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20250712 JIDO NATIVE CLUSTERING DESIGN

Documentation for 20250712_JIDO_NATIVE_CLUSTERING_DESIGN from the Foundation repository.

Jido-Native Clustering Design: Agent-First Distributed ML Platform

Date: July 12, 2025
Status: Technical Design Specification
Scope: Complete clustering architecture built on Jido-first principles
Context: Distributed agent coordination for DSPEx platform

Executive Summary

This document specifies a revolutionary clustering architecture built on Jido-first principles where every clustering function is implemented as agents rather than traditional infrastructure services. This approach leverages Jido’s native signal-based communication and agent supervision to create a more resilient, scalable, and maintainable distributed system that perfectly supports the Cognitive Variables and DSPEx ML workflows.

Revolutionary Clustering Philosophy

Traditional Clustering Limitations

Traditional Approach: Infrastructure-heavy clustering with complex service layers

# Traditional clustering - complex service dependencies
Application.start() ->
  Cluster.NodeManager.start() ->
    Cluster.ServiceDiscovery.start() ->
      Cluster.LoadBalancer.start() ->
        Cluster.HealthMonitor.start() ->
          App.start()

Problems:

  • Complex service dependency chains
  • Difficult failure isolation
  • Hard to reason about distributed state
  • Infrastructure-first rather than application-first design

Jido-Native Clustering Revolution

Agent-First Approach: Every clustering function is an intelligent agent

# Revolutionary approach - agents all the way down
DSPEx.Clustering.Supervisor
├── DSPEx.Clustering.Agents.NodeDiscovery     # Node discovery as agent
├── DSPEx.Clustering.Agents.LoadBalancer      # Load balancing as agent  
├── DSPEx.Clustering.Agents.HealthMonitor     # Health monitoring as agent
├── DSPEx.Clustering.Agents.FailureDetector   # Failure detection as agent
├── DSPEx.Clustering.Agents.ConsistencyManager # Consistency as agent
└── DSPEx.Clustering.Agents.NetworkPartitionHandler # Partition handling as agent

Advantages:

  • ✅ Unified Mental Model: Everything is an agent with actions, sensors, signals
  • ✅ Natural Fault Isolation: Agent failures don’t cascade
  • ✅ Signal-Based Communication: All coordination via Jido signals
  • ✅ Self-Healing: Agents can detect and recover from failures autonomously
  • ✅ Easy Testing: Mock agents for testing distributed scenarios
  • ✅ Incremental Deployment: Add clustering agents gradually

Core Clustering Agent Architecture

1. Node Discovery Agent - Intelligent Network Awareness

defmodule DSPEx.Clustering.Agents.NodeDiscovery do
  @moduledoc """
  Intelligent node discovery and network topology management
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Clustering.Actions.DiscoverNodes,
    DSPEx.Clustering.Actions.AnnouncePresence,
    DSPEx.Clustering.Actions.ValidateNodes,
    DSPEx.Clustering.Actions.UpdateTopology,
    DSPEx.Clustering.Actions.HandleNodeFailure,
    DSPEx.Clustering.Actions.OptimizeNetworkLayout
  ]
  
  @sensors [
    DSPEx.Clustering.Sensors.NetworkScanSensor,
    DSPEx.Clustering.Sensors.NodeHealthSensor,
    DSPEx.Clustering.Sensors.LatencyMeasurementSensor,
    DSPEx.Clustering.Sensors.BandwidthMonitorSensor
  ]
  
  @skills [
    DSPEx.Clustering.Skills.TopologyOptimization,
    DSPEx.Clustering.Skills.NetworkAnalysis,
    DSPEx.Clustering.Skills.FailurePrediction
  ]
  
  defstruct [
    :cluster_topology,        # Current cluster network topology
    :discovered_nodes,        # Map of discovered nodes and their capabilities
    :network_metrics,         # Network performance metrics between nodes
    :discovery_strategies,    # Available node discovery strategies
    :topology_history,        # Historical topology changes
    :failure_predictions,     # Predicted node failures based on metrics
    :optimization_config,     # Network topology optimization settings
    :announcement_frequency   # How often to announce presence
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      cluster_topology: %{nodes: [node()], connections: %{}},
      discovered_nodes: %{},
      network_metrics: %{},
      discovery_strategies: opts[:discovery_strategies] || [:multicast, :dns, :consul],
      topology_history: [],
      failure_predictions: %{},
      optimization_config: opts[:optimization] || %{enabled: true, frequency: 60_000},
      announcement_frequency: opts[:announcement_frequency] || 30_000
    }
    
    # Start node discovery cycle
    :timer.send_interval(initial_state.announcement_frequency, :announce_presence)
    :timer.send_interval(10_000, :discover_nodes)
    :timer.send_interval(60_000, :validate_topology)
    
    # Subscribe to cluster signals
    Jido.Signal.Bus.subscribe(self(), :cluster_events)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_signal({:node_announcement, node_info}, agent) do
    case validate_node_announcement(node_info, agent.state) do
      {:valid, validated_node} ->
        add_discovered_node(validated_node, agent.state)
        
