Orchestration

January 30, 2026

Orchestration is how you coordinate multiple agents — or multiple capabilities within a single agent — to accomplish complex tasks. It's the control plane that decides what runs when, manages handoffs, and maintains coherence across a multi-step workflow.

Why Orchestrate?

Single agents hit limits. They run out of context, lose focus on long tasks, or lack specialized knowledge. Orchestration lets you:

• Specialize: Dedicate agents to specific tasks
• Parallelize: Run independent work simultaneously
• Compose: Chain capabilities into workflows
• Scale: Add capacity without redesigning

As one developer noted:

The real risk isn't 'swarms' vs 'flocks' — it's when coordination scales faster than accountability. Multi-agent systems need ownership models as much as orchestration models.

Orchestration Patterns

Sequential Pipeline

Agents run in order, each processing the output of the previous:

[Research Agent] → [Analysis Agent] → [Writing Agent] → Output

Use when: Tasks have natural phases, each requiring different expertise.

Parallel Fan-Out

Multiple agents work on independent subtasks simultaneously:

          ┌─[Agent A]─┐
Input ───►├─[Agent B]─┼──► Combine
          └─[Agent C]─┘

Use when: Work can be divided into independent chunks.

Router/Dispatcher

A coordinating agent routes requests to specialists:

          ┌─[Code Agent]
[Router]──├─[Search Agent]
          └─[Data Agent]

Use when: Different request types need different handling.

Hierarchical

A manager agent delegates to workers, who may have their own sub-workers:

[Manager]
   ├─[Worker A]
   │    └─[Helper 1]
   └─[Worker B]
        └─[Helper 2]

Use when: Complex tasks decompose into nested subtasks.

The Single-Agent Alternative

Multi-agent isn't always better. As observed in production:

single intelligent agent with proper tool access consistently outperforms complex multi-agent orchestration. the coordination overhead, context bleeding, and error propagation in agent swarms usually isn't worth it.

Before building a swarm, ask:

• Can one agent with good tools handle this?
• Is the complexity justified by the task?
• Do I have the infrastructure for coordination?

Agent Handoffs

The critical moment: transferring control from one agent to another.

What transfers:

• Current task state
• Relevant context
• User intent
• Constraints and guardrails

What doesn't:

• Full conversation history (usually summarized)
• Agent-specific reasoning
• Intermediate attempts

// OpenAI Agents SDK handoff example
const handoff = {
  target: "specialist-agent",
  context: {
    task: "Analyze the code for security issues",
    files: ["src/auth.ts", "src/db.ts"],
    constraints: ["Focus on SQL injection", "Check auth flows"]
  }
};

State Management

Multi-agent systems need shared state:

• Task state: What's been done, what's pending
• Shared memory: Facts all agents should know
• Coordination state: Who's working on what

Approaches:

• Centralized: Single source of truth (database, Durable Objects)
• Message passing: Agents communicate via events
• Shared context: Common context window injected everywhere

Framework Support

Different frameworks handle orchestration differently:

OpenAI Agents SDK

Built-in multi-agent primitives:

• Agent: Autonomous unit with tools and instructions
• Handoff: Transfer control between agents
• Guardrail: Constraints on agent behavior

LangGraph

Graph-based orchestration:

• Nodes are agents or functions
• Edges define flow and conditions
• Built-in state persistence

Vercel AI SDK

Workflow patterns via composition:

• Sequential chains
• Parallel execution
• Conditional routing

Cloudflare Agents

Durable Objects provide stateful coordination:

• Each agent is a Durable Object
• WebSocket communication between agents
• Persistent state per agent

Building Orchestration

A minimal orchestrator in pseudocode:

async function orchestrate(task: Task) {
  // 1. Analyze task
  const plan = await planner.createPlan(task);
  
  // 2. Execute steps
  for (const step of plan.steps) {
    const agent = selectAgent(step.type);
    const result = await agent.execute(step, context);
    context.update(result);
    
    // 3. Handle failures
    if (result.failed) {
      await handleFailure(step, result);
    }
  }
  
  // 4. Synthesize results
  return synthesizer.combine(context.results);
}

Common Pitfalls

Context bleeding: Agents leak information between tasks that should be isolated.

Coordination overhead: More time spent coordinating than working.

Error propagation: One agent's mistake cascades through the system.

Infinite loops: Agents ping-pong without progress.

Resource exhaustion: Parallel agents overwhelm rate limits or context.

The Swarm Question

"Agent swarms" are having a moment. As one developer observed:

Multi-agent swarm meets hot laptop: 10+ Claude agents to build a basic web browser. Hours of churn, rate limits, and roaring fans. Good coordination but orchestration and hardware still bite.

Swarms work for:

• Embarrassingly parallel tasks
• Tasks requiring diverse expertise
• Research and exploration

Swarms struggle with:

• Tight coordination requirements
• Limited API rate limits
• Tasks requiring shared state

Sources

• OpenAI Agents SDK — Multi-agent primitives
• LangGraph — Graph-based orchestration
• Cloudflare Agents — Stateful agent infrastructure

See also: Tools & Function Calling · Memory & State · OpenAI Agents SDK