# Nexus Philosophy: Start simple, grow intelligent
defmodule Nexus.Core do
  @moduledoc """
  Progressive Intelligence Architecture:
  
  Layer 1: Proven Primitives (reliable foundation)
  Layer 2: Performance Optimization (measured gains)
  Layer 3: Adaptive Intelligence (controlled emergence)
  Layer 4: Advanced Coordination (optional complexity)
  
  Each layer is independently testable and debuggable.
  """
end

# Layer 1: Simple coordination
Nexus.Agent.send_message(agent_id, message)

# Layer 2: Performance-optimized routing
Nexus.Agent.send_message(agent_id, message, routing: :optimized)

# Layer 3: Intelligent routing with adaptation
Nexus.Agent.send_message(agent_id, message, routing: :adaptive)

# Layer 4: Swarm-coordinated routing (optional)
Nexus.Agent.send_message(agent_id, message, routing: :swarm_optimized)

defmodule Nexus.Emergence.Controller do
  @moduledoc """
  Controls and monitors emergent behaviors in distributed agent systems.
  
  Features:
  - Deterministic emergence for testing
  - Behavior recording and replay
  - Emergence metrics and boundaries
  - Fail-safe fallback to simple coordination
  """
  
  def enable_emergent_behavior(agents, behavior_type, constraints) do
    # Enable with strict monitoring and fallback
  end
  
  def monitor_emergence(behavior_id) do
    # Real-time emergence monitoring with safety bounds
  end
  
  def replay_emergence(behavior_id, scenario) do
    # Deterministic replay for debugging
  end
end

defmodule Nexus.Performance do
  @moduledoc """
  High-performance coordination using established patterns:
  
  - ETS-based message routing: <1μs local operations
  - Pooled connection management: 50k+ concurrent connections
  - Lock-free message queues: 10M+ ops/sec throughput
  - NUMA-aware process placement: Hardware optimization
  """
  
  def high_performance_send(target, message) do
    # Optimized send using ETS routing table and connection pools
    route_table = :persistent_term.get(:nexus_routes)
    connection_pool = :persistent_term.get(:nexus_connections)
    
    case :ets.lookup(route_table, target) do
      [{^target, :local, pid}] -> 
        GenServer.cast(pid, message)  # <1μs for local
      [{^target, :remote, node, pid}] -> 
        pooled_remote_cast(connection_pool, node, pid, message)  # <100μs
    end
  end
end

defmodule Nexus.Observability do
  @moduledoc """
  Comprehensive observability without complexity overhead.
  
  - Structured telemetry with minimal performance impact
  - Real-time system behavior visualization
  - Predictive alerting based on pattern recognition
  - Chaos engineering integration for resilience testing
  """
  
  def instrument_operation(operation_name, fun) do
    start_time = :erlang.monotonic_time(:microsecond)
    
    try do
      result = fun.()
      duration = :erlang.monotonic_time(:microsecond) - start_time
      
      :telemetry.execute(
        [:nexus, :operation, :completed],
        %{duration: duration},
        %{operation: operation_name, result: :success}
      )
      
      result
    rescue
      error ->
        duration = :erlang.monotonic_time(:microsecond) - start_time
        
        :telemetry.execute(
          [:nexus, :operation, :failed],
          %{duration: duration},
          %{operation: operation_name, error: error}
        )
        
        reraise error, __STACKTRACE__
    end
  end
end

defmodule Nexus.Coordination.Simple do
  @moduledoc """
  Proven coordination patterns that form the foundation.
  
  - Vector clocks for causality (well-understood)
  - Gossip protocols for state sync (proven at scale)
  - Consistent hashing for placement (battle-tested)
  - Circuit breakers for fault isolation (standard pattern)
  """
  
  def coordinate_agents(agents, coordination_type) do
    case coordination_type do
      :causal -> use_vector_clocks(agents)
      :eventual -> use_gossip_protocol(agents)
      :consistent -> use_consensus_protocol(agents)
      :performance -> use_optimized_routing(agents)
    end
  end
  
  # Well-understood vector clock implementation
  defp use_vector_clocks(agents) do
    Enum.reduce(agents, %VectorClock{}, fn agent, clock ->
      VectorClock.tick(clock, agent.node_id)
    end)
  end
end

defmodule Nexus.Agent.Intelligence do
  @moduledoc """
  Four levels of agent intelligence, each optional and measurable.
  """
  
  @type intelligence_level :: 
    :reactive |      # Simple request/response
    :proactive |     # Basic learning and prediction
    :adaptive |      # Dynamic behavior adjustment
    :emergent        # Multi-agent coordination behaviors
  
  def create_agent(spec, intelligence_level \\ :reactive) do
    base_agent = create_base_agent(spec)
    
    case intelligence_level do
      :reactive -> base_agent
      :proactive -> add_learning_layer(base_agent)
      :adaptive -> add_adaptation_layer(base_agent) 
      :emergent -> add_emergence_layer(base_agent)
    end
  end
  
  defp add_learning_layer(agent) do
    # Add simple ML-based prediction and optimization
    %{agent | 
      capabilities: [:learning | agent.capabilities],
      intelligence_modules: [Nexus.Learning.BasicML | agent.intelligence_modules]
    }
  end
  
  defp add_adaptation_layer(agent) do
    # Add dynamic behavior adjustment based on environment
    %{agent |
      capabilities: [:adaptation | agent.capabilities], 
      intelligence_modules: [Nexus.Adaptation.BehaviorAdjuster | agent.intelligence_modules]
    }
  end
  
  defp add_emergence_layer(agent) do
    # Add multi-agent coordination and swarm behaviors
    %{agent |
      capabilities: [:emergence | agent.capabilities],
      intelligence_modules: [Nexus.Emergence.SwarmCoordinator | agent.intelligence_modules]
    }
  end
end

defmodule Nexus.Performance.Optimizations do
  @moduledoc """
  Performance optimizations using proven techniques:
  
  1. ETS-based routing: O(1) local message routing
  2. Process pools: Minimize context switching overhead
  3. Binary protocols: Efficient serialization
  4. Connection pooling: Amortize connection costs
  5. NUMA awareness: Hardware-optimized placement
  """
  
  def setup_high_performance_routing() do
    # Create ETS table for O(1) message routing
    :ets.new(:nexus_routes, [
      :named_table, 
      :public, 
      :set, 
      {:read_concurrency, true},
      {:write_concurrency, true}
    ])
    
    # Create connection pools for each remote node
    nodes = Node.list()
    Enum.each(nodes, &setup_connection_pool/1)
    
    # Set up NUMA-aware process placement
    setup_numa_placement()
  end
  
  defp setup_connection_pool(node) do
    pool_config = [
      size: 10,
      max_overflow: 50,
      strategy: :fifo
    ]
    
    :poolboy.start_link(pool_config, {Nexus.Connection.Worker, node})
  end
  
  defp setup_numa_placement() do
    # Place processes based on NUMA topology for optimal memory access
    numa_nodes = get_numa_topology()
    
    Enum.each(numa_nodes, fn numa_node ->
      spawn_opt(
        fn -> start_numa_aware_processes(numa_node) end,
        [{:scheduler, numa_node.scheduler_id}]
      )
    end)
  end
end

defmodule Nexus.Emergence.Controlled do
  @moduledoc """
  Controlled emergence with safety bounds and monitoring.
  