      {:invalid, reason} ->
        log_invalid_node_announcement(node_info, reason)
        {:ok, agent}
    end
  end
  
  def handle_signal({:node_failure_detected, failed_node}, agent) do
    update_topology_for_node_failure(failed_node, agent.state)
  end
  
  def handle_signal({:network_partition_detected, partition_info}, agent) do
    handle_network_partition(partition_info, agent.state)
  end
  
  def handle_info(:announce_presence, agent) do
    announce_node_presence(agent.state)
    {:ok, agent}
  end
  
  def handle_info(:discover_nodes, agent) do
    case discover_new_nodes(agent.state) do
      {:nodes_discovered, new_nodes} ->
        updated_state = integrate_new_nodes(new_nodes, agent.state)
        {:ok, %{agent | state: updated_state}}
        
      {:no_new_nodes, _} ->
        {:ok, agent}
    end
  end
  
  def handle_info(:validate_topology, agent) do
    case validate_current_topology(agent.state) do
      {:topology_valid, _} ->
        {:ok, agent}
        
      {:topology_invalid, problems} ->
        fix_topology_problems(problems, agent.state)
    end
  end
  
  defp discover_new_nodes(state) do
    # Execute all discovery strategies in parallel
    discovery_results = Enum.map(state.discovery_strategies, fn strategy ->
      Task.async(fn -> execute_discovery_strategy(strategy, state) end)
    end)
    |> Task.await_many(5_000)
    |> List.flatten()
    |> Enum.uniq()
    
    new_nodes = Enum.reject(discovery_results, fn node ->
      Map.has_key?(state.discovered_nodes, node.id)
    end)
    
    case new_nodes do
      [] -> {:no_new_nodes, state}
      nodes -> {:nodes_discovered, nodes}
    end
  end
  
  defp execute_discovery_strategy(:multicast, state) do
    # UDP multicast discovery
    multicast_announce()
    listen_for_multicast_responses()
  end
  
  defp execute_discovery_strategy(:dns, state) do
    # DNS-based service discovery
    resolve_cluster_dns_records()
  end
  
  defp execute_discovery_strategy(:consul, state) do
    # Consul service discovery
    query_consul_for_cluster_nodes()
  end
  
  defp announce_node_presence(state) do
    announcement = %{
      node_id: node(),
      capabilities: get_node_capabilities(),
      load: get_current_load(),
      version: get_node_version(),
      cluster_name: get_cluster_name(),
      timestamp: DateTime.utc_now()
    }
    
    # Broadcast announcement via all available channels
    broadcast_multicast_announcement(announcement)
    register_with_service_discovery(announcement)
    
    # Signal other cluster agents
    Jido.Signal.Bus.broadcast(:cluster_events, {:node_announcement, announcement})
  end
end

2. Load Balancer Agent - Intelligent Workload Distribution

defmodule DSPEx.Clustering.Agents.LoadBalancer do
  @moduledoc """
  Intelligent workload distribution across cluster nodes
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Clustering.Actions.DistributeWorkload,
    DSPEx.Clustering.Actions.MonitorNodeLoad,
    DSPEx.Clustering.Actions.RebalanceAgents,
    DSPEx.Clustering.Actions.OptimizeRouting,
    DSPEx.Clustering.Actions.HandleOverload,
    DSPEx.Clustering.Actions.PredictLoadPatterns
  ]
  
  @sensors [
    DSPEx.Clustering.Sensors.NodeLoadSensor,
    DSPEx.Clustering.Sensors.AgentDistributionSensor,
    DSPEx.Clustering.Sensors.NetworkThroughputSensor,
    DSPEx.Clustering.Sensors.RequestPatternSensor
  ]
  
  @skills [
    DSPEx.Clustering.Skills.LoadPrediction,
    DSPEx.Clustering.Skills.OptimalPlacement,
    DSPEx.Clustering.Skills.CapacityPlanning
  ]
  
  defstruct [
    :node_loads,              # Current load on each node
    :agent_distribution,      # Where agents are currently placed
    :load_history,            # Historical load patterns
    :balancing_strategies,    # Available load balancing strategies
    :routing_table,           # Optimal routing decisions
    :overload_thresholds,     # When to trigger rebalancing
    :prediction_models,       # Models for load prediction
    :rebalancing_cooldown     # Minimum time between rebalancing operations
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      node_loads: %{},
      agent_distribution: %{},
      load_history: [],
      balancing_strategies: opts[:strategies] || [:least_connections, :round_robin, :weighted],
      routing_table: %{},
      overload_thresholds: opts[:thresholds] || %{cpu: 0.8, memory: 0.85, agents: 100},
      prediction_models: initialize_prediction_models(),
      rebalancing_cooldown: opts[:rebalancing_cooldown] || 30_000
    }
    
    # Start load monitoring
    :timer.send_interval(5_000, :monitor_loads)
    :timer.send_interval(30_000, :evaluate_rebalancing)
    :timer.send_interval(60_000, :update_predictions)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_signal({:agent_placement_request, agent_spec, constraints}, agent) do
    optimal_placement = find_optimal_placement(agent_spec, constraints, agent.state)
    execute_agent_placement(agent_spec, optimal_placement, agent.state)
  end
  
  def handle_signal({:load_report, node_id, load_data}, agent) do
    updated_loads = Map.put(agent.state.node_loads, node_id, load_data)
    updated_state = %{agent.state | node_loads: updated_loads}
    
    # Check if rebalancing is needed
    case evaluate_immediate_rebalancing(updated_state) do
      {:rebalancing_needed, rebalancing_plan} ->
        execute_load_rebalancing(rebalancing_plan, updated_state)
        
