern_history: updated_history}

      {detected_patterns, updated_state}
    end

    defp detect_patterns_in_window(events, _window, state) do
      patterns = []

      # Detect coordination cascades
      patterns = patterns ++ detect_coordination_cascades(events, state)

      # Detect emergent leadership
      patterns = patterns ++ detect_emergent_leadership(events, state)

      # Detect collaborative clusters
      patterns = patterns ++ detect_collaborative_clusters(events, state)

      # Detect innovation waves
      patterns = patterns ++ detect_innovation_waves(events, state)

      # Detect efficiency improvements
      patterns = patterns ++ detect_efficiency_improvements(events, state)

      patterns
    end

    defp detect_coordination_cascades(events, state) do
      # Look for rapid propagation of coordination across agents
      coordination_events =
        Enum.filter(events, fn event ->
          event.type in [:consensus_reached, :collaboration_request, :task_delegation]
        end)

      if length(coordination_events) > state.emergence_thresholds.coordination_cascade do
        # Analyze the cascade pattern
        cascade_analysis = analyze_cascade_structure(coordination_events)

        [
          %{
            type: :coordination_cascade,
            window: DateTime.utc_now(),
            event_count: length(coordination_events),
            propagation_speed: cascade_analysis.propagation_speed,
            network_coverage: cascade_analysis.network_coverage,
            efficiency_gain: cascade_analysis.efficiency_gain,
            initiating_agents: cascade_analysis.initiators
          }
        ]
      else
        []
      end
    end

    defp detect_emergent_leadership(events, state) do
      # Detect agents that naturally emerge as leaders without formal assignment
      agent_influence_scores = calculate_agent_influence(events)

      emergent_leaders =
        agent_influence_scores
        |> Enum.filter(fn {_agent_id, influence} ->
          influence.leadership_score > state.emergence_thresholds.leadership_emergence
        end)
        |> Enum.sort_by(fn {_agent_id, influence} -> influence.leadership_score end, :desc)
        |> Enum.take(10)

      if length(emergent_leaders) > 0 do
        [
          %{
            type: :emergent_leadership,
            window: DateTime.utc_now(),
            leaders: emergent_leaders,
            leadership_patterns: analyze_leadership_patterns(emergent_leaders, events),
            network_impact: calculate_leadership_network_impact(emergent_leaders, events)
          }
        ]
      else
        []
      end
    end

    defp detect_collaborative_clusters(events, state) do
      # Detect spontaneous formation of collaborative groups
      collaboration_graph = build_collaboration_graph(events)
      clusters = detect_graph_clusters(collaboration_graph)

      significant_clusters =
        Enum.filter(clusters, fn cluster ->
          cluster.cohesion > state.emergence_thresholds.cluster_cohesion and
            length(cluster.members) > state.emergence_thresholds.min_cluster_size
        end)

      Enum.map(significant_clusters, fn cluster ->
        %{
          type: :collaborative_cluster,
          window: DateTime.utc_now(),
          cluster_id: cluster.id,
          members: cluster.members,
          cohesion_score: cluster.cohesion,
          collaboration_intensity: cluster.intensity,
          expertise_diversity: calculate_expertise_diversity(cluster.members),
          performance_metrics: calculate_cluster_performance(cluster, events)
        }
      end)
    end

    defp detect_innovation_waves(events, state) do
      # Detect waves of innovation spreading through the agent network
      innovation_events =
        Enum.filter(events, fn event ->
          event.type in [:novel_solution, :breakthrough_discovery, :optimization_found]
        end)

      if length(innovation_events) > state.emergence_thresholds.innovation_wave do
        wave_analysis = analyze_innovation_propagation(innovation_events)

        [
          %{
            type: :innovation_wave,
            window: DateTime.utc_now(),
            innovation_count: length(innovation_events),
            propagation_pattern: wave_analysis.pattern,
            innovation_types: wave_analysis.types,
            network_adoption_rate: wave_analysis.adoption_rate,
            performance_impact: wave_analysis.performance_impact
          }
        ]
      else
        []
      end
    end

    defp detect_efficiency_improvements(events, state) do
      # Detect emergent efficiency improvements in agent coordination
      current_metrics = calculate_current_efficiency_metrics(events)

      case state.collaboration_metrics do
        %{efficiency: previous_metrics} ->
          improvement = calculate_efficiency_improvement(previous_metrics, current_metrics)

          if improvement.overall_improvement > state.emergence_thresholds.efficiency_improvement do
            [
              %{
                type: :efficiency_emergence,
                window: DateTime.utc_now(),
                improvement_percentage: improvement.overall_improvement,
                efficiency_gains: improvement.specific_gains,
                contributing_factors: improvement.factors,
                sustainability_prediction: improvement.sustainability
              }
            ]
          else
            []
          end

