Handoff vs. Dispatch: Two Collaboration Models

Two Collaboration Models

Multi-agent systems have two broad patterns for how agents cooperate. Dispatch places a central orchestrator in charge of all coordination: it creates subtasks, decides which worker receives each one, and collects results. The orchestrator always retains control. Handoff has agents pass control directly to a peer: Agent A determines that Agent B is better suited for the remaining work, transfers the entire execution context to B, and terminates itself.

┌─────────────────────────────────────────────────────────────────┐
│           Dispatch vs. Handoff Control Flow                     │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Dispatch:                                                      │
│  User → Orchestrator → Worker A  (returns result)              │
│                      → Worker B  (returns result)              │
│                      → Worker C  (returns result)              │
│                     ← Orchestrator synthesizes                  │
│                                                                 │
│  Handoff:                                                       │
│  User → Agent A → Agent B → Agent C → final answer             │
│          (full control  (full control                           │
│           transfer)      transfer)                              │
│          A no longer     B no longer                            │
│          involved        involved                               │
└─────────────────────────────────────────────────────────────────┘

OpenAI Agents SDK Primitives

The OpenAI Agents SDK elevates the handoff pattern to a first-class primitive. Three core concepts underpin the SDK.

Agent: An LLM execution unit with a specific role, tools, and instructions. It runs its own loop and can declare peer delegation by listing other agents in its handoffs property.

Handoff: The mechanism by which one agent transfers control to another. It does not merely return a result — it transfers the entire execution context. The receiving agent inherits the previous agent’s conversation history and continues from there.

Guardrail: A layer that validates agent inputs and outputs. It operates in parallel with model execution and blocks harmful output or out-of-scope requests. Because Guardrails run deterministically outside the model, an LLM cannot reason its way around a safety constraint.

This is a conceptual pseudocode example; actual API signatures may differ.

# Conceptual example in OpenAI Agents SDK style
from agents import Agent, Handoff, Runner

# Define specialized agents
code_agent = Agent(
    name="CodeAgent",
    instructions="Handle code writing and debugging.",
    tools=[read_file, write_file, run_tests],
)

research_agent = Agent(
    name="ResearchAgent",
    instructions="Handle technical documentation search and analysis.",
    tools=[web_search, read_url],
    # hand off to CodeAgent when coding is required
    handoffs=[Handoff(agent=code_agent)],
)

triage_agent = Agent(
    name="TriageAgent",
    instructions="Analyze user requests and route to the appropriate agent.",
    handoffs=[
        Handoff(agent=research_agent),
        Handoff(agent=code_agent),
    ],
)

# Execution: TriageAgent starts; handoffs occur as needed
result = Runner.run(triage_agent, user_message)

When Handoff Fits Best

The handoff model shines under the following conditions.

Clear specialization boundaries: When Agent A reaches the edge of its domain, passing to the specialist agent for that domain is natural. A customer service bot handing off a technical support question to a tech agent, or a billing inquiry to a payments agent, is the archetypal case.

No result aggregation needed: Handoff is a linear chain. The final agent responds directly to the user, with no separate synthesis step. This is a good fit when the task can be handled sequentially through a series of transformations.

Context continuity matters: When the next agent must inherit the information collected by the previous one, handoff delivers the full context intact.

When Dispatch Fits Best

Result aggregation is required: If multiple workers’ results must be combined into one coherent answer, you need an orchestrator. Handoff does not naturally express this synthesis step.

Parallelism is critical: Running ten subtasks simultaneously requires an orchestrator to start all ten workers at once. Handoff is inherently sequential.

Task plans need to change dynamically: An orchestrator can adjust the next round of subtasks each time it receives worker results. Replanningmid-flight is awkward in a handoff chain.

Combining Both Models

Where A2A and Agent Skills Fit

Handoff and dispatch are control-flow patterns inside one system. A2A is an interoperability contract for independently deployed agents, often across team or runtime boundaries, to advertise capabilities and exchange tasks, status, and artifacts. MCP connects an agent host to tools and data; A2A concerns delegation between agents. It is not a replacement for an in-process function call.

Agent Skills package instructions, scripts, and reference material for reusable work. A2A answers “which agent should receive this task?”; Skills answer “which procedure and resources should that agent use?” Both need explicit permissions, input/output contracts, and versioning.

Real systems often combine the two patterns. The top layer uses an orchestrator for parallel dispatch; each worker uses handoffs internally to delegate to sub-specialists. This nested structure lets you get the parallelism benefits of dispatch where you need them while keeping handoff’s simplicity for linear sub-workflows.

Criterion	Handoff	Dispatch
Control flow	linear transfer (A→B→C)	centralized (O→A, O→B)
Result synthesis	not needed (last agent responds directly)	orchestrator synthesizes
Parallelism	inherently sequential	natural parallel execution
Context delivery	full transfer	task spec only
Failure recovery	chain breaks, hard to recover	per-worker independent retry

The next chapter turns to the most fundamental scientific question for multi-agent and long-horizon systems: METR’s research on time horizon — how long a task an AI can reliably complete, and why that number matters.