      {:no_rebalancing_needed, _} ->
        {:ok, %{agent | state: updated_state}}
    end
  end
  
  def handle_signal({:overload_detected, overloaded_node, overload_type}, agent) do
    emergency_plan = create_emergency_rebalancing_plan(overloaded_node, overload_type, agent.state)
    execute_emergency_rebalancing(emergency_plan, agent.state)
  end
  
  def handle_info(:monitor_loads, agent) do
    # Collect load data from all nodes
    current_loads = collect_cluster_load_data()
    
    # Update load history
    updated_history = [current_loads | Enum.take(agent.state.load_history, 99)]
    
    # Update state
    updated_state = %{agent.state | 
      node_loads: current_loads,
      load_history: updated_history
    }
    
    {:ok, %{agent | state: updated_state}}
  end
  
  def handle_info(:evaluate_rebalancing, agent) do
    case should_rebalance_cluster?(agent.state) do
      {:yes, rebalancing_plan} ->
        execute_planned_rebalancing(rebalancing_plan, agent.state)
        
      {:no, reason} ->
        log_rebalancing_decision(reason)
        {:ok, agent}
    end
  end
  
  defp find_optimal_placement(agent_spec, constraints, state) do
    available_nodes = filter_nodes_by_constraints(constraints, state.node_loads)
    
    placement_scores = Enum.map(available_nodes, fn node ->
      {node, calculate_placement_score(agent_spec, node, state)}
    end)
    |> Enum.sort_by(fn {_node, score} -> score end, :desc)
    
    case placement_scores do
      [{optimal_node, _score} | _] -> {:ok, optimal_node}
      [] -> {:error, :no_suitable_nodes}
    end
  end
  
  defp calculate_placement_score(agent_spec, node, state) do
    node_load = Map.get(state.node_loads, node, %{})
    
    # Calculate score based on multiple factors
    cpu_score = 1.0 - (node_load[:cpu_usage] || 0.0)
    memory_score = 1.0 - (node_load[:memory_usage] || 0.0)
    agent_count_score = 1.0 - min(1.0, (node_load[:agent_count] || 0) / 100.0)
    network_score = calculate_network_score(node, agent_spec, state)
    capability_score = calculate_capability_match_score(agent_spec, node)
    
    # Weighted combination
    cpu_score * 0.3 + 
    memory_score * 0.3 + 
    agent_count_score * 0.2 + 
    network_score * 0.1 + 
    capability_score * 0.1
  end
end

3. Health Monitor Agent - Distributed Health Intelligence

defmodule DSPEx.Clustering.Agents.HealthMonitor do
  @moduledoc """
  Intelligent cluster health monitoring with predictive failure detection
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Clustering.Actions.MonitorClusterHealth,
    DSPEx.Clustering.Actions.DetectAnomalies,
    DSPEx.Clustering.Actions.PredictFailures,
    DSPEx.Clustering.Actions.TriggerRecovery,
    DSPEx.Clustering.Actions.UpdateHealthMetrics,
    DSPEx.Clustering.Actions.GenerateHealthAlerts
  ]
  
  @sensors [
    DSPEx.Clustering.Sensors.NodeVitalsSensor,
    DSPEx.Clustering.Sensors.NetworkHealthSensor,
    DSPEx.Clustering.Sensors.AgentHealthSensor,
    DSPEx.Clustering.Sensors.PerformanceDegradationSensor
  ]
  
  @skills [
    DSPEx.Clustering.Skills.AnomalyDetection,
    DSPEx.Clustering.Skills.FailurePrediction,
    DSPEx.Clustering.Skills.HealthAnalysis
  ]
  
  defstruct [
    :cluster_health_state,    # Overall cluster health status
    :node_health_history,     # Historical health data for each node
    :health_thresholds,       # Thresholds for different health metrics
    :anomaly_detection_models, # ML models for anomaly detection
    :failure_prediction_models, # Models for predicting node failures
    :recovery_strategies,     # Available recovery strategies
    :alert_policies,          # When and how to generate alerts
    :monitoring_frequency     # How often to collect health data
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      cluster_health_state: %{status: :healthy, last_updated: DateTime.utc_now()},
      node_health_history: %{},
      health_thresholds: opts[:thresholds] || default_health_thresholds(),
      anomaly_detection_models: initialize_anomaly_models(),
      failure_prediction_models: initialize_failure_prediction_models(),
      recovery_strategies: opts[:recovery_strategies] || default_recovery_strategies(),
      alert_policies: opts[:alert_policies] || default_alert_policies(),
      monitoring_frequency: opts[:monitoring_frequency] || 5_000
    }
    
    # Start health monitoring cycles
    :timer.send_interval(initial_state.monitoring_frequency, :collect_health_data)
    :timer.send_interval(30_000, :analyze_health_trends)
    :timer.send_interval(60_000, :predict_failures)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_signal({:health_data, node_id, health_metrics}, agent) do
    # Update node health history
    updated_history = update_node_health_history(node_id, health_metrics, agent.state)
    
    # Detect anomalies in health data
    anomalies = detect_health_anomalies(node_id, health_metrics, agent.state)
    
    case anomalies do
      [] ->
        {:ok, %{agent | state: %{agent.state | node_health_history: updated_history}}}
        
      detected_anomalies ->
        handle_health_anomalies(node_id, detected_anomalies, agent.state)
    end
  end
  
  def handle_signal({:node_failure_suspected, node_id, failure_indicators}, agent) do
    case validate_failure_suspicion(node_id, failure_indicators, agent.state) do
      {:confirmed_failure, failure_type} ->
        trigger_failure_recovery(node_id, failure_type, agent.state)
        
