# Ideal: Agent operations are location-agnostic
agent = Agent.get("worker-123")
result = Agent.execute(agent, action, params)
# ↑ Should work whether agent is local, remote, or migrating

# Ideal: Async by default, sync when explicitly needed
Agent.send_async(agent, message)                    # Fire and forget
Agent.request_async(agent, request) |> await()      # Async request/response  
Agent.call_sync(agent, request, timeout)            # Explicit sync when needed

# Ideal: Graceful degradation during partitions
case Agent.execute(agent, action, params) do
  {:ok, result} -> result
  {:error, :partition} -> use_cached_or_degrade_gracefully()
  {:error, :timeout} -> retry_with_backoff()
end

# Ideal: Linear scaling with node additions
cluster_capacity = NodeCount * avg_node_capacity
agent_placement = consistent_hash(agent_id, available_nodes)

# Ideal: Rich telemetry and tracing
:telemetry.span([:agent, :execute], metadata, fn ->
  # Automatic distributed tracing
  # Performance metrics collection
  # Error tracking and correlation
end)

defmodule DistributedAgents.Registry do
  @moduledoc """
  Cluster-aware agent registry using consistent hashing and replication.
  
  Key features:
  - Consistent hashing for agent placement
  - Replication for fault tolerance  
  - Dynamic rebalancing on topology changes
  - O(1) lookups with minimal network calls
  """
  
  @type agent_id :: String.t()
  @type location :: {:local, pid()} | {:remote, node(), pid()} | {:migrating, old_node(), new_node()}
  
  @doc "Register agent with automatic placement"
  @spec register_agent(agent_id(), agent_spec()) :: {:ok, location()} | {:error, term()}
  def register_agent(agent_id, agent_spec) do
    # Use consistent hashing to determine placement
    target_node = determine_placement(agent_id, agent_spec)
    
    case target_node do
      ^node() -> register_locally(agent_id, agent_spec)
      remote_node -> register_remotely(remote_node, agent_id, agent_spec)
    end
  end
  
  @doc "Find agent location with caching"
  @spec locate_agent(agent_id()) :: {:ok, location()} | {:error, :not_found}
  def locate_agent(agent_id) do
    # Check local cache first
    case Agent.LocationCache.get(agent_id) do
      {:ok, location} -> verify_and_return(location)
      :miss -> cluster_lookup(agent_id)
    end
  end
  
  @doc "Migrate agent to new node"
  @spec migrate_agent(agent_id(), target_node()) :: {:ok, location()} | {:error, term()}
  def migrate_agent(agent_id, target_node) do
    # Coordinate migration with zero downtime
    with {:ok, current_location} <- locate_agent(agent_id),
         {:ok, checkpoint} <- checkpoint_agent(agent_id),
         {:ok, new_pid} <- start_agent_on_node(target_node, checkpoint),
         :ok <- transition_traffic(agent_id, current_location, new_pid),
         :ok <- cleanup_old_agent(current_location) do
      {:ok, {:local, new_pid}}
    end
  end
  
  # Implementation uses consistent hashing ring with configurable replication
  defp determine_placement(agent_id, agent_spec) do
    ring = ClusterTopology.get_hash_ring()
    hash = :erlang.phash2(agent_id)
    
    # Consider resource requirements for placement
    candidates = HashRing.find_nodes(ring, hash, replication_factor())
    ResourceScheduler.select_best_node(candidates, agent_spec.resources)
  end
end

defmodule DistributedAgents.Messaging do
  @moduledoc """
  High-performance message passing with delivery guarantees.
  
  Features:
  - Multiple delivery semantics (at-most-once, at-least-once, exactly-once)
  - Message ordering guarantees where needed
  - Automatic retry with exponential backoff
  - Circuit breakers for failing destinations
  - Message compression and batching
  """
  
  @type delivery_guarantee :: :at_most_once | :at_least_once | :exactly_once
  @type message_options :: [
    delivery: delivery_guarantee(),
    timeout: timeout(),
    priority: :low | :normal | :high,
    ordering: boolean(),
    compression: boolean()
  ]
  
