defmodule Foundation.Agents.Agent do
  @moduledoc "A generic behaviour for a goal-oriented, reasoning agent."

  alias Foundation.Agents.{Goal, Plan, Step}

  # An agent receives a high-level goal and must return a plan.
  @callback plan(goal :: Goal.t(), state :: map()) :: {:ok, Plan.t()} | {:error, term()}

  # An agent receives a single step from a plan and must execute it.
  @callback execute_step(step :: Step.t(), state :: map()) :: {:ok, result :: term()} | {:error, term()}

  # An agent declares its capabilities for planner matching.
  @callback get_capabilities() :: [atom()]
end

defmodule Foundation.Agents.Planner do
  alias Foundation.MABEAM.{AgentRegistry, Coordination}
  alias Foundation.Agents.{Goal, Plan}

  # This is the entry point for meta-optimizers.
  def execute_goal(goal, available_agents_by_capability) do
    # 1. Decompose the goal into a logical plan (this part is simple initially)
    plan = decompose(goal)

    # 2. Find and start the necessary agents using the generic MABEAM API
    required_capabilities = Enum.map(plan.steps, & &1.required_capability)
    agent_assignments = assign_agents(required_capabilities, available_agents_by_capability)
    
    for {step, agent_id} <- agent_assignments do
      # This is a direct call to the generic MABEAM layer
      AgentRegistry.start_agent(agent_id) 
    end

    # 3. Execute the plan
    execute_plan(plan, agent_assignments)
  end

  defp execute_plan(plan, assignments) do
    # This is where the coordination logic happens.
    # It sends `:execute_step` messages to the assigned agents' PIDs,
    # respecting the plan's dependency graph.
    # It might use MABEAM.Coordination.barrier_sync to wait for parallel steps.
  end
end

defmodule ElixirML.MABEAM.Integration do
  # This module implements the generic `Foundation.Agents.Agent` behaviour
  # for DSPEx programs.

  defstruct [:program_module, :variable_config]

  @behaviour Foundation.Agents.Agent

  # `plan/2` is simple for a program-as-agent: the plan is a single step.
  @impl Foundation.Agents.Agent
  def plan(goal, state) do
    # Translate the generic goal into specific DSPEx input fields.
    dspex_inputs = translate_goal_to_inputs(goal, state.program_module)
    
    step = %Foundation.Agents.Step{
      id: :execute_program,
      action: :forward,
      params: dspex_inputs,
      required_capability: state.program_module.get_capability() # e.g., :code_generation_python
    }
    
    plan = %Foundation.Agents.Plan{steps: [step]}
    {:ok, plan}
  end

  # `execute_step/2` calls the DSPEx program's `forward` function.
  @impl Foundation.Agents.Agent
  def execute_step(step, state) do
    DSPEx.Program.forward(state.program_module, step.params, config: state.variable_config)
  end
  
  @impl Foundation.Agents.Agent
  def get_capabilities, do: # ...
end

# In DSPEx.AgentTeam.OptimizerTarget

@impl DSPEx.Teleprompter.OptimizationTarget
def evaluate(target, _training_data, metric_fn) do
  # 1. Get the architecture chosen by the optimizer for this run.
  architecture = target.current_architecture
  # e.g., %{coder: PythonCoder, reviewer: StrictReviewer}

  # 2. Build the high-level goal for the generic Foundation.Agents layer.
  goal = %Foundation.Agents.Goal{
    id: :build_feature,
    description: "Implement and review the user login feature.",
    # The Planner will use this to find agents with matching capabilities
    steps: [
      %{id: :coding, capability: :code_generation, input: "feature specs..."},
      %{id: :review, capability: :code_review, depends_on: :coding}
    ]
  }

  # 3. Define the pool of available agents for the planner.
  available_agents = %{
    code_generation: [architecture.coder],
    code_review: [architecture.reviewer]
  }

  # 4. DIRECTLY CALL the generic Foundation.Agents Planner.
  #    This is the "system call" from the ML application to the Agent OS.
  case Foundation.Agents.Planner.execute_goal(goal, available_agents) do
    {:ok, final_result} ->
      # 5. Score the result and return it to the optimizer.
      score = metric_fn.(final_result)
      {:ok, score}
    