      {:false_alarm, _} ->
        log_false_failure_alarm(node_id, failure_indicators)
        {:ok, agent}
        
      {:investigation_needed, _} ->
        start_failure_investigation(node_id, failure_indicators, agent.state)
    end
  end
  
  def handle_info(:collect_health_data, agent) do
    # Collect health data from all cluster nodes
    cluster_health_data = collect_cluster_health_data()
    
    # Update overall cluster health
    updated_cluster_health = calculate_cluster_health(cluster_health_data, agent.state)
    
    # Process each node's health data
    updated_state = process_health_data_collection(cluster_health_data, agent.state)
    |> Map.put(:cluster_health_state, updated_cluster_health)
    
    {:ok, %{agent | state: updated_state}}
  end
  
  def handle_info(:analyze_health_trends, agent) do
    trend_analysis = analyze_cluster_health_trends(agent.state.node_health_history)
    
    case identify_concerning_trends(trend_analysis) do
      [] ->
        {:ok, agent}
        
      concerning_trends ->
        generate_trend_alerts(concerning_trends, agent.state)
    end
  end
  
  def handle_info(:predict_failures, agent) do
    failure_predictions = predict_node_failures(agent.state)
    
    case filter_high_risk_predictions(failure_predictions) do
      [] ->
        {:ok, agent}
        
      high_risk_predictions ->
        handle_failure_predictions(high_risk_predictions, agent.state)
    end
  end
  
  defp detect_health_anomalies(node_id, health_metrics, state) do
    historical_data = Map.get(state.node_health_history, node_id, [])
    
    # Use ML models to detect anomalies
    anomalies = Enum.flat_map(health_metrics, fn {metric_name, metric_value} ->
      case detect_metric_anomaly(metric_name, metric_value, historical_data, state) do
        {:anomaly, anomaly_data} -> [anomaly_data]
        {:normal, _} -> []
      end
    end)
    
    anomalies
  end
  
  defp predict_node_failures(state) do
    Enum.map(state.node_health_history, fn {node_id, health_history} ->
      failure_probability = calculate_failure_probability(health_history, state.failure_prediction_models)
      time_to_failure = estimate_time_to_failure(health_history, state.failure_prediction_models)
      
      %{
        node_id: node_id,
        failure_probability: failure_probability,
        estimated_time_to_failure: time_to_failure,
        confidence: calculate_prediction_confidence(health_history)
      }
    end)
  end
end

4. Consistency Manager Agent - Distributed State Coordination

defmodule DSPEx.Clustering.Agents.ConsistencyManager do
  @moduledoc """
  Intelligent distributed consistency management for cluster state
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Clustering.Actions.CoordinateConsensus,
    DSPEx.Clustering.Actions.ManageReplication,
    DSPEx.Clustering.Actions.ResolveConflicts,
    DSPEx.Clustering.Actions.HandlePartitions,
    DSPEx.Clustering.Actions.SynchronizeState,
    DSPEx.Clustering.Actions.ValidateConsistency
  ]
  
  @sensors [
    DSPEx.Clustering.Sensors.ConsistencyViolationSensor,
    DSPEx.Clustering.Sensors.ReplicationLagSensor,
    DSPEx.Clustering.Sensors.ConflictDetectionSensor,
    DSPEx.Clustering.Sensors.PartitionDetectionSensor
  ]
  
  defstruct [
    :consistency_level,       # Required consistency level (:strong, :eventual, :causal)
    :replication_factor,      # Number of replicas for each piece of data
    :consensus_protocol,      # Consensus algorithm (:raft, :pbft, :gossip)
    :conflict_resolution,     # Conflict resolution strategies
    :partition_tolerance,     # How to handle network partitions
    :state_snapshots,         # Periodic state snapshots for recovery
    :pending_operations,      # Operations waiting for consensus
    :node_roles               # Roles of different nodes (leader, follower, etc.)
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      consistency_level: opts[:consistency_level] || :eventual,
      replication_factor: opts[:replication_factor] || 3,
      consensus_protocol: opts[:consensus_protocol] || :raft,
      conflict_resolution: opts[:conflict_resolution] || %{
        strategy: :timestamp,
        custom_resolvers: %{}
      },
      partition_tolerance: opts[:partition_tolerance] || :ap, # AP vs CP in CAP theorem
      state_snapshots: %{},
      pending_operations: %{},
      node_roles: %{node() => :candidate}
    }
    
    # Start consistency management cycles
    :timer.send_interval(1_000, :process_pending_operations)
    :timer.send_interval(30_000, :validate_cluster_consistency)
    :timer.send_interval(300_000, :create_state_snapshot)
    
    # Initialize consensus protocol
    initialize_consensus_protocol(initial_state.consensus_protocol, initial_state)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_signal({:state_update_request, update_spec}, agent) do
    case agent.state.consistency_level do
      :strong ->
        coordinate_strong_consistency_update(update_spec, agent.state)
        
      :eventual ->
        apply_eventual_consistency_update(update_spec, agent.state)
        
      :causal ->
        coordinate_causal_consistency_update(update_spec, agent.state)
    end
  end
  
  def handle_signal({:consensus_vote_request, proposal_id, proposal}, agent) do
    case evaluate_consensus_proposal(proposal_id, proposal, agent.state) do
      {:vote, :accept} ->
        send_consensus_vote(proposal_id, :accept, agent.state)
        
      {:vote, :reject} ->
        send_consensus_vote(proposal_id, :reject, agent.state)
        
