← Back to Docs20250627

060 COORDINATION PATTERNS

Documentation for 060_COORDINATION_PATTERNS from the Foundation repository.

Multi-Agent Coordination Patterns

Overview

This document defines the coordination patterns used in MABEAM for multi-agent ML workflows, including consensus protocols, leader election, distributed decision making, and variable synchronization across agent networks.

Coordination Architecture

Coordination System Hierarchy

MABEAM.CoordinationSupervisor
├── MABEAM.Coordination (Consensus & Leader Election)
├── MABEAM.Economics (Resource Allocation & Cost Optimization)
└── MABEAM.LoadBalancer (Workload Distribution)

Coordination Primitives

1. Consensus Protocol (Modified Raft)

  • Purpose: Distributed agreement on parameter changes
  • Participants: All agents marked as participates_in_consensus: true
  • Guarantee: At least majority agreement before parameter changes
  • Fault Tolerance: Handles network partitions and agent failures

2. Leader Election

  • Purpose: Designate coordination leader for conflict resolution
  • Algorithm: Bully algorithm with stability preference
  • Failover: Automatic re-election on leader failure
  • Scope: Can be global or per-agent-group

3. Variable Synchronization

  • Purpose: Coordinate ML parameter updates across agents
  • Pattern: Broadcast with acknowledgment
  • Consistency: Eventual consistency with conflict resolution
  • Ordering: Vector clocks for causal ordering

4. Distributed Barriers

  • Purpose: Synchronize multi-agent operations
  • Implementation: Count-based barriers with timeout
  • Use Cases: Coordinated training epochs, checkpoint synchronization

Core Coordination Patterns

Pattern 1: Variable Consensus Protocol

Consensus Initiation

defmodule MABEAM.Coordination do
  def propose_variable_change(variable_name, new_value, metadata \\ %{}) do
    proposal = %{
      id: generate_proposal_id(),
      variable: variable_name,
      value: new_value,
      proposer: self(),
      timestamp: DateTime.utc_now(),
      metadata: metadata
    }
    
    # Start consensus round
    start_consensus_round(proposal)
  end

  defp start_consensus_round(proposal) do
    # Get all agents eligible for consensus
    eligible_agents = get_consensus_eligible_agents()
    
    # Broadcast proposal to all agents
    for agent_pid <- eligible_agents do
      GenServer.cast(agent_pid, {:consensus_proposal, proposal})
    end
    
    # Set timeout for consensus
    ref = make_ref()
    Process.send_after(self(), {:consensus_timeout, proposal.id, ref}, 30_000)
    
    # Track consensus state
    consensus_state = %{
      proposal: proposal,
      votes: %{},
      participants: eligible_agents,
      timeout_ref: ref,
      status: :voting
    }
    
    put_consensus_state(proposal.id, consensus_state)
  end
end

Agent Consensus Participation

defmodule MABEAM.Agents.BaseAgent do
  def handle_cast({:consensus_proposal, proposal}, state) do
    # Evaluate proposal based on agent's current context
    vote = evaluate_consensus_proposal(proposal, state)
    
    # Cast vote back to coordination system
    MABEAM.Coordination.cast_vote(proposal.id, vote, %{
      agent_id: state.agent_id,
      rationale: vote.rationale
    })
    
    {:noreply, state}
  end

  defp evaluate_consensus_proposal(proposal, state) do
    # Agent-specific evaluation logic
    case proposal.variable do
      :learning_rate ->
        evaluate_learning_rate_change(proposal.value, state)
      :batch_size ->
        evaluate_batch_size_change(proposal.value, state)
      :model_architecture ->
        evaluate_architecture_change(proposal.value, state)
      _ ->
        # Default: accept if no specific concerns
        %{vote: :accept, confidence: 0.5, rationale: "no specific concerns"}
    end
  end
end