    {:error, reason} ->
      {:error, reason}
  end
end

# lib/foundation/environment_detector.ex
defmodule Foundation.EnvironmentDetector do
  def detect_optimal_configuration() do
    %{
      environment: detect_environment(),
      infrastructure: detect_infrastructure(), 
      scale: detect_scale_requirements(),
      profile: determine_profile()
    }
  end
end

# lib/foundation/profiles.ex  
defmodule Foundation.Profiles do
  def get_profile(:development), do: development_profile()
  def get_profile(:production), do: production_profile()
  def get_profile(:enterprise), do: enterprise_profile()
end

# lib/foundation/configuration.ex
defmodule Foundation.Configuration do
  def configure_from_profile(profile) do
    # Automatically configure all tools based on profile
  end
end

# lib/foundation/strategies/mdns_lite.ex
defmodule Foundation.Strategies.MdnsLite do
  @behaviour Cluster.Strategy
  
  def start_link(opts) do
    # Configure mdns_lite for Foundation services
    configure_mdns_services()
    start_service_discovery()
  end
  
  defp configure_mdns_services() do
    MdnsLite.add_mdns_service(%{
      id: :foundation_node,
      protocol: "foundation",
      transport: "tcp",
      port: get_distribution_port(),
      txt_payload: [
        "node=#{Node.self()}",
        "foundation_version=#{Foundation.version()}",
        "capabilities=#{encode_capabilities()}"
      ]
    })
  end
end

# lib/foundation/development_cluster.ex
defmodule Foundation.DevelopmentCluster do
  def start_development_cluster() do
    # Automatic cluster formation for development
    discover_and_connect_nodes()
    setup_development_features()
  end
end

# lib/foundation/cluster_manager.ex
defmodule Foundation.ClusterManager do
  def start_clustering(config \\ :auto) do
    strategy = determine_clustering_strategy(config)
    configure_libcluster(strategy)
    start_cluster_monitoring()
  end
  
  defp determine_clustering_strategy(:auto) do
    cond do
      kubernetes_available?() -> 
        {Cluster.Strategy.Kubernetes, kubernetes_config()}
      consul_available?() -> 
        {Cluster.Strategy.Consul, consul_config()}
      dns_srv_available?() -> 
        {Cluster.Strategy.DNS, dns_config()}
      true -> 
        {Cluster.Strategy.Epmd, static_config()}
    end
  end
end

# lib/foundation/cluster_health.ex
defmodule Foundation.ClusterHealth do
  def monitor_cluster_health() do
    # Continuous cluster health monitoring
    # Automatic failover strategies
    # Performance optimization recommendations
  end
end

# lib/foundation/messaging.ex
defmodule Foundation.Messaging do
  def send_message(target, message, opts \\ []) do
    case resolve_target(target) do
      {:local, pid} -> send(pid, message)
      {:remote, node, name} -> send_remote(node, name, message, opts)
      {:service, service_name} -> route_to_service(service_name, message, opts)
      {:broadcast, topic} -> broadcast_message(topic, message, opts)
    end
  end
  
  def subscribe(topic, handler \\ self()) do
    Phoenix.PubSub.subscribe(Foundation.PubSub, topic)
    register_handler(topic, handler)
  end
end

# lib/foundation/pubsub_manager.ex
defmodule Foundation.PubSubManager do
  def configure_pubsub(profile) do
    case profile do
      :development -> configure_local_pubsub()
      :production -> configure_distributed_pubsub()
      :enterprise -> configure_federated_pubsub()
    end
  end
end

# lib/foundation/process_manager.ex
defmodule Foundation.ProcessManager do
  def start_distributed_process(module, args, opts \\ []) do
    strategy = determine_distribution_strategy(module, opts)
    
    case strategy do
      :singleton -> start_singleton_process(module, args, opts)
      :replicated -> start_replicated_process(module, args, opts)  
      :partitioned -> start_partitioned_process(module, args, opts)
      :local -> start_local_process(module, args, opts)
    end
  end
end

# lib/foundation/horde_manager.ex
defmodule Foundation.HordeManager do
  def start_child_with_retry(supervisor, child_spec, opts \\ []) do
    # Handle Horde's eventual consistency intelligently
    # Retry logic for failed registrations
    # Wait for registry sync when needed
  end
  
  def optimize_horde_performance(cluster_info) do
    # Adjust sync intervals based on cluster size
    # Optimize distribution strategies
    # Monitor and alert on consistency issues
  end
end