  Features:
  - Emergence boundaries: Define safe operating ranges
  - Behavior recording: Capture emergent patterns for analysis
  - Fallback mechanisms: Revert to simple coordination if needed
  - Testing support: Deterministic emergence for testing
  """
  
  defstruct [
    :emergence_id,
    :agents,
    :behavior_constraints,
    :safety_bounds,
    :monitoring_metrics,
    :fallback_strategy
  ]
  
  def enable_emergence(agents, emergence_config) do
    emergence = %__MODULE__{
      emergence_id: UUID.uuid4(),
      agents: agents,
      behavior_constraints: emergence_config.constraints,
      safety_bounds: emergence_config.safety_bounds,
      monitoring_metrics: initialize_monitoring(),
      fallback_strategy: emergence_config.fallback || :simple_coordination
    }
    
    # Start emergence with monitoring
    start_emergence_monitoring(emergence)
    enable_emergent_behaviors(emergence)
    
    emergence
  end
  
  def monitor_emergence(emergence_id) do
    case :ets.lookup(:nexus_emergence, emergence_id) do
      [{^emergence_id, metrics}] -> 
        analyze_emergence_health(metrics)
      [] -> 
        {:error, :emergence_not_found}
    end
  end
  
  defp analyze_emergence_health(metrics) do
    cond do
      metrics.coherence < 0.5 -> {:warning, :low_coherence}
      metrics.performance_delta < -0.2 -> {:warning, :performance_degradation}
      metrics.error_rate > 0.1 -> {:critical, :high_error_rate}
      true -> {:ok, :healthy}
    end
  end
  
  def fallback_to_simple_coordination(emergence_id) do
    Logger.warn("Emergence #{emergence_id} falling back to simple coordination")
    
    # Disable emergent behaviors
    disable_emergent_behaviors(emergence_id)
    
    # Switch to proven coordination patterns
    enable_simple_coordination(emergence_id)
    
    :ok
  end
end

defmodule Nexus.Foundation do
  @moduledoc """
  Foundation layer providing proven, reliable primitives.
  
  Architecture:
  - OTP supervision trees for fault tolerance
  - ETS/DETS for high-performance storage
  - Standard TCP/UDP for networking
  - Proven consensus algorithms (Raft)
  """
  
  use Application
  
  def start(_type, _args) do
    children = [
      # Core infrastructure
      {Nexus.Registry.Distributed, []},
      {Nexus.Cluster.Coordinator, []},
      {Nexus.Performance.Monitor, []},
      
      # Agent supervision
      {Nexus.Agent.Supervisor, []},
      
      # Communication layer
      {Nexus.Transport.Supervisor, []},
      
      # Intelligence layers (optional)
      {Nexus.Intelligence.Supervisor, intelligence_config()}
    ]
    
    opts = [strategy: :one_for_one, name: Nexus.Supervisor]
    Supervisor.start_link(children, opts)
  end
  
  defp intelligence_config() do
    [
      enable_learning: Application.get_env(:nexus, :enable_learning, false),
      enable_adaptation: Application.get_env(:nexus, :enable_adaptation, false),
      enable_emergence: Application.get_env(:nexus, :enable_emergence, false)
    ]
  end
end

defmodule Nexus.Registry.Distributed do
  @moduledoc """
  High-performance distributed registry using ETS and consistent hashing.
  
  Performance targets:
  - Local lookup: <1μs
  - Remote lookup: <100μs  
  - Registration: <10μs
  - 1M+ agents per node
  """
  
  use GenServer
  
  def start_link(opts) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def init(_opts) do
    # Create high-performance ETS tables
    :ets.new(:nexus_local_registry, [
      :named_table,
      :public,
      :set,
      {:read_concurrency, true},
      {:write_concurrency, true}
    ])
    
    :ets.new(:nexus_remote_registry, [
      :named_table,
      :public,
      :set,
      {:read_concurrency, true}
    ])
    
    # Initialize consistent hash ring
    hash_ring = Nexus.HashRing.new()
    
    {:ok, %{hash_ring: hash_ring}}
  end
  
  @doc """
  Register agent with O(1) performance.
  """
  def register_agent(agent_id, pid, metadata \\ %{}) do
    # Determine optimal placement
    target_node = Nexus.HashRing.get_node(agent_id)
    
    if target_node == node() do
      # Local registration - <1μs
      :ets.insert(:nexus_local_registry, {agent_id, pid, metadata})
      
      # Replicate to remote nodes - background
      Task.start(fn -> replicate_registration(agent_id, pid, metadata) end)
      
      {:ok, :local}
    else
      # Remote registration
      case :rpc.call(target_node, __MODULE__, :register_agent, [agent_id, pid, metadata]) do
        {:ok, :local} -> {:ok, {:remote, target_node}}
        error -> error
      end
    end
  end
  
  @doc """
  Find agent with O(1) performance.
  """
  def find_agent(agent_id) do
    # Try local first - <1μs
    case :ets.lookup(:nexus_local_registry, agent_id) do
      [{^agent_id, pid, metadata}] -> 
        {:ok, {:local, pid, metadata}}
      [] -> 
        # Try remote cache - <10μs
        case :ets.lookup(:nexus_remote_registry, agent_id) do
          [{^agent_id, node, pid, metadata}] -> 
            {:ok, {:remote, node, pid, metadata}}
          [] -> 
            # Remote lookup - <100μs
            remote_lookup(agent_id)
        end
    end
  end
  
  defp remote_lookup(agent_id) do
    target_node = Nexus.HashRing.get_node(agent_id)
    
    case :rpc.call(target_node, __MODULE__, :local_lookup, [agent_id], 1000) do
      {:ok, {pid, metadata}} -> 
        # Cache for future lookups
        :ets.insert(:nexus_remote_registry, {agent_id, target_node, pid, metadata})
        {:ok, {:remote, target_node, pid, metadata}}
      
      error -> 
        error
    end
  end
  
  def local_lookup(agent_id) do
    case :ets.lookup(:nexus_local_registry, agent_id) do
      [{^agent_id, pid, metadata}] -> {:ok, {pid, metadata}}
      [] -> {:error, :not_found}
    end
  end
end

defmodule Nexus.Agent do
  @moduledoc """
  Agent with progressive intelligence capabilities.
  