  @doc "Send message with delivery guarantees"
  @spec send_message(agent_id(), message(), message_options()) :: 
    :ok | {:ok, message_id()} | {:error, term()}
  def send_message(agent_id, message, opts \\ []) do
    delivery = Keyword.get(opts, :delivery, :at_most_once)
    
    with {:ok, location} <- DistributedAgents.Registry.locate_agent(agent_id),
         {:ok, route} <- determine_route(location, opts),
         {:ok, envelope} <- create_envelope(message, opts) do
      
      case delivery do
        :at_most_once -> fire_and_forget(route, envelope)
        :at_least_once -> reliable_send(route, envelope, opts)
        :exactly_once -> exactly_once_send(route, envelope, opts)
      end
    end
  end
  
  @doc "Request-response pattern with timeout"
  @spec request(agent_id(), request(), timeout()) :: {:ok, response()} | {:error, term()}
  def request(agent_id, request, timeout \\ 5000) do
    correlation_id = generate_correlation_id()
    
    with :ok <- send_message(agent_id, {:request, correlation_id, request}, 
                            delivery: :at_least_once, timeout: timeout),
         {:ok, response} <- await_response(correlation_id, timeout) do
      {:ok, response}
    end
  end
  
  @doc "Async request returning a future"
  @spec request_async(agent_id(), request()) :: Future.t()
  def request_async(agent_id, request) do
    Future.new(fn -> request(agent_id, request) end)
  end
  
  # Message routing optimizations
  defp determine_route({:local, pid}, _opts) do
    {:ok, {:local_send, pid}}
  end
  
  defp determine_route({:remote, node, pid}, opts) do
    case NetworkTopology.latency(node) do
      latency when latency < 5 -> {:ok, {:direct_send, node, pid}}
      _high_latency -> {:ok, {:batched_send, node, pid}}
    end
  end
  
  # Exactly-once delivery using distributed coordination
  defp exactly_once_send(route, envelope, opts) do
    message_id = envelope.id
    
    case MessageDeduplication.check_and_mark(message_id) do
      :already_delivered -> {:ok, message_id}
      :new_message -> 
        with :ok <- reliable_send(route, envelope, opts),
             :ok <- MessageDeduplication.mark_delivered(message_id) do
          {:ok, message_id}
        end
    end
  end
end

defmodule DistributedAgents.State do
  @moduledoc """
  Distributed state management with configurable consistency guarantees.
  
  Supports:
  - Strong consistency (CP in CAP theorem)
  - Eventual consistency (AP in CAP theorem)  
  - Session consistency (bounded staleness)
  - Conflict-free replicated data types (CRDTs)
  """
  
  @type consistency_level :: :strong | :eventual | :session | :crdt
  @type state_options :: [
    consistency: consistency_level(),
    replication_factor: pos_integer(),
    partition_strategy: :pause | :continue | :degraded
  ]
  
  defstruct [
    :agent_id,
    :version,              # Vector clock for ordering
    :data,                 # Actual state data  
    :replicas,             # Where replicas are stored
    :consistency_level,    # Consistency guarantees
    :last_modified,        # Timestamp for conflict resolution
    :conflict_resolution   # Strategy for handling conflicts
  ]
  
  @doc "Update state with chosen consistency level"
  @spec update_state(agent_id(), update_function(), state_options()) :: 
    {:ok, new_state()} | {:error, term()}
  def update_state(agent_id, update_fn, opts \\ []) do
    consistency = Keyword.get(opts, :consistency, :eventual)
    
    case consistency do
      :strong -> strong_consistency_update(agent_id, update_fn, opts)
      :eventual -> eventual_consistency_update(agent_id, update_fn, opts)
      :session -> session_consistency_update(agent_id, update_fn, opts)
      :crdt -> crdt_update(agent_id, update_fn, opts)
    end
  end
  
  # Strong consistency: coordinate with all replicas before committing
  defp strong_consistency_update(agent_id, update_fn, opts) do
    replication_factor = Keyword.get(opts, :replication_factor, 3)
    replicas = StateLocator.get_replicas(agent_id, replication_factor)
    
    # Two-phase commit across replicas
    case TwoPhaseCommit.coordinate(replicas, agent_id, update_fn) do
      {:ok, new_state} -> 
        StateCache.invalidate(agent_id)
        {:ok, new_state}
      {:error, :conflict} -> 
        resolve_conflict_and_retry(agent_id, update_fn, replicas)
      error -> error
    end
  end
  