# lib/foundation/service_mesh.ex
defmodule Foundation.ServiceMesh do
  def register_service(name, pid, capabilities \\ []) do
    # Multi-layer registration for redundancy
    registrations = [
      register_locally(name, pid, capabilities),
      register_in_horde(name, pid, capabilities),
      announce_via_pubsub(name, pid, capabilities)
    ]
    
    validate_registrations(registrations)
  end
  
  def discover_services(criteria) do
    # Intelligent service discovery with multiple fallbacks
    local_services = discover_local_services(criteria)
    cluster_services = discover_cluster_services(criteria) 
    
    merge_and_prioritize_services(local_services, cluster_services)
  end
end

# lib/foundation/load_balancer.ex
defmodule Foundation.LoadBalancer do
  def route_to_service(service_name, message, opts \\ []) do
    instances = get_healthy_instances(service_name)
    strategy = determine_routing_strategy(instances, message, opts)
    
    route_with_strategy(instances, message, strategy)
  end
end

# lib/foundation/context.ex
defmodule Foundation.Context do
  def with_context(context_map, fun) do
    # Store context in process dictionary and telemetry
    # Automatic propagation across process boundaries
    # Integration with Phoenix.PubSub for distributed context
  end
  
  def propagate_context(target, message) do
    # Automatic context injection for all message types
    # Support for different target types (pid, via tuple, service name)
    # Context compression for large payloads
  end
end

# lib/foundation/distributed_debugging.ex  
defmodule Foundation.DistributedDebugging do
  def enable_cluster_debugging() do
    # Integration with ElixirScope for distributed debugging
    # Automatic trace correlation across nodes
    # Performance profiling across cluster
  end
end

# lib/foundation/cache_manager.ex
defmodule Foundation.CacheManager do
  def configure_cache(profile) do
    case profile do
      :development -> configure_local_cache()
      :production -> configure_distributed_cache()
      :enterprise -> configure_multi_tier_cache()
    end
  end
  
  def get_or_compute(key, computation_fn, opts \\ []) do
    # Intelligent cache hierarchy
    # Automatic cache warming
    # Distributed cache invalidation
  end
end

# lib/foundation/state_synchronization.ex
defmodule Foundation.StateSynchronization do
  def sync_state_across_cluster(state_name, initial_state) do
    # CRDT-based state synchronization
    # Conflict resolution strategies
    # Partition tolerance
  end
end

# lib/foundation/performance_optimizer.ex
defmodule Foundation.PerformanceOptimizer do
  def optimize_cluster_performance() do
    cluster_metrics = collect_cluster_metrics()
    optimizations = analyze_and_recommend(cluster_metrics)
    apply_safe_optimizations(optimizations)
  end
  
  def adaptive_scaling(service_name) do
    # Predictive scaling based on metrics
    # Automatic process distribution adjustment
    # Resource utilization optimization
  end
end

# lib/foundation/telemetry_integration.ex
defmodule Foundation.TelemetryIntegration do
  def setup_comprehensive_monitoring() do
    # Cluster-wide telemetry aggregation
    # Performance trend analysis
    # Automatic alerting on anomalies
    # Integration with external monitoring systems
  end
end

# lib/foundation/federation.ex
defmodule Foundation.Federation do
  def federate_clusters(clusters, strategy \\ :mesh) do
    case strategy do
      :mesh -> create_mesh_federation(clusters)
      :hub_spoke -> create_hub_spoke_federation(clusters)
      :hierarchical -> create_hierarchical_federation(clusters)
    end
  end
  
  def send_to_cluster(cluster_name, service_name, message) do
    # Cross-cluster communication
    # Routing strategies for different cluster types
    # Fallback mechanisms for cluster failures
  end
end

# lib/foundation/cluster_registry.ex
defmodule Foundation.ClusterRegistry do
  def register_cluster(cluster_name, config) do
    # Multi-cluster service registry
    # Cross-cluster service discovery
    # Global load balancing
  end
end