Consensus Resolution

def handle_call({:cast_vote, proposal_id, vote, metadata}, {agent_pid, _}, state) do
  case get_consensus_state(proposal_id) do
    {:ok, consensus_state} when consensus_state.status == :voting ->
      # Record vote
      new_votes = Map.put(consensus_state.votes, agent_pid, {vote, metadata})
      updated_state = %{consensus_state | votes: new_votes}
      
      # Check if we have enough votes
      case check_consensus_threshold(updated_state) do
        {:consensus_reached, decision} ->
          finalize_consensus(proposal_id, decision, updated_state)
          {:reply, :ok, state}
          
        {:consensus_failed, reason} ->
          fail_consensus(proposal_id, reason, updated_state)
          {:reply, :ok, state}
          
        :insufficient_votes ->
          put_consensus_state(proposal_id, updated_state)
          {:reply, :ok, state}
      end
      
    _ ->
      {:reply, {:error, :invalid_consensus_state}, state}
  end
end

defp check_consensus_threshold(consensus_state) do
  total_agents = length(consensus_state.participants)
  votes_cast = map_size(consensus_state.votes)
  
  # Require majority participation
  if votes_cast >= div(total_agents, 2) + 1 do
    # Analyze vote distribution
    vote_summary = analyze_votes(consensus_state.votes)
    
    cond do
      vote_summary.accept_ratio >= 0.6 ->
        {:consensus_reached, :accept}
      vote_summary.reject_ratio >= 0.4 ->
        {:consensus_reached, :reject}
      true ->
        {:consensus_failed, :no_clear_majority}
    end
  else
    :insufficient_votes
  end
end

Pattern 2: Leader Election Protocol

Election Initiation

defmodule MABEAM.Coordination.LeaderElection do
  def start_election(reason \\ :leadership_vacant) do
    election_id = generate_election_id()
    
    # Get all leadership-eligible agents
    candidates = get_leadership_eligible_agents()
    
    election_context = %{
      id: election_id,
      reason: reason,
      candidates: candidates,
      votes: %{},
      started_at: DateTime.utc_now(),
      status: :voting
    }
    
    # Broadcast election call to all agents
    for agent_pid <- get_all_agents() do
      GenServer.cast(agent_pid, {:leader_election, election_context})
    end
    
    # Set election timeout
    Process.send_after(self(), {:election_timeout, election_id}, 15_000)
    
    put_election_state(election_id, election_context)
  end

  def handle_cast({:election_vote, election_id, voted_for, voter_metadata}, state) do
    case get_election_state(election_id) do
      {:ok, election} when election.status == :voting ->
        # Record vote
        new_votes = Map.put(election.votes, voter_metadata.agent_id, voted_for)
        updated_election = %{election | votes: new_votes}
        
        # Check if election can be decided
        case tally_election_votes(updated_election) do
          {:leader_elected, leader_agent_id} ->
            finalize_leader_election(election_id, leader_agent_id)
            {:noreply, state}
            
          :insufficient_votes ->
            put_election_state(election_id, updated_election)
            {:noreply, state}
        end
        
      _ ->
        {:noreply, state}
    end
  end
end

Agent Election Participation

def handle_cast({:leader_election, election_context}, state) do
  # Evaluate candidates and cast vote
  preferred_leader = evaluate_leadership_candidates(
    election_context.candidates, 
    state
  )
  
  # Cast vote for preferred leader
  MABEAM.Coordination.LeaderElection.cast_vote(
    election_context.id,
    preferred_leader,
    %{agent_id: state.agent_id, timestamp: DateTime.utc_now()}
  )
  
  {:noreply, state}
end

defp evaluate_leadership_candidates(candidates, state) do
  # Score candidates based on:
  # - Performance metrics
  # - Resource availability  
  # - Specialization match
  # - Historical reliability
  
  candidate_scores = Enum.map(candidates, fn candidate ->
    score = calculate_leadership_score(candidate, state)
    {candidate, score}
  end)
  