  Intelligence Levels:
  1. Reactive: Simple request/response (100% reliable)
  2. Proactive: Learning and prediction (measured improvement)
  3. Adaptive: Dynamic behavior adjustment (controlled adaptation)
  4. Emergent: Multi-agent coordination (optional, monitored)
  """
  
  use GenServer
  
  defstruct [
    :id,
    :state,
    :intelligence_level,
    :capabilities,
    :performance_metrics,
    :learning_model,
    :adaptation_parameters,
    :emergence_participation
  ]
  
  def start_link(agent_spec) do
    GenServer.start_link(__MODULE__, agent_spec, name: via_tuple(agent_spec.id))
  end
  
  def init(agent_spec) do
    # Register with distributed registry
    {:ok, location} = Nexus.Registry.Distributed.register_agent(agent_spec.id, self())
    
    state = %__MODULE__{
      id: agent_spec.id,
      state: agent_spec.initial_state || %{},
      intelligence_level: agent_spec.intelligence_level || :reactive,
      capabilities: initialize_capabilities(agent_spec),
      performance_metrics: initialize_metrics(),
      learning_model: initialize_learning(agent_spec),
      adaptation_parameters: initialize_adaptation(agent_spec),
      emergence_participation: %{}
    }
    
    # Enable intelligence layers based on configuration
    enable_intelligence_layers(state)
    
    {:ok, state}
  end
  
  # Reactive level - simple, reliable request/response
  def handle_call({:reactive_request, request}, _from, state) do
    result = process_reactive_request(request, state)
    {:reply, result, state}
  end
  
  # Proactive level - learning-enhanced responses
  def handle_call({:proactive_request, request}, _from, state) do
    # Use learning model to optimize response
    optimized_request = optimize_with_learning(request, state.learning_model)
    result = process_reactive_request(optimized_request, state)
    
    # Update learning model
    new_learning_model = update_learning_model(state.learning_model, request, result)
    new_state = %{state | learning_model: new_learning_model}
    
    {:reply, result, new_state}
  end
  
  # Adaptive level - dynamic behavior adjustment
  def handle_call({:adaptive_request, request}, _from, state) do
    # Adjust behavior based on current environment
    adjusted_behavior = adapt_behavior(request, state.adaptation_parameters)
    result = execute_adapted_behavior(adjusted_behavior, state)
    
    # Update adaptation parameters
    new_adaptation = update_adaptation_parameters(
      state.adaptation_parameters, 
      request, 
      result
    )
    
    new_state = %{state | adaptation_parameters: new_adaptation}
    
    {:reply, result, new_state}
  end
  
  # Emergent level - multi-agent coordination
  def handle_call({:emergent_request, request}, _from, state) do
    case state.emergence_participation do
      %{enabled: true, coordinator: coordinator} ->
        # Coordinate with other agents
        coordination_result = Nexus.Emergence.Controlled.coordinate_request(
          coordinator,
          state.id,
          request
        )
        
        result = execute_coordinated_behavior(coordination_result, state)
        {:reply, result, state}
      
      _ ->
        # Fall back to adaptive behavior
        handle_call({:adaptive_request, request}, _from, state)
    end
  end
  
  defp via_tuple(agent_id) do
    {:via, Registry, {Nexus.LocalRegistry, agent_id}}
  end
end

defmodule Nexus.Intelligence.Reactive do
  @moduledoc """
  Foundation layer: Simple, reliable request/response patterns.
  
  Characteristics:
  - 100% predictable behavior
  - <1ms response time for local operations
  - Zero learning or adaptation overhead
  - Perfect for production-critical operations
  """
  
  def process_request(request, agent_state) do
    case request.type do
      :query -> handle_query(request.data, agent_state)
      :update -> handle_update(request.data, agent_state)
      :action -> handle_action(request.data, agent_state)
    end
  end
  
  defp handle_query(query, agent_state) do
    # Simple, deterministic query processing
    Map.get(agent_state, query.key, query.default)
  end
  
  defp handle_update(update, agent_state) do
    # Simple state update with validation
    case validate_update(update, agent_state) do
      :ok -> Map.put(agent_state, update.key, update.value)
      {:error, reason} -> {:error, reason}
    end
  end
  
  defp handle_action(action, agent_state) do
    # Execute deterministic action
    execute_action(action, agent_state)
  end
end

defmodule Nexus.Intelligence.Proactive do
  @moduledoc """
  Learning layer: Basic prediction and optimization.
  
  Characteristics:
  - Simple ML models (linear regression, decision trees)
  - Measured performance improvements
  - Fallback to reactive behavior on errors
  - Transparent learning process
  """
  
  defstruct [
    :prediction_model,
    :optimization_history,
    :performance_baseline,
    :learning_enabled
  ]
  
  def new() do
    %__MODULE__{
      prediction_model: Nexus.ML.LinearRegression.new(),
      optimization_history: [],
      performance_baseline: nil,
      learning_enabled: true
    }
  end
  
  def optimize_request(request, learning_state) do
    if learning_state.learning_enabled do
      # Use simple ML to optimize request
      predicted_optimal = Nexus.ML.LinearRegression.predict(
        learning_state.prediction_model,
        extract_features(request)
      )
      
      apply_optimization(request, predicted_optimal)
    else
      # Fall back to reactive processing
      request
    end
  end
  
  def update_learning(learning_state, request, result, performance_metrics) do
    # Update model with new data point
    features = extract_features(request)
    performance = calculate_performance_score(result, performance_metrics)
    
    new_model = Nexus.ML.LinearRegression.update(
      learning_state.prediction_model,
      features,
      performance
    )
    
    new_history = [
      {request, result, performance_metrics} | 
      Enum.take(learning_state.optimization_history, 999)
    ]
    
    %{learning_state |
      prediction_model: new_model,
      optimization_history: new_history
    }
  end
  
  defp extract_features(request) do
    # Extract numerical features for ML model
    [
      request.complexity || 1.0,
      request.priority || 0.5,
      request.resource_requirements || 1.0,
      :os.timestamp() |> elem(2) |> rem(1000) / 1000.0  # Time of day
    ]
  end
end

defmodule Nexus.Intelligence.Adaptive do
  @moduledoc """
  Adaptation layer: Dynamic behavior adjustment based on environment.
  