# Integration with ElixirScope
defmodule ElixirScope.Foundation.Integration do
  def enable_distributed_debugging() do
    Foundation.DistributedDebugging.register_debug_service()
    setup_cluster_wide_tracing()
  end
  
  def trace_across_cluster(trace_id) do
    Foundation.Messaging.broadcast(
      "elixir_scope:tracing",
      {:start_trace, trace_id, Foundation.Context.current_context()}
    )
  end
end

# Integration with DSPEx  
defmodule DSPEx.Foundation.Integration do
  def distribute_optimization(program, dataset, metric) do
    # Use Foundation's process distribution for AI workloads
    workers = Foundation.ProcessManager.start_process_group(
      DSPEx.Worker,
      strategy: :distributed,
      count: Foundation.Cluster.optimal_worker_count()
    )
    
    Foundation.WorkDistribution.coordinate_work(workers, dataset, metric)
  end
end

# lib/foundation/production_readiness.ex
defmodule Foundation.ProductionReadiness do
  def validate_production_config(config) do
    # Configuration validation
    # Security best practices checking
    # Performance baseline establishment
    # Capacity planning recommendations
  end
  
  def health_check() do
    # Comprehensive cluster health assessment
    # Service availability verification
    # Performance benchmark validation
  end
end

# lib/foundation/migration_helpers.ex
defmodule Foundation.MigrationHelpers do
  def migrate_from_libcluster(current_config) do
    # Seamless migration from plain libcluster
    # Configuration transformation
    # Zero-downtime upgrade path
  end
  
  def migrate_from_swarm(current_config) do
    # Migration path from Swarm to Foundation
    # Process migration strategies
    # Data preservation techniques
  end
end

# mix.exs
defp deps do
  [
    # Core clustering
    {:libcluster, "~> 3.3"},
    {:mdns_lite, "~> 0.8"},
    
    # Process distribution  
    {:horde, "~> 0.9"},
    
    # Messaging
    {:phoenix_pubsub, "~> 2.1"},
    
    # Caching
    {:nebulex, "~> 2.5"},
    
    # Observability
    {:telemetry, "~> 1.2"},
    {:telemetry_metrics, "~> 0.6"},
    
    # Optional advanced features
    {:partisan, "~> 5.0", optional: true},
    
    # Development and testing
    {:mox, "~> 1.0", only: :test},
    {:excoveralls, "~> 0.18", only: :test},
    {:credo, "~> 1.7", only: [:dev, :test]},
    {:dialyxir, "~> 1.4", only: [:dev, :test]}
  ]
end

# test/support/cluster_case.ex
defmodule Foundation.ClusterCase do
  use ExUnit.CaseTemplate
  
  using do
    quote do
      import Foundation.ClusterCase
      import Foundation.TestHelpers
    end
  end
  
  def setup_test_cluster(node_count \\ 3) do
    # Start multiple nodes for integration testing
    nodes = start_test_nodes(node_count)
    setup_foundation_on_nodes(nodes)
    
    on_exit(fn -> cleanup_test_nodes(nodes) end)
    
    %{nodes: nodes}
  end
end

# test/support/test_helpers.ex
defmodule Foundation.TestHelpers do
  def wait_for_cluster_formation(expected_nodes, timeout \\ 5000) do
    # Wait for cluster to stabilize
    wait_until(fn -> 
      Foundation.Cluster.size() == expected_nodes 
    end, timeout)
  end
  
  def simulate_network_partition(nodes_a, nodes_b) do
    # Simulate network partitions for testing
    partition_nodes(nodes_a, nodes_b)
    
    on_exit(fn -> heal_partition(nodes_a, nodes_b) end)
  end
  
  def assert_eventually(assertion, timeout \\ 5000) do
    # Assert that something becomes true within timeout
    wait_until(assertion, timeout)
    assertion.()
  end
end

# mix.exs - Just add Foundation
{:foundation, "~> 2.0"}

# lib/my_app/application.ex
defmodule MyApp.Application do
  def start(_type, _args) do
    children = [
      # Foundation automatically handles clustering in development
      {Foundation, []},
      
      # Your services work distributed automatically
      MyApp.UserService,
      MyApp.OrderService,
      MyApp.Web.Endpoint
    ]
    