      {:abstain, reason} ->
        send_consensus_abstention(proposal_id, reason, agent.state)
    end
  end
  
  def handle_signal({:partition_detected, partition_info}, agent) do
    case agent.state.partition_tolerance do
      :ap -> # Availability and Partition tolerance
        handle_ap_partition(partition_info, agent.state)
        
      :cp -> # Consistency and Partition tolerance  
        handle_cp_partition(partition_info, agent.state)
        
      :custom ->
        handle_custom_partition_strategy(partition_info, agent.state)
    end
  end
  
  defp coordinate_strong_consistency_update(update_spec, state) do
    case state.consensus_protocol do
      :raft ->
        coordinate_raft_update(update_spec, state)
        
      :pbft ->
        coordinate_pbft_update(update_spec, state)
        
      :gossip ->
        coordinate_gossip_update(update_spec, state)
    end
  end
  
  defp coordinate_raft_update(update_spec, state) do
    # Raft consensus for strong consistency
    if is_leader?(state.node_roles) do
      # Leader coordinates the update
      proposal = create_raft_proposal(update_spec)
      broadcast_raft_proposal(proposal, state)
      wait_for_raft_consensus(proposal.id, state)
    else
      # Forward to leader
      leader_node = find_leader_node(state.node_roles)
      forward_update_to_leader(update_spec, leader_node, state)
    end
  end
end

Advanced Clustering Patterns

5. Network Partition Handler Agent - Intelligent Split-Brain Resolution

defmodule DSPEx.Clustering.Agents.NetworkPartitionHandler do
  @moduledoc """
  Intelligent network partition detection and resolution
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Clustering.Actions.DetectPartitions,
    DSPEx.Clustering.Actions.AnalyzePartitionType,
    DSPEx.Clustering.Actions.ChoosePartitionStrategy,
    DSPEx.Clustering.Actions.CoordinateRecovery,
    DSPEx.Clustering.Actions.PreventSplitBrain,
    DSPEx.Clustering.Actions.MergePartitions
  ]
  
  @sensors [
    DSPEx.Clustering.Sensors.NetworkConnectivitySensor,
    DSPEx.Clustering.Sensors.PartitionTopologySensor,
    DSPEx.Clustering.Sensors.NodeIsolationSensor
  ]
  
  defstruct [
    :partition_detection_config, # Configuration for partition detection
    :recovery_strategies,        # Available partition recovery strategies
    :split_brain_prevention,     # Split-brain prevention mechanisms
    :partition_history,          # Historical partition events
    :current_partitions,         # Currently detected partitions
    :recovery_timeout,           # Maximum time to wait for recovery
    :quorum_size                # Minimum nodes needed for quorum
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      partition_detection_config: opts[:detection_config] || default_detection_config(),
      recovery_strategies: opts[:recovery_strategies] || [:quorum_based, :manual, :automatic],
      split_brain_prevention: opts[:split_brain_prevention] || %{enabled: true, quorum_required: true},
      partition_history: [],
      current_partitions: %{},
      recovery_timeout: opts[:recovery_timeout] || 300_000, # 5 minutes
      quorum_size: opts[:quorum_size] || 3
    }
    
    # Start partition detection
    :timer.send_interval(10_000, :detect_partitions)
    :timer.send_interval(60_000, :validate_partition_status)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_signal({:partition_suspected, partition_evidence}, agent) do
    case analyze_partition_evidence(partition_evidence, agent.state) do
      {:confirmed_partition, partition_details} ->
        handle_confirmed_partition(partition_details, agent.state)
        
      {:false_alarm, _} ->
        log_partition_false_alarm(partition_evidence)
        {:ok, agent}
        
      {:needs_investigation, _} ->
        start_partition_investigation(partition_evidence, agent.state)
    end
  end
  
  def handle_signal({:partition_recovery_complete, partition_id}, agent) do
    complete_partition_recovery(partition_id, agent.state)
  end
  
  defp handle_confirmed_partition(partition_details, state) do
    # Determine partition strategy based on partition type and cluster state
    strategy = choose_partition_strategy(partition_details, state)
    
    case strategy do
      :quorum_based ->
        handle_quorum_based_partition(partition_details, state)
        
      :manual_intervention ->
        request_manual_partition_resolution(partition_details, state)
        
      :automatic_recovery ->
        start_automatic_partition_recovery(partition_details, state)
        
      :graceful_degradation ->
        enable_graceful_degradation_mode(partition_details, state)
    end
  end
  
  defp choose_partition_strategy(partition_details, state) do
    partition_size = length(partition_details.isolated_nodes)
    remaining_cluster_size = length(partition_details.remaining_nodes)
    
    cond do
      remaining_cluster_size >= state.quorum_size ->
        :quorum_based
        
      partition_size == 1 and remaining_cluster_size > 1 ->
        :automatic_recovery
        
      partition_details.partition_type == :symmetric ->
        :manual_intervention
        
      true ->
        :graceful_degradation
    end
  end
end

6. Agent Migration Manager - Intelligent Workload Mobility

defmodule DSPEx.Clustering.Agents.AgentMigrationManager do
  @moduledoc """
  Intelligent agent migration across cluster nodes with state preservation
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Clustering.Actions.PlanAgentMigration,
    DSPEx.Clustering.Actions.ExecuteStatefulMigration,
    DSPEx.Clustering.Actions.ValidateMigrationSuccess,
    DSPEx.Clustering.Actions.RollbackFailedMigration,
    DSPEx.Clustering.Actions.OptimizeMigrationRoutes,
    DSPEx.Clustering.Actions.PreserveCognitiveVariableState
  ]
  