  Characteristics:
  - Responds to changing conditions
  - Maintains safety bounds
  - Gradual adaptation with monitoring
  - Rollback capability for poor adaptations
  """
  
  defstruct [
    :adaptation_rules,
    :environment_sensors,
    :adaptation_history,
    :safety_bounds,
    :rollback_capability
  ]
  
  def new() do
    %__MODULE__{
      adaptation_rules: initialize_adaptation_rules(),
      environment_sensors: initialize_sensors(),
      adaptation_history: :queue.new(),
      safety_bounds: default_safety_bounds(),
      rollback_capability: true
    }
  end
  
  def adapt_behavior(request, adaptation_state) do
    # Sense current environment
    environment = sense_environment(adaptation_state.environment_sensors)
    
    # Determine if adaptation is needed
    case needs_adaptation?(environment, adaptation_state.adaptation_rules) do
      {true, adaptation_type} ->
        # Apply adaptation within safety bounds
        adapted_behavior = apply_adaptation(
          request, 
          adaptation_type, 
          adaptation_state.safety_bounds
        )
        
        # Record adaptation for monitoring
        record_adaptation(adaptation_state, adaptation_type, adapted_behavior)
        
        adapted_behavior
      
      false ->
        # No adaptation needed
        request
    end
  end
  
  defp sense_environment(sensors) do
    %{
      cpu_load: get_cpu_load(),
      memory_usage: get_memory_usage(),
      network_latency: get_network_latency(),
      error_rate: get_error_rate(),
      throughput: get_throughput()
    }
  end
  
  defp needs_adaptation?(environment, rules) do
    Enum.find_value(rules, false, fn rule ->
      if rule.condition_fn.(environment) do
        {true, rule.adaptation_type}
      end
    end)
  end
  
  defp apply_adaptation(request, adaptation_type, safety_bounds) do
    case adaptation_type do
      :reduce_complexity ->
        # Simplify request to reduce load
        simplify_request(request, safety_bounds.complexity_reduction)
      
      :increase_timeout ->
        # Increase timeout for high latency environments
        adjust_timeout(request, safety_bounds.max_timeout)
      
      :enable_caching ->
        # Enable caching for repeated requests
        enable_request_caching(request)
      
      :prioritize_quality ->
        # Prioritize quality over speed
        adjust_quality_speed_tradeoff(request, :quality)
    end
  end
  
  def rollback_adaptation(adaptation_state, adaptation_id) do
    case find_adaptation(adaptation_state.adaptation_history, adaptation_id) do
      {:ok, adaptation} ->
        # Rollback to previous behavior
        revert_adaptation(adaptation)
        
        # Remove from history
        new_history = remove_adaptation(adaptation_state.adaptation_history, adaptation_id)
        %{adaptation_state | adaptation_history: new_history}
      
      {:error, :not_found} ->
        {:error, :adaptation_not_found}
    end
  end
end

defmodule Nexus.Intelligence.Emergent do
  @moduledoc """
  Emergence layer: Multi-agent coordination and swarm behaviors.
  
  Characteristics:
  - Optional and strictly monitored
  - Safety bounds and fallback mechanisms
  - Deterministic emergence for testing
  - Performance monitoring and rollback
  """
  
  defstruct [
    :swarm_coordinator,
    :emergence_patterns,
    :coordination_rules,
    :safety_monitor,
    :fallback_strategy
  ]
  
  def new() do
    %__MODULE__{
      swarm_coordinator: Nexus.Swarm.Coordinator.new(),
      emergence_patterns: [],
      coordination_rules: default_coordination_rules(),
      safety_monitor: Nexus.Safety.Monitor.new(),
      fallback_strategy: :immediate_fallback
    }
  end
  
  def coordinate_agents(agents, coordination_goal, emergence_state) do
    # Check safety bounds before enabling emergence
    case Nexus.Safety.Monitor.check_safety(emergence_state.safety_monitor, agents) do
      :safe ->
        # Enable emergent coordination
        enable_swarm_coordination(agents, coordination_goal, emergence_state)
      
      {:unsafe, reason} ->
        Logger.warn("Emergence disabled due to safety: #{inspect(reason)}")
        # Fall back to simple coordination
        simple_coordination(agents, coordination_goal)
    end
  end
  
  defp enable_swarm_coordination(agents, coordination_goal, emergence_state) do
    # Initialize swarm with safety monitoring
    swarm = Nexus.Swarm.Coordinator.initialize_swarm(
      emergence_state.swarm_coordinator,
      agents,
      coordination_goal
    )
    
    # Start emergence with monitoring
    Task.async(fn ->
      monitor_emergence(swarm, emergence_state.safety_monitor)
    end)
    
    # Execute coordinated behavior
    execute_swarm_behavior(swarm, coordination_goal)
  end
  
  defp monitor_emergence(swarm, safety_monitor) do
    Stream.interval(100)  # Monitor every 100ms
    |> Enum.reduce_while(swarm, fn _tick, current_swarm ->
      case Nexus.Safety.Monitor.check_swarm_health(safety_monitor, current_swarm) do
        :healthy -> 
          {:cont, current_swarm}
        
        {:degraded, metrics} ->
          Logger.warn("Swarm performance degraded: #{inspect(metrics)}")
          {:cont, current_swarm}
        
        {:critical, reason} ->
          Logger.error("Swarm critical failure: #{inspect(reason)}")
          emergency_fallback(current_swarm)
          {:halt, :emergency_stopped}
      end
    end)
  end
  
  defp emergency_fallback(swarm) do
    # Immediately disable emergent behaviors
    Nexus.Swarm.Coordinator.emergency_stop(swarm)
    
    # Revert all agents to adaptive intelligence level
    Enum.each(swarm.agents, fn agent ->
      Nexus.Agent.set_intelligence_level(agent.id, :adaptive)
    end)
    
    # Notify operators
    Nexus.Alerts.emergency_alert("Swarm emergency fallback triggered")
  end
end

defmodule Nexus.Coordination.Proven do
  @moduledoc """
  Battle-tested coordination patterns with known characteristics.
  
  Philosophy: Use well-understood algorithms with known performance
  characteristics rather than exotic quantum-inspired patterns.
  """
  
  # Vector clocks for causal consistency
  def coordinate_with_causality(agents, operations) do
    vector_clock = VectorClock.new()
    
    Enum.reduce(operations, {[], vector_clock}, fn operation, {results, clock} ->
      # Update clock for this operation
      new_clock = VectorClock.tick(clock, node())
      
      # Execute operation with causal ordering
      result = execute_with_causality(operation, new_clock)
      
      {[result | results], new_clock}
    end)
  end
  
  # Gossip protocol for eventual consistency
  def synchronize_state(nodes, state) do
    # Use proven gossip algorithm with exponential convergence
    gossip_rounds = :math.ceil(:math.log2(length(nodes))) + 3
    
    Enum.reduce(1..gossip_rounds, state, fn _round, current_state ->
      # Each node exchanges state with random subset
      exchange_count = min(3, length(nodes) - 1)  # Proven optimal
      