    Supervisor.start_link(children, strategy: :one_for_one)
  end
end

# Start multiple nodes for development
# iex --name dev1@localhost -S mix
# iex --name dev2@localhost -S mix
# Foundation automatically discovers and connects them via mdns_lite

# config/prod.exs
config :foundation,
  cluster: :kubernetes  # That's it!

# Foundation automatically:
# - Configures libcluster with Kubernetes strategy
# - Sets up Horde for distributed processes
# - Enables Phoenix.PubSub for messaging
# - Configures Nebulex for distributed caching
# - Provides health checks and monitoring

# config/prod.exs
config :foundation,
  profile: :enterprise,
  clusters: %{
    # Web tier
    web: [
      strategy: {Cluster.Strategy.Kubernetes, [
        mode: :hostname,
        kubernetes_selector: "tier=web"
      ]},
      services: [:web_servers, :api_gateways],
      messaging: [:user_events, :api_requests]
    ],
    
    # Processing tier  
    processing: [
      strategy: {Cluster.Strategy.Kubernetes, [
        kubernetes_selector: "tier=processing"
      ]},
      services: [:background_jobs, :data_processors],
      messaging: [:job_queue, :data_events]
    ],
    
    # AI tier
    ai: [
      strategy: {Cluster.Strategy.Consul, [
        service_name: "ai-cluster"
      ]},
      services: [:ai_workers, :model_cache],
      messaging: [:inference_requests, :model_updates]
    ]
  }

# lib/my_app/user_service.ex
defmodule MyApp.UserService do
  use GenServer
  
  def start_link(opts) do
    # Foundation makes this automatically distributed
    Foundation.ProcessManager.start_distributed_process(
      __MODULE__, 
      opts,
      strategy: :singleton,  # One instance across entire cluster
      cluster: :web
    )
  end
  
  def process_user(user_id) do
    # Foundation handles service discovery and routing
    Foundation.Messaging.send_message(
      {:service, :user_service},
      {:process_user, user_id}
    )
  end
end

# Distributed debugging across Foundation cluster
defmodule MyApp.DebuggingExample do
  def debug_distributed_request(request_id) do
    # ElixirScope + Foundation = distributed debugging magic
    Foundation.Context.with_context(%{
      request_id: request_id,
      debug_session: ElixirScope.start_debug_session()
    }) do
      
      # Process request across multiple services/nodes
      user = Foundation.call_service(:user_service, {:get_user, user_id})
      order = Foundation.call_service(:order_service, {:create_order, user, items})
      
      # ElixirScope automatically traces across all nodes
      ElixirScope.complete_trace(request_id)
    end
  end
end

# Leverage Foundation for distributed AI optimization
defmodule MyApp.AIOptimization do
  def optimize_model_across_cluster() do
    # DSPEx + Foundation = distributed AI optimization
    program = create_base_program()
    dataset = load_training_dataset()
    metric = &accuracy_metric/2
    
    # Foundation automatically distributes across AI cluster
    optimized_program = Foundation.AI.distribute_optimization(
      DSPEx.BootstrapFewShot,
      program,
      dataset, 
      metric,
      cluster: :ai,
      max_workers: Foundation.Cluster.node_count(:ai) * 4
    )
    
    # Result is automatically cached across cluster
    Foundation.Cache.put("optimized_model_v#{version()}", optimized_program)
    
    optimized_program
  end
end

# Before: Manual libcluster setup
config :libcluster,
  topologies: [
    k8s: [
      strategy: Cluster.Strategy.Kubernetes,
      config: [
        mode: :hostname,
        kubernetes_node_basename: "myapp",
        kubernetes_selector: "app=myapp"
      ]
    ]
  ]

# After: Foundation orchestration
config :foundation,
  cluster: :kubernetes  # Foundation handles the rest
  
# Migration helper
Foundation.Migration.from_libcluster(existing_libcluster_config)

# Before: Swarm registration
Swarm.register_name(:my_process, MyProcess, :start_link, [args])

# After: Foundation process management  
Foundation.ProcessManager.start_distributed_process(
  MyProcess,
  args,
  name: :my_process,
  strategy: :singleton
)

# Migration helper
Foundation.Migration.from_swarm(existing_swarm_processes)

# Before: Manual distribution setup
children = [
  {Phoenix.PubSub, name: MyApp.PubSub},
  {Registry, keys: :unique, name: MyApp.Registry},
  MyApp.Worker
]

