Multi-agent systems coordinate by dividing work, deciding which agent controls each step, and passing the right context between agents. The key design choice is not simply how many agents to use: it is whether a manager should retain control, a specialist should take over, an orchestrator should run a group discussion, or application code should determine the workflow.
How do multi-agent systems coordinate tasks?
Coordination combines task decomposition, control flow, and context transfer. An application first decides what work can be separated, then chooses who assigns or performs each part, and finally specifies what information and results must move between agents. OpenAI’s Agents SDK defines orchestration as “the flow of agents in your app” in its agent orchestration guide.
Four common patterns differ mainly in who controls the next step:
| Pattern | Who controls the next step? | Useful when |
|---|---|---|
| Manager with agents as tools | A manager calls specialists and retains responsibility for the user-facing task. | A central agent needs to combine specialist results or enforce shared guardrails. |
| Handoff | Control transfers to the receiving specialist, which owns the next part of the interaction. | A task needs to move to a specialist that should take over rather than merely return advice. |
| Group chat | A central orchestrator selects the next speaker and synchronizes participant histories. | Several agents need iterative contributions within a shared conversation, under an orchestrator’s direction. |
| Code-directed orchestration | Application code determines the sequence, branching, parallel work, or evaluation loop. | The workflow needs explicit, predictable control over execution order or task routing. |
OpenAI documents manager, handoff, and code-directed approaches; Microsoft’s Agent Framework documents handoff and group-chat orchestration. These are design options, not a universal ranking. See the OpenAI Agents SDK guide, Microsoft handoff guide, and Microsoft group-chat guide.
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What is the difference between agents as tools and handoffs?
The distinction is task ownership. In a manager pattern, the specialist supplies a bounded result to the manager, which remains accountable for completing the user’s task. In a handoff, the receiving agent takes control of the next part of the interaction. OpenAI describes the first approach as a manager calling agents as tools; Microsoft describes handoff orchestration as a peer mesh without a central workflow orchestrator.
Choose agents-as-tools when one agent must synthesize outputs, decide what to ask next, or apply consistent constraints. Choose a handoff when a specialist should directly own the next step. A handoff is not just a tool call with a different name: it changes who is responsible for continuing the workflow.
When should I use a manager agent versus a group chat?
A manager is suited to a task with a clear owner and bounded specialist assignments. A group chat is suited to iterative participation in a conversation where an orchestrator chooses the next speaker. Microsoft describes group chat as a star topology: the orchestrator sits in the middle and synchronizes each agent’s session with the conversation history before that agent’s turn.
Group chat should not be confused with direct peer handoff. In handoff orchestration, control moves to the receiving specialist; in group chat, the orchestrator remains responsible for selecting speakers and managing the shared discussion. The right choice depends on whether the workflow needs a single synthesizing owner or orchestrated turns among participants—not on a general claim that one pattern performs better.
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“Shared context” can refer to several different mechanisms: replaying a common conversation transcript, passing a task-specific brief, keeping persistent session state, or referring to server-managed conversation state. These approaches do not automatically mean that every agent sees every internal event.
In Microsoft’s documented handoff flow, agents have distinct session instances and synchronize user and agent messages. Tool-control content, such as tool calls and their results, is not broadcast as ordinary conversation history. In group chat, the orchestrator synchronizes an agent’s session with the conversation history before that agent takes a turn. Details are in the handoff and group-chat documentation.
OpenAI’s running-agents guide describes distinct continuation strategies: application-managed replay history, SDK sessions, conversation IDs, and previous response IDs. Choose a deliberate context strategy for each conversation. Combining local replay with server-managed state without reconciling what each contains can duplicate context.
How should you design context transfer between agents?
Define the transfer contract before adding more agents. For each worker, specify:
- What it receives: the task, relevant conversation details, constraints, and any necessary artifacts.
- What stays local: intermediate reasoning or tool-control details that the next agent does not need as ordinary conversation history.
- What it must return: the result, evidence or artifacts, unresolved questions, and any decision the coordinator needs to make.
- What the coordinator validates: whether the output addresses the assigned task, respects shared constraints, and is sufficient to combine or pass onward.
This contract helps preserve useful context without assuming that every participant needs the complete history. It also makes ownership clearer: the receiving agent knows what it must do, and the coordinator knows what it must check.
When does parallel delegation help?
Parallel agents can help when subtasks are independent and can be bounded—for example, separate research questions or distinct areas of code exploration. OpenAI’s Responses multi-agent guide notes possible speed benefits, but also warns that additional agents can increase token use and may be less useful when tasks depend tightly on one another or frequently write to shared mutable state.
Do not parallelize merely because several agents are available. If one task depends on another’s answer, a sequential workflow may avoid stale assumptions and extra synthesis work. If workers modify shared state, specify how writes are coordinated before running them concurrently. The cited documentation offers qualitative tradeoffs, not a controlled comparison establishing a universal speed, cost, or quality winner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you choose an orchestration pattern?
Use the task’s ownership and dependency structure to make the choice:
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- One clear owner must synthesize results: use a manager with specialist agents as tools.
- A specialist should take over: use a handoff and make the ownership change explicit.
- Participants need iterative turns under a central selector: use group chat.
- Order, branching, or evaluation must be explicit in the application: use code-directed orchestration.
- Independent work can proceed separately: consider parallel delegation, while accounting for additional coordination and token use.
Compare options by task ownership, dependency handling, context isolation, synthesis burden, observability, and expected coordination overhead. OpenAI recommends monitoring systems and investing in evaluation as part of agent design; treat evaluation as a continuing way to detect workflow failures, not as a substitute for choosing a clear control model. See the orchestration guide and practical guide to building agents.
Further reading
For foundational coverage beyond current LLM implementations, MIT Press publishes Multiagent Systems, second edition, edited by Gerhard Weiss. The publisher describes its scope as including agent organizations, communication, coordination, distributed cognition, and engineering; it is broad background rather than a current implementation manual.
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