      Enum.reduce(1..exchange_count, current_state, fn _exchange, acc_state ->
        partner_node = Enum.random(nodes -- [node()])
        exchange_state_with_node(acc_state, partner_node)
      end)
    end)
  end
  
  # Raft consensus for strong consistency
  def coordinate_with_consensus(nodes, operation) do
    case select_leader(nodes) do
      {:ok, leader} when leader == node() ->
        # We are leader, coordinate the operation
        coordinate_as_leader(nodes, operation)
      
      {:ok, leader} ->
        # Forward to leader
        :rpc.call(leader, __MODULE__, :coordinate_as_leader, [nodes, operation])
      
      {:error, :no_leader} ->
        # Trigger leader election
        elect_leader(nodes)
        coordinate_with_consensus(nodes, operation)
    end
  end
  
  # Simple load balancing with proven algorithms
  def balance_load(agents, requests) do
    case get_balancing_strategy() do
      :round_robin ->
        round_robin_balance(agents, requests)
      
      :least_connections ->
        least_connections_balance(agents, requests)
      
      :weighted_response_time ->
        weighted_response_time_balance(agents, requests)
      
      :resource_aware ->
        resource_aware_balance(agents, requests)
    end
  end
  
  defp round_robin_balance(agents, requests) do
    # Simple, predictable round-robin
    agent_count = length(agents)
    
    requests
    |> Enum.with_index()
    |> Enum.map(fn {request, index} ->
      agent = Enum.at(agents, rem(index, agent_count))
      {agent, request}
    end)
  end
  
  defp least_connections_balance(agents, requests) do
    # Assign to agent with fewest active connections
    Enum.map(requests, fn request ->
      agent = Enum.min_by(agents, &get_connection_count/1)
      {agent, request}
    end)
  end
end

defmodule Nexus.Coordination.Enhancement do
  @moduledoc """
  Gradual enhancement of coordination capabilities.
  
  Start with simple patterns, add sophistication incrementally.
  """
  
  @enhancement_levels [
    :basic,        # Round-robin, simple routing
    :optimized,    # Load-aware routing, connection pooling
    :intelligent,  # ML-based optimization, predictive routing
    :adaptive,     # Dynamic adaptation to changing conditions
    :emergent      # Multi-agent coordination (optional)
  ]
  
  def coordinate(agents, goal, enhancement_level \\ :basic) do
    case enhancement_level do
      :basic -> 
        basic_coordination(agents, goal)
      
      :optimized -> 
        optimized_coordination(agents, goal)
      
      :intelligent -> 
        intelligent_coordination(agents, goal)
      
      :adaptive -> 
        adaptive_coordination(agents, goal)
      
      :emergent -> 
        emergent_coordination(agents, goal)
    end
  end
  
  defp basic_coordination(agents, goal) do
    # Simple, reliable coordination
    case goal.type do
      :distribute_work ->
        Nexus.Coordination.Proven.round_robin_balance(agents, goal.work_items)
      
      :synchronize_state ->
        Nexus.Coordination.Proven.synchronize_state(agents, goal.state)
      
      :leader_election ->
        Nexus.Coordination.Proven.elect_leader(agents)
    end
  end
  
  defp optimized_coordination(agents, goal) do
    # Add performance optimizations
    optimized_agents = optimize_agent_selection(agents, goal)
    
    case goal.type do
      :distribute_work ->
        Nexus.Coordination.Proven.least_connections_balance(optimized_agents, goal.work_items)
      
      :synchronize_state ->
        # Use optimized gossip with network topology awareness
        topology_aware_gossip(optimized_agents, goal.state)
      
      :leader_election ->
        # Use performance-aware leader election
        performance_aware_election(optimized_agents)
    end
  end
  
  defp intelligent_coordination(agents, goal) do
    # Add machine learning for optimization
    ml_model = get_coordination_model(goal.type)
    
    case Nexus.ML.predict_optimal_coordination(ml_model, agents, goal) do
      {:ok, coordination_plan} ->
        execute_coordination_plan(coordination_plan)
      
      {:error, :insufficient_data} ->
        # Fall back to optimized coordination
        optimized_coordination(agents, goal)
    end
  end
  
  defp adaptive_coordination(agents, goal) do
    # Add dynamic adaptation based on conditions
    conditions = assess_coordination_conditions(agents, goal)
    
    adaptation_strategy = select_adaptation_strategy(conditions)
    
    case adaptation_strategy do
      :simple -> basic_coordination(agents, goal)
      :optimized -> optimized_coordination(agents, goal)
      :intelligent -> intelligent_coordination(agents, goal)
      :custom -> custom_adaptive_coordination(agents, goal, conditions)
    end
  end
  
  defp emergent_coordination(agents, goal) do
    # Optional emergent behaviors with safety monitoring
    case Nexus.Intelligence.Emergent.coordinate_agents(agents, goal, get_emergence_state()) do
      {:ok, result} -> result
      {:fallback, reason} -> 
        Logger.warn("Emergent coordination fallback: #{inspect(reason)}")
        adaptive_coordination(agents, goal)
    end
  end
end

defmodule Nexus.Performance.Microsecond do
  @moduledoc """
  Microsecond-level performance optimizations using proven techniques.
  
  Target performance:
  - Local message routing: <1μs
  - Remote message routing: <100μs
  - State lookup: <1μs
  - Load balancing decision: <10μs
  """
  
  @compile {:inline, [route_local: 2, lookup_route: 1]}
  
  def setup_high_performance_infrastructure() do
    # Pre-compile routing tables
    compile_routing_tables()
    
    # Set up connection pools
    setup_connection_pools()
    
    # Configure NUMA-aware placement
    configure_numa_placement()
    
    # Pre-warm critical paths
    prewarm_critical_paths()
  end
  
  defp compile_routing_tables() do
    # Use persistent_term for ultra-fast access
    nodes = [node() | Node.list()]
    
    routing_table = Enum.reduce(nodes, %{}, fn node, acc ->
      Map.put(acc, node, setup_node_routes(node))
    end)
    
    :persistent_term.put({:nexus, :routing_table}, routing_table)
    
    # Create ETS table for agent-to-node mapping
    :ets.new(:nexus_agent_routes, [
      :named_table,
      :public,
      :set,
      {:read_concurrency, true},
      {:write_concurrency, true}
    ])
  end
  
  def route_message_ultra_fast(agent_id, message) do
    # <1μs for local, <100μs for remote
    case lookup_route(agent_id) do
      {:local, pid} ->
        # Direct local send - <1μs
        GenServer.cast(pid, message)
      
      {:remote, node, pid} ->
        # Pooled remote send - <100μs
        pooled_remote_cast(node, pid, message)
      