        _ ->
          []
      end
    end

    defp notify_emergent_behaviors(patterns) do
      Enum.each(patterns, fn pattern ->
        case pattern.type do
          :coordination_cascade ->
            IO.puts(
              "🌊 EMERGENT: Coordination cascade detected - #{pattern.event_count} coordinated events"
            )

          :emergent_leadership ->
            IO.puts(
              "👑 EMERGENT: Leadership emergence detected - #{length(pattern.leaders)} new leaders"
            )

          :collaborative_cluster ->
            IO.puts(
              "🤝 EMERGENT: Collaborative cluster formed - #{length(pattern.members)} agents, cohesion: #{Float.round(pattern.cohesion_score, 2)}"
            )

          :innovation_wave ->
            IO.puts(
              "💡 EMERGENT: Innovation wave detected - #{pattern.innovation_count} innovations spreading"
            )

          :efficiency_emergence ->
            IO.puts(
              "⚡ EMERGENT: Efficiency improvement - #{Float.round(pattern.improvement_percentage * 100, 1)}% gain"
            )
        end
      end)
    end

    # Simplified helper functions
    defp initialize_pattern_library, do: %{}
    defp initialize_anomaly_detector, do: %{}

    defp default_emergence_thresholds do
      %{
        coordination_cascade: 50,
        leadership_emergence: 0.8,
        cluster_cohesion: 0.7,
        min_cluster_size: 5,
        innovation_wave: 20,
        efficiency_improvement: 0.15
      }
    end

    defp group_events_by_time_window(events, window_ms) do
      Enum.group_by(events, fn event ->
        timestamp = Map.get(event, :timestamp, DateTime.utc_now())
        window_start = DateTime.to_unix(timestamp, :millisecond)
        div(window_start, window_ms) * window_ms
      end)
    end

    defp analyze_cascade_structure(_events) do
      %{
        propagation_speed: :rand.uniform(),
        network_coverage: :rand.uniform(),
        efficiency_gain: :rand.uniform(),
        initiators: ["agent_1", "agent_2"]
      }
    end

    defp calculate_agent_influence(events) do
      agents =
        events
        |> Enum.map(fn event -> Map.get(event, :agent_id, "unknown") end)
        |> Enum.uniq()

      Enum.map(agents, fn agent_id ->
        agent_events =
          Enum.filter(events, fn event ->
            Map.get(event, :agent_id) == agent_id
          end)

        leadership_score = calculate_leadership_score(agent_events)

        {agent_id,
         %{
           leadership_score: leadership_score,
           event_count: length(agent_events),
           influence_type: determine_influence_type(agent_events)
         }}
      end)
      |> Map.new()
    end

    defp calculate_leadership_score(agent_events) do
      coordination_events =
        Enum.count(agent_events, fn event ->
          event.type in [:coordination_request, :consensus_initiated, :task_delegation]
        end)

      total_events = length(agent_events)
      if total_events > 0, do: coordination_events / total_events, else: 0.0
    end

    defp determine_influence_type(_events), do: :collaborative_leader
    defp analyze_leadership_patterns(_leaders, _events), do: %{pattern: :distributed_leadership}

    defp calculate_leadership_network_impact(_leaders, _events),
      do: %{reach: 100, effectiveness: 0.8}

    defp build_collaboration_graph(_events), do: %{nodes: [], edges: []}
    defp detect_graph_clusters(_graph), do: []
    defp calculate_expertise_diversity(_members), do: 0.8
    defp calculate_cluster_performance(_cluster, _events), do: %{productivity: 1.2, quality: 0.9}

    defp analyze_innovation_propagation(_events) do
      %{
        pattern: :viral_spread,
        types: [:algorithmic_improvement, :process_optimization],
        adoption_rate: 0.75,
        performance_impact: 1.15
      }
    end

    defp calculate_current_efficiency_metrics(_events) do
      %{
        task_completion_rate: 0.85,
        coordination_overhead: 0.15,
        resource_utilization: 0.80
      }
    end

    defp calculate_efficiency_improvement(_previous, _current) do
      %{
        overall_improvement: 0.20,
        specific_gains: %{coordination: 0.15, throughput: 0.25},
        factors: [:better_coordination, :skill_specialization],
        sustainability: :high
      }
    end

    defp update_collaboration_metrics(state, _events) do
      %{state | collaboration_metrics: %{efficiency: %{updated_at: DateTime.utc_now()}}}
    end
  end

  defmodule AdaptiveLoadBalancer do
    @moduledoc """
    Self-organizing load balancer that adapts to changing agent capabilities
    and workload patterns using GenStage for dynamic redistribution.
    """

    use GenStage

    defstruct [
      :agent_capabilities,
      :workload_history,
      :performance_predictions,
      :load_distribution_strategy,
      :adaptation_algorithms,
      :fairness_constraints
    ]

    def start_link(opts) do
      GenStage.start_link(__MODULE__, opts, name: __MODULE__)
    end