  @sensors [
    DSPEx.Clustering.Sensors.MigrationOpportunitySensor,
    DSPEx.Clustering.Sensors.StateTransferProgressSensor,
    DSPEx.Clustering.Sensors.MigrationPerformanceSensor
  ]
  
  defstruct [
    :migration_policies,      # Policies governing when/how to migrate agents
    :active_migrations,       # Currently in-progress migrations
    :migration_history,       # Historical migration data for optimization
    :state_preservation_config, # How to preserve agent state during migration
    :rollback_strategies,     # Strategies for handling failed migrations
    :performance_thresholds,  # When migrations are beneficial
    :cognitive_variable_handling # Special handling for Cognitive Variables
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      migration_policies: opts[:policies] || default_migration_policies(),
      active_migrations: %{},
      migration_history: [],
      state_preservation_config: opts[:state_preservation] || %{
        method: :checkpoint_restore,
        compression: true,
        encryption: true
      },
      rollback_strategies: opts[:rollback] || [:state_rollback, :agent_restart],
      performance_thresholds: opts[:thresholds] || %{
        cpu_improvement: 0.2,
        latency_improvement: 0.15,
        cost_reduction: 0.1
      },
      cognitive_variable_handling: %{
        pause_adaptation: true,
        sync_before_migration: true,
        preserve_coordination_state: true
      }
    }
    
    # Start migration monitoring
    :timer.send_interval(30_000, :evaluate_migration_opportunities)
    :timer.send_interval(5_000, :monitor_active_migrations)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_signal({:migration_request, agent_id, target_node, reason}, agent) do
    case plan_agent_migration(agent_id, target_node, reason, agent.state) do
      {:ok, migration_plan} ->
        execute_agent_migration(migration_plan, agent.state)
        
      {:error, reason} ->
        reject_migration_request(agent_id, target_node, reason, agent.state)
    end
  end
  
  def handle_signal({:cognitive_variable_migration_request, variable_id, migration_spec}, agent) do
    # Special handling for Cognitive Variables that coordinate other agents
    plan_cognitive_variable_migration(variable_id, migration_spec, agent.state)
  end
  
  defp plan_cognitive_variable_migration(variable_id, migration_spec, state) do
    # Cognitive Variables require special migration handling because they coordinate other agents
    cognitive_variable_info = get_cognitive_variable_info(variable_id)
    
    migration_plan = %{
      type: :cognitive_variable_migration,
      variable_id: variable_id,
      source_node: cognitive_variable_info.current_node,
      target_node: migration_spec.target_node,
      affected_agents: cognitive_variable_info.affected_agents,
      coordination_state: cognitive_variable_info.coordination_state,
      
      # Special steps for Cognitive Variable migration
      steps: [
        :pause_variable_adaptation,
        :notify_affected_agents,
        :sync_coordination_state,
        :create_state_checkpoint,
        :transfer_variable_agent,
        :restore_coordination_state,
        :resume_adaptation,
        :validate_agent_coordination
      ]
    }
    
    execute_cognitive_variable_migration(migration_plan, state)
  end
  
  defp execute_cognitive_variable_migration(migration_plan, state) do
    migration_id = generate_migration_id()
    
    # Step 1: Pause variable adaptation
    Jido.Signal.Bus.broadcast(migration_plan.variable_id, {:pause_adaptation, migration_id})
    
    # Step 2: Notify affected agents about impending migration
    Enum.each(migration_plan.affected_agents, fn agent_id ->
      Jido.Signal.Bus.broadcast(agent_id, {
        :coordination_variable_migrating, 
        migration_plan.variable_id, 
        migration_id
      })
    end)
    
    # Step 3: Synchronize coordination state across cluster
    sync_coordination_state(migration_plan.variable_id, migration_plan.coordination_state)
    
    # Step 4: Create comprehensive state checkpoint
    checkpoint = create_cognitive_variable_checkpoint(migration_plan.variable_id)
    
    # Step 5: Transfer the Variable agent to target node
    transfer_result = transfer_variable_agent(
      migration_plan.variable_id,
      migration_plan.target_node,
      checkpoint
    )
    
    case transfer_result do
      {:ok, new_variable_pid} ->
        # Step 6: Restore coordination state
        restore_coordination_state(new_variable_pid, checkpoint.coordination_state)
        
        # Step 7: Resume adaptation
        Jido.Signal.Bus.broadcast(new_variable_pid, {:resume_adaptation, migration_id})
        
        # Step 8: Validate agent coordination is working
        validate_variable_coordination(new_variable_pid, migration_plan.affected_agents)
        
      {:error, reason} ->
        # Rollback migration
        rollback_cognitive_variable_migration(migration_plan, checkpoint, reason)
    end
  end
end

Signal-Based Cluster Communication

Cluster Signal Patterns

defmodule DSPEx.Clustering.SignalPatterns do
  @moduledoc """
  Standard signal patterns for cluster communication
  """
  
  # Node lifecycle signals
  def node_announcement_signal(node_info) do
    %Jido.Signal{
      type: :node_announcement,
      payload: node_info,
      metadata: %{
        cluster_event: true,
        priority: :normal,
        broadcast_scope: :cluster
      }
    }
  end
  
  def node_failure_signal(failed_node, failure_type) do
    %Jido.Signal{
      type: :node_failure,
      payload: %{node: failed_node, failure_type: failure_type},
      metadata: %{
        cluster_event: true,
        priority: :high,
        broadcast_scope: :cluster
      }
    }
  end
  