      :not_found ->
        # Fallback to registry lookup
        route_via_registry(agent_id, message)
    end
  end
  
  defp lookup_route(agent_id) do
    # ETS lookup - <1μs
    case :ets.lookup(:nexus_agent_routes, agent_id) do
      [{^agent_id, :local, pid}] -> {:local, pid}
      [{^agent_id, :remote, node, pid}] -> {:remote, node, pid}
      [] -> :not_found
    end
  end
  
  defp pooled_remote_cast(node, pid, message) do
    # Use pre-established connection pool
    pool_name = :"nexus_pool_#{node}"
    
    :poolboy.transaction(pool_name, fn worker ->
      GenServer.cast({pid, node}, message)
    end)
  end
  
  def ultra_fast_load_balance(agents, requests) do
    # <10μs load balancing decision
    agent_count = length(agents)
    counter = :counters.get(:persistent_term.get(:nexus_lb_counter), 1)
    
    Enum.map(requests, fn request ->
      # Simple but fast round-robin
      agent_index = rem(counter, agent_count)
      agent = :lists.nth(agent_index + 1, agents)  # 1-indexed
      
      :counters.add(:persistent_term.get(:nexus_lb_counter), 1, 1)
      
      {agent, request}
    end)
  end
end

defmodule Nexus.Performance.Optimization do
  @moduledoc """
  Memory and CPU optimizations for high-scale deployments.
  
  Targets:
  - <1KB memory per idle agent
  - <1% CPU per 1000 active agents
  - Linear scaling to 1M+ agents per node
  """
  
  def optimize_memory_usage() do
    # Use binary protocols for state storage
    configure_binary_protocols()
    
    # Implement memory pooling
    setup_memory_pools()
    
    # Configure garbage collection
    optimize_garbage_collection()
    
    # Use memory mapping for large state
    setup_memory_mapping()
  end
  
  defp configure_binary_protocols() do
    # Use efficient binary serialization
    Application.put_env(:nexus, :serialization, :erlang_binary)
    Application.put_env(:nexus, :compression, :lz4)
    
    # Pre-compile serialization functions
    compile_serialization_functions()
  end
  
  defp setup_memory_pools() do
    # Pre-allocate memory pools for common data structures
    :ets.new(:nexus_message_pool, [
      :named_table,
      :public,
      :bag,
      {:write_concurrency, true}
    ])
    
    # Pre-populate with common message types
    Enum.each(1..1000, fn _ ->
      :ets.insert(:nexus_message_pool, {get_pooled_message()})
    end)
  end
  
  defp optimize_garbage_collection() do
    # Configure GC for high-throughput scenarios
    :erlang.system_flag(:fullsweep_after, 0)  # Disable full sweep
    :erlang.system_flag(:min_heap_size, 8192)  # Larger initial heap
    :erlang.system_flag(:min_bin_vheap_size, 8192)  # Larger binary heap
  end
  
  def optimize_cpu_usage() do
    # Configure scheduler optimization
    configure_scheduler_optimization()
    
    # Set up CPU affinity
    setup_cpu_affinity()
    
    # Configure dirty schedulers
    configure_dirty_schedulers()
  end
  
  defp configure_scheduler_optimization() do
    # Balance CPU usage across schedulers
    scheduler_count = :erlang.system_info(:logical_processors)
    
    # Configure scheduler bind type
    :erlang.system_flag(:scheduler_bind_type, :thread_spread)
    
    # Set CPU topology
    case detect_cpu_topology() do
      {:ok, topology} ->
        :erlang.system_flag(:cpu_topology, topology)
      _ ->
        :ok
    end
  end
  
  def measure_and_optimize_performance() do
    # Continuous performance measurement and optimization
    spawn_link(fn -> performance_optimization_loop() end)
  end
  
  defp performance_optimization_loop() do
    Process.sleep(5000)  # Measure every 5 seconds
    
    # Collect performance metrics
    metrics = collect_performance_metrics()
    
    # Optimize based on current performance
    optimize_based_on_metrics(metrics)
    
    performance_optimization_loop()
  end
  
  defp collect_performance_metrics() do
    %{
      memory_usage: :erlang.memory(),
      scheduler_utilization: :scheduler.utilization(1000),
      process_count: :erlang.system_info(:process_count),
      message_queue_lengths: get_message_queue_lengths(),
      gc_statistics: get_gc_statistics()
    }
  end
  
  defp optimize_based_on_metrics(metrics) do
    cond do
      high_memory_usage?(metrics) ->
        trigger_memory_optimization()
      
      high_cpu_usage?(metrics) ->
        trigger_cpu_optimization()
      
      unbalanced_load?(metrics) ->
        trigger_load_rebalancing()
      
      true ->
        :ok
    end
  end
end

defmodule Nexus.Testing.DeterministicEmergence do
  @moduledoc """
  Deterministic emergence patterns for comprehensive testing.
  
  Features:
  - Reproducible emergent behaviors
  - Parameterized emergence scenarios
  - Automated emergence verification
  - Chaos testing integration
  """
  
  defstruct [
    :scenario_id,
    :agent_configurations,
    :interaction_rules,
    :environment_parameters,
    :expected_outcomes,
    :verification_functions
  ]
  
  def create_test_scenario(scenario_config) do
    %__MODULE__{
      scenario_id: scenario_config.id,
      agent_configurations: scenario_config.agents,
      interaction_rules: scenario_config.rules,
      environment_parameters: scenario_config.environment,
      expected_outcomes: scenario_config.expected,
      verification_functions: scenario_config.verification
    }
  end
  
  def run_deterministic_emergence(scenario) do
    # Set up deterministic environment
    test_env = setup_deterministic_environment(scenario)
    
    # Create agents with fixed random seeds
    agents = create_deterministic_agents(scenario.agent_configurations, test_env)
    
    # Run emergence simulation
    emergence_result = simulate_emergence(agents, scenario.interaction_rules, test_env)
    
    # Verify outcomes
    verification_result = verify_emergence_outcomes(emergence_result, scenario)
    
    # Clean up test environment
    cleanup_test_environment(test_env)
    
    {emergence_result, verification_result}
  end
  
  defp setup_deterministic_environment(scenario) do
    # Create isolated test environment with fixed parameters
    test_cluster = start_test_cluster(scenario.environment_parameters.node_count)
    
    # Set deterministic random seeds
    Enum.each(test_cluster.nodes, fn node ->
      :rpc.call(node, :rand, :seed, [{:exsss, scenario.environment_parameters.random_seed}])
    end)
    
    # Configure network conditions
    configure_test_network(test_cluster, scenario.environment_parameters.network)
    
    test_cluster
  end
  
  defp create_deterministic_agents(agent_configs, test_env) do
    Enum.map(agent_configs, fn config ->
      # Create agent with deterministic configuration
      node = select_deterministic_node(test_env, config.placement_seed)
      
      agent_spec = %{
        id: config.id,
        behavior: config.behavior,
        initial_state: config.initial_state,
        intelligence_level: config.intelligence_level,
        deterministic_seed: config.random_seed
      }
      
