Process Orchestration

graph TD
    O[Orchestrator] --> TL[Task Ledger]
    O --> PL[Progress Ledger]
    TL --> A1[Agent 1]
    TL --> A2[Agent 2]
    PL --> TL
    
    subgraph Agents
        A1
        A2
    end

• Task Management
  
  • Decomposition strategies
  • Priority handling
  • Resource allocation
  • Progress tracking
• Implementation Example
  

  class ProcessOrchestrator:
      def __init__(self):
          self.task_ledger = TaskLedger()
          self.progress_ledger = ProgressLedger()
          self.agents = AgentPool()
  
      def orchestrate(self, process):
          tasks = self.decompose_process(process)
          for task in tasks:
              agent = self.agents.select_for_task(task)
              self.task_ledger.assign(task, agent)
              self.monitor_execution(agent, task)
  

State Management

stateDiagram-v2
    [*] --> Pending
    Pending --> Active
    Active --> Stalled
    Active --> Completed
    Stalled --> Active
    Completed --> [*]

• Key Components
  
  • State transitions
  • Context preservation
  • Failure recovery
  • Progress tracking
• Implementation Example
  

  class StateManager:
      def transition(self, process_id, from_state, to_state):
          with self.state_lock(process_id):
              current = self.get_state(process_id)
              if self.can_transition(current, to_state):
                  self.update_state(process_id, to_state)
                  self.notify_observers(process_id, current, to_state)
  

Agent Communication

sequenceDiagram
    participant O as Orchestrator
    participant A1 as Agent 1
    participant A2 as Agent 2
    participant M as Memory System
    
    O->>A1: Assign Task
    A1->>M: Store Context
    A1->>A2: Hand off Subtask
    A2->>M: Retrieve Context
    A2->>O: Report Progress

• Protocol Design
  
  • Message formats
  • Context sharing
  • Error handling
  • Progress reporting
• Implementation Example
  

  class AgentCommunication:
      async def handle_message(self, message):
          match message.type:
              case "TASK_ASSIGNMENT":
                  return await self.process_task(message)
              case "HANDOFF":
                  return await self.handle_handoff(message)
              case "PROGRESS_UPDATE":
                  return await self.update_progress(message)
              case "ERROR":
                  return await self.handle_error(message)
  

Process Definition

process:
  name: "Document Analysis"
  steps:
    - type: "extraction"
      agent: "document_processor"
      requirements:
        - "text_extraction"
        - "metadata_analysis"
    - type: "analysis"
      agent: "content_analyzer"
      dependencies:
        - "extraction"
    - type: "summary"
      agent: "summarizer"
      dependencies:
        - "analysis"

Error Recovery

graph TD
    E[Error Detected] --> A{Recoverable?}
    A -->|Yes| R[Retry Logic]
    A -->|No| F[Failure Handling]
    R --> B{Success?}
    B -->|Yes| C[Continue]
    B -->|No| F

Distributed Issue Tracking with beads

• Integration with Agent Systems
  

  # Create a task for an agent session
  bd new "Implement user authentication" "Add OAuth2 flow with JWT tokens"
  
  # Agents can comment on progress
  bd comment www.wal.sh-abc "Completed OAuth2 configuration"
  
  # Close when done
  bd close www.wal.sh-abc "Implementation complete, tests passing"
  

• Key Properties for APM
  

  Property	Benefit
  Git-backed	State travels with code, no external deps
  Distributed	Works offline, syncs on push
  Context-aware	Task history preserved in commit graph
  Agent-friendly	Simple CLI for automated task management

• Multi-Session Coordination
  

  # Agent session can track its own bead
  class AgentSession:
      def __init__(self, task_description):
          self.bead_id = self.create_bead(task_description)
  
      def create_bead(self, description):
          result = subprocess.run(
              ['bd', 'new', description],
              capture_output=True, text=True
          )
          return self.extract_bead_id(result.stdout)
  
      def log_progress(self, message):
          subprocess.run(['bd', 'comment', self.bead_id, message])
  
      def complete(self):
          subprocess.run(['bd', 'close', self.bead_id])
  

• Workflow Example
  

  sequenceDiagram
      participant U as User
      participant A1 as Agent Session 1
      participant B as beads (bd)
      participant A2 as Agent Session 2
  
      U->>B: bd new "Feature request"
      B->>A1: Assign bead-xyz
      A1->>B: bd comment "Starting research"
      A1->>B: bd comment "Design complete"
      A1->>A2: Context handoff
      A2->>B: bd comment "Implementation started"
      A2->>B: bd close "Feature complete"
  

Performance Considerations

Scaling Strategies