  # Return candidate with highest score
  {best_candidate, _score} = Enum.max_by(candidate_scores, fn {_, score} -> score end)
  best_candidate
end

Pattern 3: Variable Synchronization

Broadcast Synchronization

defmodule MABEAM.Coordination.VariableSync do
  def broadcast_variable_update(variable_name, new_value, metadata \\ %{}) do
    update_context = %{
      variable: variable_name,
      value: new_value,
      version: generate_version(),
      timestamp: DateTime.utc_now(),
      metadata: metadata,
      coordinator: self()
    }
    
    # Vector clock for causal ordering
    vector_clock = get_current_vector_clock()
    update_with_clock = Map.put(update_context, :vector_clock, vector_clock)
    
    # Broadcast to all agents
    agents = get_all_active_agents()
    for agent_pid <- agents do
      GenServer.cast(agent_pid, {:variable_update, update_with_clock})
    end
    
    # Track acknowledgments
    track_update_acknowledgments(update_context.version, agents)
  end

  def handle_cast({:variable_update_ack, version, agent_id, local_clock}, state) do
    # Update vector clock with agent's local clock
    merged_clock = merge_vector_clocks(state.vector_clock, local_clock)
    
    # Mark acknowledgment received
    mark_acknowledgment_received(version, agent_id)
    
    # Check if all agents have acknowledged
    case check_all_acknowledged(version) do
      true ->
        finalize_variable_update(version)
        {:noreply, %{state | vector_clock: merged_clock}}
        
      false ->
        {:noreply, %{state | vector_clock: merged_clock}}
    end
  end
end

Agent Variable Synchronization

def handle_cast({:variable_update, update_context}, state) do
  # Check if this update is newer than current state
  case compare_vector_clocks(update_context.vector_clock, state.vector_clock) do
    :newer ->
      # Apply update
      new_variables = Map.put(state.variables, update_context.variable, update_context.value)
      new_state = %{state | 
        variables: new_variables,
        vector_clock: update_context.vector_clock
      }
      
      # Acknowledge update
      MABEAM.Coordination.VariableSync.acknowledge_update(
        update_context.version,
        state.agent_id,
        new_state.vector_clock
      )
      
      # Trigger any necessary behavior changes
      adapted_state = adapt_to_variable_change(
        update_context.variable,
        update_context.value,
        new_state
      )
      
      {:noreply, adapted_state}
      
    :older ->
      # Ignore stale update
      {:noreply, state}
      
    :concurrent ->
      # Conflict resolution needed
      resolved_state = resolve_variable_conflict(update_context, state)
      {:noreply, resolved_state}
  end
end

Pattern 4: Distributed Barriers

Barrier Coordination

defmodule MABEAM.Coordination.Barriers do
  def create_barrier(barrier_id, participant_count, timeout \\ 30_000) do
    barrier_context = %{
      id: barrier_id,
      required_participants: participant_count,
      arrived_participants: [],
      timeout: timeout,
      status: :waiting,
      created_at: DateTime.utc_now()
    }
    
    # Set timeout
    Process.send_after(self(), {:barrier_timeout, barrier_id}, timeout)
    
    put_barrier_state(barrier_id, barrier_context)
  end

  def arrive_at_barrier(barrier_id, agent_id) do
    case get_barrier_state(barrier_id) do
      {:ok, barrier} when barrier.status == :waiting ->
        # Add agent to arrived list
        new_arrived = [agent_id | barrier.arrived_participants]
        updated_barrier = %{barrier | arrived_participants: new_arrived}
        
        # Check if barrier condition met
        if length(new_arrived) >= barrier.required_participants do
          # Release all waiting agents
          release_barrier(barrier_id, updated_barrier)
        else
          put_barrier_state(barrier_id, updated_barrier)
        end
        