# After: Foundation orchestration
children = [
  {Foundation, []},  # Handles PubSub, Registry, clustering
  MyApp.Worker      # Automatically gets distributed capabilities
]

# lib/foundation/health_monitor.ex
defmodule Foundation.HealthMonitor do
  def cluster_health_check() do
    %{
      cluster_status: check_cluster_status(),
      node_health: check_all_nodes(),
      service_health: check_all_services(),
      performance_metrics: get_performance_summary(),
      recommendations: generate_health_recommendations()
    }
  end
  
  defp check_cluster_status() do
    %{
      expected_nodes: Foundation.Config.get(:expected_nodes),
      actual_nodes: Foundation.Cluster.size(),
      partitions: detect_network_partitions(),
      last_topology_change: get_last_topology_change()
    }
  end
end

# Web endpoint for health checks
# GET /health -> Foundation.HealthMonitor.cluster_health_check()

# lib/foundation/performance_monitor.ex
defmodule Foundation.PerformanceMonitor do
  def collect_cluster_metrics() do
    %{
      message_throughput: measure_message_throughput(),
      service_latency: measure_service_latencies(),
      resource_utilization: measure_resource_usage(),
      cache_hit_rates: measure_cache_performance(),
      horde_sync_times: measure_horde_performance()
    }
  end
  
  def performance_dashboard() do
    # Real-time performance dashboard
    metrics = collect_cluster_metrics()
    trends = analyze_performance_trends()
    alerts = check_performance_alerts()
    
    %{
      current_metrics: metrics,
      trends: trends,
      alerts: alerts,
      recommendations: generate_performance_recommendations(metrics)
    }
  end
end

# lib/foundation/disaster_recovery.ex
defmodule Foundation.DisasterRecovery do
  def backup_cluster_state() do
    %{
      cluster_topology: Foundation.Cluster.get_topology(),
      service_registry: Foundation.ServiceMesh.export_registry(),
      process_state: Foundation.ProcessManager.export_state(),
      configuration: Foundation.Config.export_config()
    }
  end
  
  def restore_cluster_state(backup) do
    # Restore cluster from backup
    Foundation.Cluster.restore_topology(backup.cluster_topology)
    Foundation.ServiceMesh.import_registry(backup.service_registry)
    Foundation.ProcessManager.restore_state(backup.process_state)
    Foundation.Config.import_config(backup.configuration)
  end
  
  def failover_to_backup_cluster(backup_cluster_config) do
    # Automatic failover to backup cluster
    current_state = backup_cluster_state()
    connect_to_backup_cluster(backup_cluster_config)
    restore_cluster_state(current_state)
  end
end

# lib/foundation/mabeam/core.ex
defmodule Foundation.MABEAM.Core do
  @moduledoc """
  Universal Variable Orchestrator integrated with Foundation's distributed infrastructure.
  
  Leverages Foundation.ProcessManager for agent distribution and 
  Foundation.Channels for coordination messaging.
  """
  
  def register_orchestration_variable(variable) do
    # Register coordination variable in Foundation's service mesh
    Foundation.ServiceMesh.register_service(
      {:orchestration_variable, variable.id},
      %{
        type: :coordination_variable,
        variable: variable,
        capabilities: variable.coordination_capabilities
      }
    )
  end
  
  def coordinate_system(variable_id, context \\ %{}) do
    # Use Foundation's messaging for coordination
    agents = Foundation.ServiceMesh.discover_services(
      capabilities: [:agent_coordination],
      variable_id: variable_id
    )
    
    Foundation.Channels.coordinate_consensus(agents, context)
  end
end

# Integration with Foundation.ProcessManager
defmodule Foundation.ProcessManager do
  # Enhanced with MABEAM agent patterns
  def start_agent(agent_spec, coordination_opts \\ []) do
    case coordination_opts[:strategy] do
      :singleton -> start_singleton_agent(agent_spec, coordination_opts)
      :replicated -> start_replicated_agent(agent_spec, coordination_opts)
      :ecosystem -> start_agent_ecosystem(agent_spec, coordination_opts)
    end
  end
end