  # Load balancing signals
  def load_report_signal(node_id, load_data) do
    %Jido.Signal{
      type: :load_report,
      payload: %{node: node_id, load: load_data},
      metadata: %{
        cluster_event: true,
        priority: :low,
        frequency: :regular
      }
    }
  end
  
  def rebalancing_request_signal(rebalancing_plan) do
    %Jido.Signal{
      type: :rebalancing_request,
      payload: rebalancing_plan,
      metadata: %{
        cluster_event: true,
        priority: :high,
        coordination_required: true
      }
    }
  end
  
  # Health monitoring signals
  def health_alert_signal(alert_type, alert_data) do
    %Jido.Signal{
      type: :health_alert,
      payload: %{alert_type: alert_type, data: alert_data},
      metadata: %{
        cluster_event: true,
        priority: :high,
        alert: true
      }
    }
  end
  
  # Consensus and coordination signals
  def consensus_proposal_signal(proposal_id, proposal) do
    %Jido.Signal{
      type: :consensus_proposal,
      payload: %{id: proposal_id, proposal: proposal},
      metadata: %{
        cluster_event: true,
        priority: :high,
        consensus_required: true
      }
    }
  end
  
  def consensus_vote_signal(proposal_id, vote, voter_node) do
    %Jido.Signal{
      type: :consensus_vote,
      payload: %{proposal_id: proposal_id, vote: vote, voter: voter_node},
      metadata: %{
        cluster_event: true,
        priority: :high,
        consensus_response: true
      }
    }
  end
  
  # Variable coordination signals (for Cognitive Variables)
  def variable_sync_signal(variable_id, sync_data) do
    %Jido.Signal{
      type: :variable_sync,
      payload: %{variable_id: variable_id, sync_data: sync_data},
      metadata: %{
        cluster_event: true,
        priority: :medium,
        variable_coordination: true
      }
    }
  end
  
  def variable_conflict_signal(variable_id, conflict_data) do
    %Jido.Signal{
      type: :variable_conflict,
      payload: %{variable_id: variable_id, conflict: conflict_data},
      metadata: %{
        cluster_event: true,
        priority: :high,
        conflict_resolution_required: true
      }
    }
  end
end

Performance Optimization and Monitoring

Cluster Performance Intelligence

defmodule DSPEx.Clustering.PerformanceIntelligence do
  @moduledoc """
  Advanced performance monitoring and optimization for Jido-native clustering
  """
  
  use Jido.Agent
  
  @actions [
    DSPEx.Clustering.Actions.CollectPerformanceMetrics,
    DSPEx.Clustering.Actions.AnalyzeClusterPerformance,
    DSPEx.Clustering.Actions.OptimizeClusterConfiguration,
    DSPEx.Clustering.Actions.PredictPerformanceBottlenecks,
    DSPEx.Clustering.Actions.GenerateOptimizationRecommendations
  ]
  
  @sensors [
    DSPEx.Clustering.Sensors.ClusterThroughputSensor,
    DSPEx.Clustering.Sensors.AgentCoordinationLatencySensor,
    DSPEx.Clustering.Sensors.SignalProcessingEfficiencySensor,
    DSPEx.Clustering.Sensors.ResourceUtilizationSensor
  ]
  
  defstruct [
    :performance_metrics,     # Real-time cluster performance metrics
    :optimization_models,     # ML models for performance optimization
    :benchmark_targets,       # Performance targets for the cluster
    :bottleneck_detection,    # Bottleneck detection algorithms
    :optimization_history,    # Historical optimization results
    :performance_predictions, # Predicted performance trends
    :alert_thresholds        # Performance alert thresholds
  ]
  
  def mount(agent, opts) do
    initial_state = %__MODULE__{
      performance_metrics: %{},
      optimization_models: initialize_optimization_models(),
      benchmark_targets: opts[:targets] || default_performance_targets(),
      bottleneck_detection: initialize_bottleneck_detection(),
      optimization_history: [],
      performance_predictions: %{},
      alert_thresholds: opts[:alert_thresholds] || default_alert_thresholds()
    }
    
    # Start performance monitoring
    :timer.send_interval(5_000, :collect_performance_data)
    :timer.send_interval(60_000, :analyze_performance_trends)
    :timer.send_interval(300_000, :generate_optimization_recommendations)
    
    {:ok, %{agent | state: initial_state}}
  end
  
  def handle_info(:collect_performance_data, agent) do
    # Collect comprehensive cluster performance data
    performance_data = collect_cluster_performance_data()
    
    # Update performance metrics
    updated_metrics = update_performance_metrics(performance_data, agent.state)
    
    # Detect performance issues
    case detect_performance_issues(updated_metrics, agent.state) do
      [] ->
        {:ok, %{agent | state: %{agent.state | performance_metrics: updated_metrics}}}
        
      issues ->
        handle_performance_issues(issues, agent.state)
    end
  end
  
  defp collect_cluster_performance_data do
    %{
      # Cluster-level metrics
      total_throughput: measure_cluster_throughput(),
      average_latency: measure_cluster_latency(),
      agent_coordination_efficiency: measure_coordination_efficiency(),
      signal_processing_speed: measure_signal_processing_speed(),
      