      _ ->
        {:error, :barrier_not_available}
    end
  end

  defp release_barrier(barrier_id, barrier_context) do
    # Notify all participants that barrier is released
    for agent_id <- barrier_context.arrived_participants do
      case get_agent_pid(agent_id) do
        {:ok, pid} ->
          GenServer.cast(pid, {:barrier_released, barrier_id})
        _ ->
          :ok
      end
    end
    
    # Update barrier status
    completed_barrier = %{barrier_context | 
      status: :completed,
      completed_at: DateTime.utc_now()
    }
    
    put_barrier_state(barrier_id, completed_barrier)
  end
end

Agent Barrier Participation

def handle_call({:wait_for_barrier, barrier_id}, from, state) do
  # Arrive at barrier
  case MABEAM.Coordination.Barriers.arrive_at_barrier(barrier_id, state.agent_id) do
    :ok ->
      # Wait for barrier release
      waiting_state = %{state | 
        waiting_for_barrier: barrier_id,
        barrier_caller: from
      }
      {:noreply, waiting_state}
      
    {:error, reason} ->
      {:reply, {:error, reason}, state}
  end
end

def handle_cast({:barrier_released, barrier_id}, state) do
  if state.waiting_for_barrier == barrier_id do
    # Reply to waiting caller
    GenServer.reply(state.barrier_caller, :ok)
    
    # Clear waiting state
    new_state = %{state | 
      waiting_for_barrier: nil,
      barrier_caller: nil
    }
    
    {:noreply, new_state}
  else
    {:noreply, state}
  end
end

Coordination Use Cases

Use Case 1: Coordinated Hyperparameter Optimization

Scenario: Multiple agents optimize different aspects of ML pipeline

def coordinate_hyperparameter_optimization(optimization_context) do
  # 1. Elect optimization coordinator
  {:ok, coordinator} = MABEAM.Coordination.elect_coordinator(:hyperparameter_optimization)
  
  # 2. Distribute optimization tasks
  tasks = partition_hyperparameter_space(optimization_context.parameter_space)
  
  for {agent_id, task} <- Enum.zip(get_optimizer_agents(), tasks) do
    assign_optimization_task(agent_id, task)
  end
  
  # 3. Coordinate optimization rounds
  for round <- 1..optimization_context.max_rounds do
    # Barrier: Wait for all agents to complete round
    barrier_id = "optimization_round_#{round}"
    MABEAM.Coordination.Barriers.create_barrier(barrier_id, length(tasks))
    
    # Agents perform optimization and arrive at barrier
    # ...
    
    # Consensus: Decide on best parameters from round
    round_results = collect_round_results(round)
    {:ok, best_params} = MABEAM.Coordination.reach_consensus_on_best_params(round_results)
    
    # Broadcast: Update all agents with best parameters
    MABEAM.Coordination.broadcast_parameter_update(best_params)
  end
end

Use Case 2: Distributed Model Training

Scenario: Multiple agents train different parts of a model collaboratively

def coordinate_distributed_training(training_context) do
  # 1. Partition training data across agents
  data_partitions = partition_training_data(training_context.dataset)
  
  # 2. Synchronize model initialization
  initial_model = initialize_model(training_context.model_spec)
  MABEAM.Coordination.broadcast_model_state(initial_model)
  
  # 3. Coordinate training epochs
  for epoch <- 1..training_context.epochs do
    epoch_barrier = "training_epoch_#{epoch}"
    
    # Start epoch
    MABEAM.Coordination.broadcast_epoch_start(epoch)
    
    # Agents train on local data
    # ...
    
    # Barrier: Wait for all agents to complete local training
    MABEAM.Coordination.Barriers.create_barrier(epoch_barrier, length(data_partitions))
    
    # Collect gradients from all agents
    gradients = collect_agent_gradients(epoch)
    
    # Consensus: Aggregate gradients (e.g., FedAvg)
    {:ok, aggregated_gradients} = aggregate_gradients_with_consensus(gradients)
    
    # Broadcast: Update model with aggregated gradients
    MABEAM.Coordination.broadcast_model_update(aggregated_gradients)
  end
end