    @impl true
    def init(opts) do
      state = %__MODULE__{
        agent_capabilities: initialize_agent_capabilities(),
        workload_history: [],
        performance_predictions: %{},
        load_distribution_strategy: Keyword.get(opts, :strategy, :capability_weighted),
        adaptation_algorithms: initialize_adaptation_algorithms(),
        fairness_constraints: Keyword.get(opts, :fairness, default_fairness_constraints())
      }

      # Start periodic adaptation cycle
      schedule_adaptation_cycle()

      {:producer_consumer, state}
    end

    @impl true
    def handle_events(events, _from, state) do
      # Process workload distribution requests
      distribution_results =
        Enum.map(events, fn event ->
          distribute_workload(event, state)
        end)

      # Update performance tracking
      updated_state = update_performance_tracking(state, events, distribution_results)

      {:noreply, distribution_results, updated_state}
    end

    @impl true
    def handle_info(:adaptation_cycle, state) do
      # Perform adaptive load balancing optimization
      updated_state = perform_adaptation_cycle(state)

      schedule_adaptation_cycle()
      {:noreply, [], updated_state}
    end

    defp distribute_workload(workload_request, state) do
      case workload_request.type do
        :computational_task ->
          distribute_computational_workload(workload_request, state)

        :coordination_task ->
          distribute_coordination_workload(workload_request, state)

        :analysis_task ->
          distribute_analysis_workload(workload_request, state)

        :creative_task ->
          distribute_creative_workload(workload_request, state)

        _ ->
          distribute_general_workload(workload_request, state)
      end
    end

    defp distribute_computational_workload(request, state) do
      # Select agents based on computational capabilities
      suitable_agents =
        select_agents_by_capability(
          state.agent_capabilities,
          :computational_power,
          request.resource_requirements
        )

      # Apply load balancing algorithm
      distribution =
        apply_load_balancing_algorithm(
          suitable_agents,
          request,
          state.load_distribution_strategy
        )

      %{
        type: :workload_distribution,
        request_id: request.id,
        distribution: distribution,
        strategy_used: :computational_optimization,
        estimated_completion_time: calculate_completion_time(distribution),
        load_balance_score: calculate_load_balance_score(distribution),
        fairness_score: calculate_fairness_score(distribution, state.fairness_constraints)
      }
    end

    defp distribute_coordination_workload(request, state) do
      # Select agents with strong coordination capabilities
      coordination_agents =
        select_agents_by_capability(
          state.agent_capabilities,
          :coordination_skill,
          request.coordination_requirements
        )

      # Consider network topology for optimal coordination
      network_optimized_distribution =
        optimize_for_network_topology(
          coordination_agents,
          request,
          state
        )

      %{
        type: :coordination_distribution,
        request_id: request.id,
        distribution: network_optimized_distribution,
        coordination_efficiency:
          calculate_coordination_efficiency(network_optimized_distribution),
        communication_overhead: estimate_communication_overhead(network_optimized_distribution)
      }
    end

    defp perform_adaptation_cycle(state) do
      # Analyze recent performance data
      performance_analysis = analyze_recent_performance(state.workload_history)

      # Update agent capability estimates
      updated_capabilities =
        update_agent_capabilities(
          state.agent_capabilities,
          performance_analysis
        )

      # Adapt load distribution strategy if needed
      adapted_strategy =
        adapt_distribution_strategy(
          state.load_distribution_strategy,
          performance_analysis
        )

      # Update performance predictions
      updated_predictions =
        update_performance_predictions(
          state.performance_predictions,
          performance_analysis
        )

      %{
        state
        | agent_capabilities: updated_capabilities,
          load_distribution_strategy: adapted_strategy,
          performance_predictions: updated_predictions
      }
    end

    defp select_agents_by_capability(capabilities, capability_type, requirements) do
      capabilities
      |> Enum.filter(fn {_agent_id, caps} ->
        meets_requirements?(caps, capability_type, requirements)
      end)
      |> Enum.sort_by(
        fn {_agent_id, caps} ->
          get_capability_score(caps, capability_type)
        end,
        :desc
      )
      # Top 50 agents for this capability
      |> Enum.take(50)
    end

    defp apply_load_balancing_algorithm(agents, request, strategy) do
      case strategy do
        :capability_weighted ->
          apply_capability_weighted_distribution(agents, request)

        :round_robin ->
          apply_round_robin_distribution(agents, request)