  # Eventual consistency: update locally and propagate asynchronously
  defp eventual_consistency_update(agent_id, update_fn, opts) do
    with {:ok, current_state} <- get_local_state(agent_id),
         {:ok, new_state} <- apply_update(current_state, update_fn),
         :ok <- store_local_state(agent_id, new_state),
         :ok <- async_propagate_to_replicas(agent_id, new_state, opts) do
      {:ok, new_state}
    end
  end
  
  # CRDT-based updates: conflict-free by design
  defp crdt_update(agent_id, update_fn, opts) do
    crdt_type = determine_crdt_type(opts)
    
    with {:ok, current_crdt} <- get_crdt_state(agent_id, crdt_type),
         {:ok, updated_crdt} <- CRDT.update(current_crdt, update_fn),
         :ok <- replicate_crdt_update(agent_id, updated_crdt, opts) do
      {:ok, CRDT.value(updated_crdt)}
    end
  end
  
  @doc "Resolve conflicts using configured strategy"
  def resolve_conflict(agent_id, conflicting_states, strategy \\ :last_write_wins) do
    case strategy do
      :last_write_wins -> 
        Enum.max_by(conflicting_states, & &1.last_modified)
      :vector_clock ->
        VectorClock.resolve_conflicts(conflicting_states)
      :merge_semantic ->
        SemanticMerge.merge_states(conflicting_states)
      custom_resolver when is_function(custom_resolver) ->
        custom_resolver.(conflicting_states)
    end
  end
end

defmodule DistributedAgents.Lifecycle do
  @moduledoc """
  Manages agent lifecycles across the cluster with supervision and migration.
  
  Features:
  - Automatic agent placement and load balancing
  - Health monitoring and automatic restart
  - Zero-downtime migration between nodes
  - Resource quota management
  - Graceful shutdown coordination
  """
  
  @type agent_spec :: %{
    id: String.t(),
    module: module(),
    args: term(),
    resources: resource_requirements(),
    placement_hints: placement_hints(),
    supervision_strategy: supervision_strategy()
  }
  
  @doc "Spawn agent with automatic placement"
  @spec spawn_agent(agent_spec()) :: {:ok, agent_ref()} | {:error, term()}
  def spawn_agent(agent_spec) do
    with {:ok, target_node} <- select_placement_node(agent_spec),
         {:ok, resources} <- reserve_resources(target_node, agent_spec.resources),
         {:ok, agent_ref} <- start_agent_on_node(target_node, agent_spec),
         :ok <- register_for_monitoring(agent_ref),
         :ok <- update_cluster_state(agent_ref, target_node) do
      {:ok, agent_ref}
    else
      {:error, :no_capacity} -> {:error, :cluster_at_capacity}
      {:error, :resource_unavailable} -> queue_for_later_placement(agent_spec)
      error -> error
    end
  end
  
  @doc "Migrate agent to different node"
  @spec migrate_agent(agent_id(), target_node(), migration_options()) :: 
    {:ok, new_location()} | {:error, term()}
  def migrate_agent(agent_id, target_node, opts \\ []) do
    migration_strategy = Keyword.get(opts, :strategy, :checkpoint_restore)
    
    case migration_strategy do
      :checkpoint_restore -> checkpoint_migration(agent_id, target_node)
      :state_transfer -> state_transfer_migration(agent_id, target_node)
      :cold_start -> cold_start_migration(agent_id, target_node)
    end
  end
  
  # Checkpoint-based migration with zero downtime
  defp checkpoint_migration(agent_id, target_node) do
    with {:ok, current_location} <- DistributedAgents.Registry.locate_agent(agent_id),
         {:ok, checkpoint} <- create_checkpoint(agent_id),
         {:ok, new_agent} <- restore_from_checkpoint(target_node, checkpoint),
         :ok <- coordinate_traffic_switch(agent_id, current_location, new_agent),
         :ok <- cleanup_old_agent(current_location) do
      {:ok, new_agent}
    end
  end
  
  @doc "Monitor agent health and trigger actions"
  def monitor_agent_health(agent_id) do
    # Continuous health monitoring
    spawn_monitor(fn ->
      health_check_loop(agent_id)
    end)
  end
  
  defp health_check_loop(agent_id) do
    case perform_health_check(agent_id) do
      :healthy -> 
        :timer.sleep(health_check_interval())
        health_check_loop(agent_id)
        
      {:unhealthy, reason} ->
        handle_unhealthy_agent(agent_id, reason)
        