Use Case 3: Multi-Agent Reinforcement Learning

Scenario: Agents coordinate exploration and exploitation strategies

def coordinate_multi_agent_rl(rl_context) do
  # 1. Elect exploration coordinator
  {:ok, coordinator} = MABEAM.Coordination.elect_coordinator(:exploration)
  
  # 2. Coordinate exploration-exploitation balance
  exploration_schedule = create_exploration_schedule(rl_context)
  
  for episode <- 1..rl_context.episodes do
    # Consensus: Decide exploration strategy for episode
    exploration_strategy = determine_exploration_strategy(episode, exploration_schedule)
    {:ok, agreed_strategy} = MABEAM.Coordination.reach_consensus_on_strategy(exploration_strategy)
    
    # Broadcast: Update all agents with exploration strategy
    MABEAM.Coordination.broadcast_exploration_strategy(agreed_strategy)
    
    # Agents execute episode with coordinated strategy
    # ...
    
    # Collect experiences from all agents
    experiences = collect_agent_experiences(episode)
    
    # Consensus: Decide on experience sharing strategy
    {:ok, shared_experiences} = MABEAM.Coordination.reach_consensus_on_experience_sharing(experiences)
    
    # Broadcast: Share experiences across agents
    MABEAM.Coordination.broadcast_shared_experiences(shared_experiences)
  end
end

Performance Considerations

Latency Optimization

  • Async Coordination: Use GenServer.cast for non-blocking coordination
  • Batch Updates: Group multiple variable updates into single broadcasts
  • Local Caching: Cache frequently accessed coordination state locally

Scalability Patterns

  • Hierarchical Coordination: Organize agents into groups with group coordinators
  • Gossip Protocols: Use epidemic protocols for large-scale information dissemination
  • Partition Tolerance: Design coordination to handle network partitions gracefully

Fault Tolerance

  • Coordinator Failover: Automatic re-election of coordination leaders
  • Partial Consensus: Continue operation with reduced participant set
  • State Recovery: Recover coordination state from persistent storage

Testing Coordination Patterns

Consensus Testing

test "consensus protocol handles majority agreement" do
  # Setup agents with different preferences
  agents = setup_test_agents(5)
  
  # Propose parameter change
  proposal = %{variable: :learning_rate, value: 0.01}
  {:ok, proposal_id} = MABEAM.Coordination.propose_variable_change(proposal)
  
  # Simulate votes (3 accept, 2 reject)
  vote_pattern = [:accept, :accept, :accept, :reject, :reject]
  simulate_consensus_votes(agents, proposal_id, vote_pattern)
  
  # Verify consensus reached
  assert_consensus_reached(proposal_id, :accept)
  assert_variable_updated(:learning_rate, 0.01)
end

Leader Election Testing

test "leader election handles node failures" do
  # Setup agents with different capabilities
  agents = setup_test_agents_with_capabilities()
  
  # Start election
  {:ok, election_id} = MABEAM.Coordination.start_election()
  
  # Simulate node failure during election
  [leader_candidate | _] = agents
  Process.exit(leader_candidate, :kill)
  
  # Verify election completes with remaining candidates
  assert_election_completed(election_id)
  assert_leader_elected_from_remaining_candidates()
end

Summary

This coordination system provides:

  1. Distributed Consensus: Reliable parameter agreement across agent networks
  2. Leader Election: Automatic coordination leadership with failover
  3. Variable Synchronization: Consistent parameter updates with conflict resolution
  4. Barrier Synchronization: Coordinated multi-agent operations
  5. Fault Tolerance: Graceful handling of network partitions and agent failures
  6. Scalability: Hierarchical coordination for large agent networks
  7. Performance: Optimized for low-latency, high-throughput coordination

The coordination patterns enable sophisticated multi-agent ML workflows while maintaining the reliability and fault tolerance characteristics of the BEAM platform.