      {:ok, agent} = :rpc.call(node, Nexus.Agent, :start_link, [agent_spec])
      
      %{id: config.id, pid: agent, node: node, config: config}
    end)
  end
  
  defp simulate_emergence(agents, interaction_rules, test_env) do
    # Run deterministic emergence simulation
    simulation_state = initialize_simulation_state(agents, interaction_rules)
    
    # Execute simulation steps
    final_state = Enum.reduce(1..test_env.simulation_steps, simulation_state, fn step, state ->
      execute_simulation_step(state, step, interaction_rules)
    end)
    
    collect_emergence_data(final_state)
  end
  
  defp verify_emergence_outcomes(emergence_result, scenario) do
    Enum.map(scenario.verification_functions, fn verification_fn ->
      try do
        case verification_fn.(emergence_result, scenario.expected_outcomes) do
          :ok -> {:passed, verification_fn}
          {:error, reason} -> {:failed, verification_fn, reason}
        end
      rescue
        error -> {:error, verification_fn, error}
      end
    end)
  end
end

defmodule Nexus.Testing.ChaosEngineering do
  @moduledoc """
  Chaos engineering for testing emergence under failure conditions.
  
  Failure scenarios:
  - Node failures and network partitions
  - Message delays and corruption
  - Resource exhaustion
  - Byzantine agent behavior
  """
  
  def chaos_test_emergence(scenario, chaos_config) do
    # Start normal emergence scenario
    {agents, test_env} = setup_emergence_test(scenario)
    
    # Inject chaos according to configuration
    chaos_injector = start_chaos_injection(chaos_config, test_env)
    
    # Monitor emergence behavior under chaos
    monitor_task = Task.async(fn ->
      monitor_emergence_under_chaos(agents, scenario.expected_outcomes)
    end)
    
    # Run test with chaos
    test_result = run_emergence_with_chaos(agents, scenario, chaos_injector)
    
    # Collect monitoring results
    monitoring_result = Task.await(monitor_task, 60_000)
    
    # Stop chaos injection
    stop_chaos_injection(chaos_injector)
    
    {test_result, monitoring_result}
  end
  
  defp start_chaos_injection(chaos_config, test_env) do
    # Schedule various failure injections
    chaos_schedule = build_chaos_schedule(chaos_config)
    
    spawn_link(fn ->
      execute_chaos_schedule(chaos_schedule, test_env)
    end)
  end
  
  defp execute_chaos_schedule(schedule, test_env) do
    Enum.each(schedule, fn {delay, chaos_action} ->
      Process.sleep(delay)
      inject_chaos(chaos_action, test_env)
    end)
  end
  
  defp inject_chaos(chaos_action, test_env) do
    case chaos_action.type do
      :kill_node ->
        # Kill random node
        target_node = Enum.random(test_env.nodes)
        Logger.info("Chaos: Killing node #{target_node}")
        kill_test_node(target_node)
      
      :partition_network ->
        # Create network partition
        partition_spec = chaos_action.partition_spec
        Logger.info("Chaos: Creating network partition #{inspect(partition_spec)}")
        create_network_partition(test_env.nodes, partition_spec)
      
      :delay_messages ->
        # Inject message delays
        delay_ms = chaos_action.delay_ms
        Logger.info("Chaos: Injecting #{delay_ms}ms message delays")
        inject_message_delays(test_env, delay_ms)
      
      :corrupt_messages ->
        # Corrupt random messages
        corruption_rate = chaos_action.corruption_rate
        Logger.info("Chaos: Corrupting messages at #{corruption_rate * 100}% rate")
        inject_message_corruption(test_env, corruption_rate)
      
      :exhaust_resources ->
        # Cause resource exhaustion
        resource_type = chaos_action.resource_type
        Logger.info("Chaos: Exhausting #{resource_type} resources")
        exhaust_resources(test_env, resource_type)
      
      :byzantine_agent ->
        # Make agent behave maliciously
        target_agent = chaos_action.target_agent
        behavior = chaos_action.byzantine_behavior
        Logger.info("Chaos: Making agent #{target_agent} byzantine")
        inject_byzantine_behavior(target_agent, behavior)
    end
  end
  
  defp monitor_emergence_under_chaos(agents, expected_outcomes) do
    # Monitor emergence metrics during chaos
    monitoring_data = %{
      coordination_success_rate: [],
      response_times: [],
      error_rates: [],
      adaptation_responses: [],
      fallback_activations: []
    }
    
    Stream.interval(1000)  # Monitor every second
    |> Enum.reduce_while(monitoring_data, fn _tick, data ->
      current_metrics = collect_emergence_metrics(agents)
      
      updated_data = %{
        coordination_success_rate: [current_metrics.coordination_success | data.coordination_success_rate],
        response_times: [current_metrics.avg_response_time | data.response_times],
        error_rates: [current_metrics.error_rate | data.error_rates],
        adaptation_responses: [current_metrics.adaptations | data.adaptation_responses],
        fallback_activations: [current_metrics.fallbacks | data.fallback_activations]
      }
      
      # Check if test should continue
      if should_continue_monitoring?(updated_data, expected_outcomes) do
        {:cont, updated_data}
      else
        {:halt, updated_data}
      end
    end)
  end
end

defmodule Nexus.Production.Monitoring do
  @moduledoc """
  Production-grade monitoring and alerting for distributed agentic systems.
  
  Features:
  - Multi-dimensional metrics collection
  - Predictive alerting
  - Automatic performance regression detection
  - Distributed tracing across agent interactions
  """
  
  use GenServer
  
  def start_link(opts) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def init(_opts) do
    # Set up comprehensive telemetry
    setup_telemetry_handlers()
    
    # Initialize metrics storage
    setup_metrics_storage()
    
    # Start monitoring loops
    start_monitoring_loops()
    
    {:ok, %{}}
  end
  
  defp setup_telemetry_handlers() do
    # Attach handlers for all critical events
    events = [
      # Agent events
      [:nexus, :agent, :started],
      [:nexus, :agent, :stopped],
      [:nexus, :agent, :message_processed],
      [:nexus, :agent, :state_updated],
      [:nexus, :agent, :intelligence_adapted],
      
      # Coordination events
      [:nexus, :coordination, :initiated],
      [:nexus, :coordination, :completed],
      [:nexus, :coordination, :failed],
      [:nexus, :coordination, :fallback_triggered],
      
      # Performance events
      [:nexus, :performance, :latency_measured],
      [:nexus, :performance, :throughput_calculated],
      [:nexus, :performance, :resource_utilization],
      