      {:unreachable, reason} ->
        handle_unreachable_agent(agent_id, reason)
    end
  end
  
  defp handle_unhealthy_agent(agent_id, reason) do
    case reason do
      :high_memory -> attempt_memory_cleanup(agent_id)
      :high_cpu -> throttle_agent_requests(agent_id)
      :unresponsive -> restart_agent(agent_id)
      :corrupted_state -> restore_from_backup(agent_id)
    end
  end
end

# Fire-and-forget messaging
Agent.send(target_agent, {:task, task_data})

# Request-response with futures
future = Agent.request_async(target_agent, {:compute, params})
result = Future.await(future, timeout: 10_000)

# Pub-sub for event distribution
EventBus.subscribe("agent.lifecycle.*")
EventBus.publish("agent.lifecycle.started", %{agent_id: "worker-123"})

# Work stealing for load balancing
defmodule WorkStealer do
  def request_work(requesting_agent) do
    overloaded_agents = find_overloaded_agents()
    
    Enum.find_value(overloaded_agents, fn agent ->
      case Agent.request(agent, :steal_work, timeout: 1000) do
        {:ok, work} -> work
        _ -> nil
      end
    end)
  end
end

# Pipeline patterns for data processing
defmodule Pipeline do
  def start_pipeline(data, stages) do
    stages
    |> Enum.reduce(Future.resolved(data), fn stage, acc_future ->
      Future.then(acc_future, &stage.process/1)
    end)
  end
end

# Barrier synchronization
defmodule DistributedBarrier do
  def wait_for_all(participants, timeout \\ 30_000) do
    barrier_id = generate_barrier_id()
    
    participants
    |> Enum.map(&Agent.send_async(&1, {:barrier_wait, barrier_id}))
    |> Future.all(timeout: timeout)
  end
end

# Leader election for coordination
defmodule LeaderElection do
  def elect_leader(candidates) do
    # Use Raft or similar consensus algorithm
    RaftConsensus.elect_leader(candidates)
  end
end

# Agent state is composed of multiple layers
defmodule AgentState do
  defstruct [
    :local_state,      # Fast, local-only state
    :shared_state,     # Replicated across cluster
    :persistent_state, # Durable storage
    :cached_state      # Performance optimization
  ]
  
  def update(state, update_type, data) do
    case update_type do
      :local -> update_local_state(state, data)
      :shared -> update_shared_state(state, data)  
      :persistent -> update_persistent_state(state, data)
    end
  end
end

defmodule EventStore do
  def append_event(agent_id, event) do
    # Store event with vector clock timestamp
    event_with_metadata = %{
      event: event,
      agent_id: agent_id,
      timestamp: VectorClock.now(),
      node: node(),
      correlation_id: get_correlation_id()
    }
    
    EventLog.append(agent_id, event_with_metadata)
  end
  
  def rebuild_state(agent_id, target_version \\ :latest) do
    agent_id
    |> EventLog.stream(until: target_version)
    |> Enum.reduce(initial_state(), &apply_event/2)
  end
end

# Use CRDTs for naturally mergeable state
defmodule CRDTState do
  def increment_counter(agent_id, amount) do
    # G-Counter for increment-only counters
    CRDT.GCounter.increment(agent_id, amount)
  end
  
  def add_to_set(agent_id, element) do
    # G-Set for grow-only sets
    CRDT.GSet.add(agent_id, element)
  end
  
  def update_map(agent_id, key, value) do
    # OR-Map for last-writer-wins maps
    CRDT.ORMap.put(agent_id, key, value)
  end
end

defmodule CircuitBreaker do
  def call(circuit_name, operation, opts \\ []) do
    case get_circuit_state(circuit_name) do
      :closed -> 
        execute_with_monitoring(operation, circuit_name)
      :open -> 
        {:error, :circuit_open}
      :half_open -> 
        attempt_recovery(operation, circuit_name)
    end
  end
  
  defp execute_with_monitoring(operation, circuit_name) do
    start_time = System.monotonic_time()
    
    case operation.() do
      {:ok, result} -> 
        record_success(circuit_name, start_time)
        {:ok, result}
      {:error, reason} -> 
        record_failure(circuit_name, reason)
        potentially_open_circuit(circuit_name)
        {:error, reason}
    end
  end
end

defmodule ResourcePools do
  def execute_in_pool(pool_name, operation) do
    case Pool.checkout(pool_name) do
      {:ok, resource} ->
        try do
          operation.(resource)
        after
          Pool.checkin(pool_name, resource)
        end
      {:error, :pool_exhausted} ->
        {:error, :resource_unavailable}
    end
  end
end

defmodule PartitionHandler do
  def handle_partition(partition_event) do
    case determine_partition_strategy(partition_event) do
      :pause_minority ->
        if in_minority_partition?() do
          pause_non_critical_agents()
        end
        
