Day 35: Orchestrating Teams of AI Agents - Coordination Patterns for Complex Systems

The agent evolution is real. Last posts covered state management, recovery, and production readiness. Agents can now survive failures, resume operations, and run reliably.

Today: Multi-agent coordination — how to orchestrate teams of agents instead of individual actors.

The Coordination Problem

Why Single Agents Aren't Enough

The reality: Complex tasks require:

• Specialization: Different agents excel at different subtasks
• Parallelism: Multiple agents working simultaneously
• Resilience: If one agent fails, others can compensate
• Scalability: More work requires more agents, not bigger agents

The challenge: Coordination. Multiple agents need to:

• Share information efficiently
• Avoid conflicting actions
• Manage task dependencies
• Handle failures gracefully

The goal: Orchestration patterns that make teams work better than individuals.

Orchestration Architectures

Hierarchical Organization

interface OrchestratedTeam {
  coordinator: CoordinatorAgent;
  subordinateAgents: Agent[];
  taskDistribution: TaskDistributionStrategy;
}

Key insight: Hierarchical structures work well for task delegation and clear responsibility assignment.

Real-world analogy: Like a project manager assigning tasks to team members based on their skills.

Peer-to-Peer Collaboration

The alternative: Agents negotiate work distribution among themselves without a central coordinator.

Benefits:

• More resilient to coordinator failures
• More flexible task routing
• Better suited for dynamic environments

Blackboard Architecture

Shared state model: All agents read and write to a shared blackboard.

Use cases:

• Projects requiring shared context
• Collaborative problem-solving
• Information sharing across agents

Technical implementation:

class BlackboardOrchestrator {
  private blackboard: Blackboard;
  private subscribers: Map<string, Set<string>>;
  
  async publish(agentId: string, cells: BlackboardCell[]): Promise<void> {
    // Store and notify subscribers
  }
  
  async subscribe(agentId: string, cellKey: string): Promise<void> {
    // Subscribe to specific information updates
  }
}

Communication Patterns

Agent-to-Agent Messaging

Structured messaging protocol:

interface AgentMessage {
  id: string;
  fromAgentId: string;
  toAgentId: string;
  messageType: 'task_assignment' | 'status' | 'failure';
  payload: Record<string, unknown>;
}

Key design considerations:

• Correlation: Match responses to original requests
• Time-to-live: Messages expire if not processed
• Priority: Handle urgent messages first
• Reliability: Ensure delivery or proper failure handling

Failure Management

Circuit Breaker Pattern

Prevents cascade failures: When an agent repeatedly fails, temporarily stop sending tasks to it.

States:

1. Closed: Normal operation (failures tracked)
2. Open: Rejection mode (all requests fail immediately)
3. Half-open: Testing if recovery occurred

Load Balancing

Dynamic Task Distribution

Scoring system: Each agent gets evaluated on:

• Current workload (queue length)
• Capability match for the task
• Recent success/failure rate
• Estimated processing time

Benefits:

• Prevents agent overload
• Routes to most capable agents
• Maintains system stability

Key Insights

When team coordination pays off:

1. Tasks are complex and require multiple capabilities
2. Workload varies across time
3. Failure resilience is required
4. Parallel execution provides significant benefit

When it's complicated:

1. Coordination overhead exceeds benefits
2. Tasks are simple and atomic
3. Limited agent variety/specialization

Next time: Day 36 explores observability practices for monitoring agent coordination in production.

Related Posts:

• Day 34: Evaluating AI Agent Performance
• Day 33: Agent State Management
• Day 32: Agent Development Ecosystem