      # Node-level metrics
      node_performance: collect_node_performance_data(),
      
      # Agent-level metrics
      agent_performance: collect_agent_performance_data(),
      
      # Cognitive Variable metrics
      variable_coordination_performance: measure_variable_coordination_performance(),
      
      # Network metrics
      network_performance: measure_network_performance(),
      
      timestamp: DateTime.utc_now()
    }
  end
  
  defp measure_cluster_throughput do
    # Measure requests/operations per second across the cluster
    cluster_nodes = DSPEx.Clustering.get_cluster_nodes()
    
    node_throughputs = Enum.map(cluster_nodes, fn node ->
      measure_node_throughput(node)
    end)
    
    Enum.sum(node_throughputs)
  end
  
  defp measure_coordination_efficiency do
    # Measure how efficiently agents coordinate across the cluster
    coordination_metrics = %{
      average_coordination_time: measure_average_coordination_time(),
      coordination_success_rate: measure_coordination_success_rate(),
      signal_routing_efficiency: measure_signal_routing_efficiency(),
      consensus_convergence_time: measure_consensus_convergence_time()
    }
    
    # Calculate overall efficiency score
    calculate_coordination_efficiency_score(coordination_metrics)
  end
  
  defp measure_variable_coordination_performance do
    # Measure performance of Cognitive Variable coordination
    %{
      variable_update_latency: measure_variable_update_latency(),
      cross_cluster_sync_time: measure_cross_cluster_sync_time(),
      variable_conflict_resolution_time: measure_conflict_resolution_time(),
      adaptation_response_time: measure_adaptation_response_time()
    }
  end
end

Production Deployment and Operations

Cluster Deployment Configuration

defmodule DSPEx.Clustering.ProductionConfig do
  @moduledoc """
  Production deployment configuration for Jido-native clustering
  """
  
  def production_cluster_config do
    %{
      # Node discovery configuration
      node_discovery: %{
        strategies: [:consul, :dns, :kubernetes],
        announcement_frequency: 30_000,
        health_check_interval: 10_000
      },
      
      # Load balancing configuration
      load_balancing: %{
        strategies: [:least_connections, :weighted_round_robin],
        rebalancing_frequency: 60_000,
        overload_thresholds: %{
          cpu: 0.8,
          memory: 0.85,
          agents: 1000
        }
      },
      
      # Health monitoring configuration
      health_monitoring: %{
        monitoring_frequency: 5_000,
        failure_detection_timeout: 30_000,
        recovery_strategies: [:automatic, :manual_fallback]
      },
      
      # Consistency management configuration
      consistency: %{
        level: :eventual,
        replication_factor: 3,
        consensus_protocol: :raft,
        partition_tolerance: :ap
      },
      
      # Performance optimization
      performance: %{
        monitoring_frequency: 5_000,
        optimization_frequency: 300_000,
        alert_thresholds: %{
          latency_p95: 100,
          throughput_min: 1000,
          error_rate_max: 0.01
        }
      },
      
      # Cognitive Variable coordination
      cognitive_variables: %{
        cluster_sync_frequency: 10_000,
        conflict_resolution_timeout: 30_000,
        adaptation_coordination: true
      }
    }
  end
  
  def development_cluster_config do
    %{
      # Simplified configuration for development
      node_discovery: %{
        strategies: [:multicast],
        announcement_frequency: 10_000
      },
      
      load_balancing: %{
        strategies: [:round_robin],
        rebalancing_frequency: 120_000
      },
      
      health_monitoring: %{
        monitoring_frequency: 10_000,
        failure_detection_timeout: 60_000
      },
      
      consistency: %{
        level: :eventual,
        replication_factor: 1,
        consensus_protocol: :gossip
      }
    }
  end
end

Conclusion

This Jido-native clustering design represents a revolutionary approach to distributed systems that leverages agent-first principles to create a more resilient, scalable, and maintainable clustering platform. Key innovations include:

Revolutionary Advantages

  1. Agent-First Architecture: Every clustering function implemented as intelligent agents
  2. Signal-Based Communication: All coordination via Jido’s efficient signal bus
  3. Natural Fault Isolation: Agent failures don’t cascade due to OTP supervision
  4. Unified Mental Model: Everything is an agent - from load balancing to health monitoring
  5. Cognitive Variable Support: Native support for intelligent Variable coordination
  6. Self-Healing Capabilities: Agents can detect and recover from failures autonomously

Technical Benefits

  1. Simplified Architecture: Fewer abstractions than traditional clustering solutions
  2. Better Testability: Mock agents for testing complex distributed scenarios
  3. Incremental Deployment: Add clustering capabilities gradually
  4. Performance Optimization: Direct signal communication vs service layer overhead
  5. Easier Reasoning: Agent patterns are easier to understand than service dependencies

Production Readiness

  1. Comprehensive Monitoring: Built-in telemetry and performance monitoring
  2. Fault Tolerance: OTP supervision ensures reliable clustering operations
  3. Scalability: Agent-based architecture scales naturally with cluster size
  4. Observability: Rich signal patterns enable comprehensive system monitoring
  5. Operations: Simple agent management vs complex service coordination

The Jido-native clustering design provides the perfect foundation for the DSPEx platform’s distributed ML capabilities, enabling intelligent agent coordination across clusters while maintaining the simplicity and reliability that makes Jido an excellent choice for agent-based systems.

Design Specification Completed: July 12, 2025
Foundation: Jido-Native Agent Architecture
Innovation: Agent-First Distributed Systems
Scope: Complete clustering platform for revolutionary DSPEx ML workflows