      :continue_degraded ->
        enable_degraded_mode()
        use_cached_data_where_possible()
        
      :split_brain_allowed ->
        continue_normal_operation()
        queue_for_later_reconciliation()
    end
  end
end

defmodule Scheduler do
  def schedule_agent(agent_spec) do
    available_nodes = get_available_nodes()
    
    best_node = available_nodes
    |> Enum.filter(&has_required_resources?(&1, agent_spec))
    |> Enum.min_by(&calculate_load_score/1)
    
    {:ok, best_node}
  end
  
  defp calculate_load_score(node) do
    %{cpu: cpu, memory: memory, network: network} = get_node_metrics(node)
    
    # Weighted score considering multiple factors
    cpu * 0.4 + memory * 0.4 + network * 0.2
  end
end

defmodule LoadBalancer do
  def route_request(request, target_agents) do
    # Use consistent hashing with bounded loads
    primary = consistent_hash_select(request.id, target_agents)
    
    case check_load(primary) do
      :acceptable -> route_to(primary, request)
      :overloaded -> route_to_least_loaded(target_agents, request)
    end
  end
end

defmodule DistributedCache do
  def get_or_compute(key, compute_fn, opts \\ []) do
    case get_from_cache(key) do
      {:ok, value} -> {:ok, value}
      :miss -> 
        case compute_fn.() do
          {:ok, value} -> 
            put_in_cache(key, value, opts)
            {:ok, value}
          error -> error
        end
    end
  end
end

# Supervision tree for distributed agent system
defmodule DistributedAgents.Application do
  use Application
  
  def start(_type, _args) do
    children = [
      # Cluster membership and topology
      {ClusterTopology, cluster_config()},
      
      # Distributed registry
      {DistributedAgents.Registry, registry_config()},
      
      # Message passing infrastructure  
      {DistributedAgents.Messaging, messaging_config()},
      
      # State management
      {DistributedAgents.State, state_config()},
      
      # Agent lifecycle management
      {DistributedAgents.Lifecycle, lifecycle_config()},
      
      # Performance monitoring
      {DistributedAgents.Telemetry, telemetry_config()},
      
      # Load balancing and scheduling
      {DistributedAgents.Scheduler, scheduler_config()}
    ]
    
    opts = [strategy: :one_for_one, name: DistributedAgents.Supervisor]
    Supervisor.start_link(children, opts)
  end
end

defmodule DistributedAgents.Agent do
  @moduledoc """
  Base agent implementation for distributed environment.
  """
  
  use GenServer
  require Logger
  
  defstruct [
    :id,
    :spec,
    :state,
    :location,
    :health_status,
    :performance_metrics
  ]
  
  def start_link(agent_spec) do
    agent = %__MODULE__{
      id: agent_spec.id,
      spec: agent_spec,
      state: agent_spec.initial_state,
      location: {:local, node()},
      health_status: :initializing
    }
    
    GenServer.start_link(__MODULE__, agent, name: via_tuple(agent_spec.id))
  end
  
  def init(agent) do
    # Register with distributed registry
    :ok = DistributedAgents.Registry.register_agent(agent.id, self())
    
    # Start health monitoring
    :ok = DistributedAgents.Lifecycle.monitor_agent_health(agent.id)
    
    # Initialize telemetry
    :telemetry.execute([:distributed_agents, :agent, :started], %{}, %{agent_id: agent.id})
    
    {:ok, %{agent | health_status: :healthy}}
  end
  
  def handle_call({:execute, action, params}, from, state) do
    # Async execution with telemetry
    Task.start(fn ->
      result = execute_action(action, params, state)
      GenServer.reply(from, result)
    end)
    
    {:noreply, state}
  end
  
  def handle_cast({:message, message}, state) do
    # Handle async messages
    new_state = process_message(message, state)
    {:noreply, new_state}
  end
  
  defp via_tuple(agent_id) do
    {:via, DistributedAgents.Registry, agent_id}
  end
end