# lib/foundation/mabeam/agent_registry.ex
defmodule Foundation.MABEAM.AgentRegistry do
  @moduledoc """
  Agent registry that extends Foundation.ProcessManager with 
  coordination-specific capabilities.
  """
  
  def register_agent(agent_id, config) do
    # Register with Foundation's service mesh
    Foundation.ServiceMesh.register_service(agent_id, %{
      type: :coordination_agent,
      capabilities: config.coordination_capabilities,
      resource_requirements: config.resource_requirements,
      coordination_protocols: config.supported_protocols
    })
    
    # Start agent using Foundation's process management
    Foundation.ProcessManager.start_distributed_process(
      config.module,
      config.args,
      strategy: config.distribution_strategy,
      name: agent_id
    )
  end
end

# lib/foundation/mabeam/coordination.ex
defmodule Foundation.MABEAM.Coordination do
  def coordinate_consensus(agents, proposal, opts \\ []) do
    # Use Foundation.Channels for coordination messaging
    Foundation.Channels.broadcast(:coordination, {:consensus_proposal, proposal})
    
    # Collect votes using Foundation's messaging infrastructure
    collect_consensus_votes(agents, proposal, opts)
  end
  
  def run_auction(auction_spec, bidders, opts \\ []) do
    # Leverage Foundation's service discovery for bidder lookup
    active_bidders = Foundation.ServiceMesh.discover_services(
      capabilities: [:auction_bidding],
      ids: bidders
    )
    
    execute_auction_protocol(auction_spec, active_bidders, opts)
  end
end

# lib/foundation/mabeam/telemetry.ex
defmodule Foundation.MABEAM.Telemetry do
  @moduledoc """
  MABEAM telemetry that extends Foundation.Telemetry with 
  agent-specific metrics and coordination analytics.
  """
  
  def record_coordination_event(event_type, agents, result, duration) do
    # Use Foundation's telemetry infrastructure
    Foundation.Telemetry.emit(
      [:mabeam, :coordination, event_type],
      %{
        agent_count: length(agents),
        result: result,
        duration: duration
      },
      %{
        coordination_type: event_type,
        cluster_size: Foundation.Cluster.size(),
        node: Node.self()
      }
    )
  end
end

# Enhanced Foundation.Channels with coordination capabilities
defmodule Foundation.Channels do
  # Existing message routing...
  
  # NEW: Coordination-specific message routing
  def coordinate_consensus(agents, proposal, opts \\ []) do
    broadcast(:coordination, {:consensus_start, proposal})
    collect_responses(agents, :consensus_vote, opts)
  end
  
  def run_auction(auction_spec, bidders, opts \\ []) do
    broadcast(:coordination, {:auction_start, auction_spec})
    collect_bids(bidders, auction_spec, opts)
  end
end

# Enhanced ElixirScope integration
defmodule ElixirScope.MultiAgentTracer do
  def trace_coordination_protocol(protocol_id, agents) do
    # Use Foundation.MABEAM.Telemetry for coordination tracing
    Foundation.MABEAM.Telemetry.start_coordination_trace(protocol_id, agents)
    
    # Leverage Foundation's distributed tracing
    Foundation.Channels.broadcast(:tracing, {:trace_coordination, protocol_id})
  end
  
  def analyze_agent_decisions(agent_id, decision_context) do
    # Trace agent decision-making process
    agent_state = Foundation.MABEAM.AgentRegistry.get_agent_state(agent_id)
    coordination_history = Foundation.MABEAM.Telemetry.get_coordination_history(agent_id)
    
    correlate_decisions_with_coordination(agent_state, coordination_history, decision_context)
  end
end

# Enhanced DSPEx integration
defmodule DSPEx.MultiAgentOptimizer do
  def optimize_with_coordination(program, dataset, metric) do
    # Create optimization agents
    agents = Foundation.MABEAM.AgentRegistry.start_optimization_agents(
      count: Foundation.Cluster.size(),
      capabilities: [:dsp_optimization, :auction_bidding]
    )
    
    # Coordinate optimization using auction mechanism
    optimization_results = Foundation.MABEAM.Coordination.run_auction(
      %{
        type: :optimization_auction,
        objective: metric,
        constraints: %{max_time: 300, max_memory: 2048}
      },
      agents
    )
    
    select_best_optimization(optimization_results)
  end
end