        :least_loaded ->
          apply_least_loaded_distribution(agents, request)

        :predictive_optimization ->
          apply_predictive_optimization(agents, request)

        _ ->
          apply_capability_weighted_distribution(agents, request)
      end
    end

    defp apply_capability_weighted_distribution(agents, request) do
      total_capability =
        Enum.sum(
          Enum.map(agents, fn {_id, caps} ->
            get_capability_score(caps, :overall)
          end)
        )

      Enum.map(agents, fn {agent_id, caps} ->
        capability_score = get_capability_score(caps, :overall)
        workload_percentage = capability_score / total_capability
        workload_amount = request.total_work * workload_percentage

        %{
          agent_id: agent_id,
          workload_amount: workload_amount,
          expected_duration: estimate_duration(workload_amount, caps),
          resource_allocation: calculate_resource_allocation(workload_amount, caps)
        }
      end)
    end

    # Simplified helper functions
    defp initialize_agent_capabilities do
      # Simulate 10,000 agents with diverse capabilities
      for agent_id <- 1..10000, into: %{} do
        {"agent_#{agent_id}",
         %{
           computational_power: :rand.uniform(),
           coordination_skill: :rand.uniform(),
           analysis_capability: :rand.uniform(),
           creativity_score: :rand.uniform(),
           reliability: :rand.uniform(),
           current_load: :rand.uniform() * 0.5,
           specializations: Enum.take_random([:ml, :systems, :web, :mobile, :data], 2)
         }}
      end
    end

    defp initialize_adaptation_algorithms, do: %{}

    defp default_fairness_constraints do
      %{
        max_load_imbalance: 0.2,
        min_work_guarantee: 0.05,
        capability_utilization_target: 0.8
      }
    end

    defp schedule_adaptation_cycle, do: Process.send_after(self(), :adaptation_cycle, 30_000)
    defp update_performance_tracking(state, _events, _results), do: state

    defp distribute_analysis_workload(request, state),
      do: distribute_general_workload(request, state)

    defp distribute_creative_workload(request, state),
      do: distribute_general_workload(request, state)

    defp distribute_general_workload(request, _state) do
      %{
        type: :general_distribution,
        request_id: request.id,
        distribution: [],
        strategy_used: :default
      }
    end

    # 1 hour
    defp calculate_completion_time(_distribution), do: 3600
    defp calculate_load_balance_score(_distribution), do: 0.85
    defp calculate_fairness_score(_distribution, _constraints), do: 0.90
    defp optimize_for_network_topology(agents, _request, _state), do: agents
    defp calculate_coordination_efficiency(_distribution), do: 0.80
    defp estimate_communication_overhead(_distribution), do: 0.15
    defp analyze_recent_performance(_history), do: %{overall_efficiency: 0.85}
    defp update_agent_capabilities(capabilities, _analysis), do: capabilities
    defp adapt_distribution_strategy(strategy, _analysis), do: strategy
    defp update_performance_predictions(predictions, _analysis), do: predictions
    defp meets_requirements?(_caps, _type, _requirements), do: true

    defp get_capability_score(caps, :overall) do
      (caps.computational_power + caps.coordination_skill + caps.analysis_capability) / 3
    end

    defp get_capability_score(caps, capability_type), do: Map.get(caps, capability_type, 0.5)

    defp apply_round_robin_distribution(agents, _request) do
      Enum.with_index(agents)
      |> Enum.map(fn {{agent_id, _caps}, index} ->
        %{agent_id: agent_id, workload_amount: 1.0 / length(agents), priority: index}
      end)
    end

    defp apply_least_loaded_distribution(agents, _request) do
      Enum.map(agents, fn {agent_id, caps} ->
        %{agent_id: agent_id, workload_amount: 1.0 - caps.current_load}
      end)
    end

    defp apply_predictive_optimization(agents, _request) do
      Enum.map(agents, fn {agent_id, _caps} ->
        %{agent_id: agent_id, workload_amount: :rand.uniform()}
      end)
    end

    # 30 minutes
    defp estimate_duration(_workload, _caps), do: 1800
    defp calculate_resource_allocation(_workload, _caps), do: %{cpu: 0.5, memory: 0.3}
  end

  def start_advanced_coordination_system(opts \\ []) do
    children = [
      {ConsensusOrchestrator, Keyword.get(opts, :consensus, [])},
      {EmergentBehaviorDetector, Keyword.get(opts, :behavior_detection, [])},
      {AdaptiveLoadBalancer, Keyword.get(opts, :load_balancing, [])}
    ]

    Supervisor.start_link(children,
      strategy: :one_for_one,
      name: AdvancedCoordinationSupervisor
    )
  end
end