      # Emergence events
      [:nexus, :emergence, :behavior_detected],
      [:nexus, :emergence, :adaptation_successful],
      [:nexus, :emergence, :safety_boundary_hit],
      [:nexus, :emergence, :emergency_fallback]
    ]
    
    Enum.each(events, fn event ->
      :telemetry.attach(event, event, &handle_telemetry_event/4, %{})
    end)
  end
  
  def handle_telemetry_event(event, measurements, metadata, _config) do
    # Store metrics in time series database
    store_metric(event, measurements, metadata)
    
    # Check alerting rules
    check_alerting_rules(event, measurements, metadata)
    
    # Update real-time dashboards
    update_dashboards(event, measurements, metadata)
    
    # Feed data to ML models for prediction
    update_prediction_models(event, measurements, metadata)
  end
  
  defp check_alerting_rules(event, measurements, metadata) do
    # Check various alerting conditions
    case event do
      [:nexus, :performance, :latency_measured] ->
        if measurements.latency > get_latency_threshold(metadata.operation) do
          trigger_alert(:high_latency, measurements, metadata)
        end
      
      [:nexus, :coordination, :failed] ->
        failure_rate = calculate_recent_failure_rate(metadata.coordination_type)
        if failure_rate > 0.1 do  # 10% failure rate threshold
          trigger_alert(:high_failure_rate, %{failure_rate: failure_rate}, metadata)
        end
      
      [:nexus, :emergence, :safety_boundary_hit] ->
        # Always alert on safety boundary hits
        trigger_alert(:emergence_safety_boundary, measurements, metadata)
      
      [:nexus, :emergence, :emergency_fallback] ->
        # Critical alert for emergency fallbacks
        trigger_critical_alert(:emergence_emergency_fallback, measurements, metadata)
    end
  end
  
  defp trigger_alert(alert_type, measurements, metadata) do
    alert = %{
      type: alert_type,
      severity: get_alert_severity(alert_type),
      timestamp: DateTime.utc_now(),
      measurements: measurements,
      metadata: metadata,
      cluster_state: get_cluster_state(),
      recommended_actions: get_recommended_actions(alert_type)
    }
    
    # Send to alerting systems
    send_to_alerting_systems(alert)
    
    # Log structured alert
    Logger.warn("Alert triggered", alert: alert)
  end
  
  defp update_prediction_models(event, measurements, metadata) do
    # Feed data to ML models for predictive alerting
    case event do
      [:nexus, :performance, :latency_measured] ->
        Nexus.ML.LatencyPredictor.update(measurements.latency, metadata)
      
      [:nexus, :performance, :resource_utilization] ->
        Nexus.ML.ResourcePredictor.update(measurements, metadata)
      
      [:nexus, :coordination, :completed] ->
        Nexus.ML.CoordinationPredictor.update(measurements.duration, metadata)
    end
  end
end

defmodule Nexus.Production.Operations do
  @moduledoc """
  Operational excellence features for production deployments.
  
  Features:
  - Zero-downtime deployments
  - Automated capacity management
  - Performance optimization
  - Incident response automation
  """
  
  def enable_zero_downtime_deployment() do
    # Implement blue-green deployment for agent systems
    current_version = get_current_version()
    new_version = get_deployment_version()
    
    Logger.info("Starting zero-downtime deployment: #{current_version} -> #{new_version}")
    
    # Phase 1: Deploy new version alongside current
    deploy_new_version_parallel(new_version)
    
    # Phase 2: Gradually migrate agents to new version
    migrate_agents_gradually(current_version, new_version)
    
    # Phase 3: Verify new version health
    case verify_deployment_health(new_version) do
      :healthy ->
        # Phase 4: Complete migration and cleanup old version
        complete_migration(current_version, new_version)
        Logger.info("Zero-downtime deployment completed successfully")
      
      {:unhealthy, reason} ->
        # Rollback to previous version
        Logger.error("Deployment health check failed: #{inspect(reason)}")
        rollback_deployment(current_version, new_version)
    end
  end
  
  defp migrate_agents_gradually(old_version, new_version) do
    # Get all active agents
    agents = Nexus.Registry.Distributed.list_agents()
    
    # Migrate in batches to minimize impact
    batch_size = calculate_migration_batch_size(length(agents))
    
    agents
    |> Enum.chunk_every(batch_size)
    |> Enum.each(fn batch ->
      # Migrate batch of agents
      Enum.each(batch, fn agent_id ->
        migrate_agent_to_new_version(agent_id, new_version)
      end)
      
      # Wait between batches for system stabilization
      Process.sleep(get_migration_delay())
      
      # Verify batch migration success
      verify_batch_migration(batch, new_version)
    end)
  end
  
  def enable_automated_capacity_management() do
    # Start capacity monitoring and management
    spawn_link(fn -> capacity_management_loop() end)
  end
  
  defp capacity_management_loop() do
    Process.sleep(30_000)  # Check every 30 seconds
    
    # Collect cluster metrics
    cluster_metrics = collect_cluster_capacity_metrics()
    
    # Determine if scaling is needed
    scaling_decision = determine_scaling_need(cluster_metrics)
    
    case scaling_decision do
      {:scale_out, reason} ->
        Logger.info("Auto-scaling out: #{inspect(reason)}")
        auto_scale_out(reason, cluster_metrics)
      
      {:scale_in, reason} ->
        Logger.info("Auto-scaling in: #{inspect(reason)}")
        auto_scale_in(reason, cluster_metrics)
      
      :no_action ->
        :ok
    end
    
    capacity_management_loop()
  end
  
  defp determine_scaling_need(metrics) do
    cond do
      metrics.cpu_utilization > 0.8 ->
        {:scale_out, :high_cpu}
      
      metrics.memory_utilization > 0.85 ->
        {:scale_out, :high_memory}
      
      metrics.agent_queue_depth > 1000 ->
        {:scale_out, :high_queue_depth}
      
      metrics.response_time_p95 > 500 ->
        {:scale_out, :high_latency}
      
      metrics.cpu_utilization < 0.3 and metrics.memory_utilization < 0.4 ->
        {:scale_in, :low_utilization}
      
      true ->
        :no_action
    end
  end
  
  def enable_automated_incident_response() do
    # Set up automated incident response
    incident_handlers = [
      {Nexus.Incidents.HighLatencyHandler, []},
      {Nexus.Incidents.NodeFailureHandler, []},
      {Nexus.Incidents.MemoryLeakHandler, []},
      {Nexus.Incidents.NetworkPartitionHandler, []}
    ]
    
    Enum.each(incident_handlers, fn {handler_module, opts} ->
      spawn_link(fn ->
        handler_module.start_monitoring(opts)
      end)
    end)
  end
end