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Multi-agent systems coordinate by dividing work, deciding which agent controls each step, and passing the right context or results between agents. The main patterns are a manager that calls specialists, a handoff that transfers control, an orchestrated group chat, and workflows directed by application code. None is best for every task: choose according to dependencies, ownership, context needs, and how much control the application requires.
How do multi-agent systems coordinate tasks?
Coordination is more than assigning subtasks to several agents. A workflow needs to define what each agent is responsible for, how the next step is selected, and what information or artifacts move between steps. OpenAI’s Agents SDK puts it simply: “Orchestration refers to the flow of agents in your app.” OpenAI Agents SDK: Agent orchestration
Four common patterns describe who directs that flow:
| Pattern | Who controls the next step? | Best suited to |
|---|---|---|
| Manager calling agents as tools | A manager remains in control and invokes specialists for bounded tasks. | Work where one agent must synthesize specialist results and remain responsible for the user-facing answer. |
| Handoff | Control passes to a specialist, which takes over the next part of the interaction. | Work where a specialist should own the next stage rather than merely return a result to the original agent. |
| Group chat | A central orchestrator selects the next speaker and synchronizes conversation histories. | Iterative work where participants need to contribute within a coordinated conversation. |
| Code-directed orchestration | Application code decides the sequence, parallel branches, or evaluation loops. | Work requiring explicit control over workflow order and execution. |
OpenAI documents manager, handoff, and code-directed approaches; Microsoft’s Agent Framework documents handoff and group-chat orchestration. These are different workflow designs, not a universal ranking. OpenAI Agents SDK: Agent orchestration Microsoft Agent Framework: Handoff orchestration Microsoft Agent Framework: Group chat orchestration
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What is the difference between agents as tools and agent handoffs?
Manager calling agents as tools
The manager keeps ownership of the task. It can ask a specialist to perform a defined job, receive the result, and decide how that result fits the response or what should happen next. This gives the application a clear place to synthesize work and apply shared constraints.
Handoff to a specialist
A handoff changes who owns the next interaction: the receiving specialist takes control. OpenAI describes this as routing to a specialist that takes over. Microsoft’s handoff orchestration describes a peer mesh without a central workflow orchestrator. The distinction is therefore about control and responsibility, not merely which agent has a particular skill. OpenAI Agents SDK: Agent orchestration Microsoft Agent Framework: Handoff orchestration
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When should I use a manager agent, a handoff, group chat, or code?
- Use a manager when specialists should return bounded outputs and one agent needs to validate and combine them.
- Use a handoff when the next specialist should take responsibility for the next stage.
- Use group chat when work benefits from iterative contributions and a central orchestrator should select speakers and keep histories synchronized.
- Use code-directed orchestration when the workflow order, branching, parallel execution, or evaluation loop should be explicit in the application.
OpenAI notes that parallel work can speed tasks such as separate research or codebase exploration. It also notes that extra agents can raise token use and may not help when tasks are tightly dependent or agents frequently write to shared mutable state. The documentation does not provide a controlled, generalizable performance comparison across these patterns, so treat speed and cost as task-specific tradeoffs rather than a guarantee. OpenAI API: Multi-agent
How do AI agents share context?
“Shared context” can mean several different things: replaying a common transcript, sending a task-specific brief to a specialist, maintaining session state, or referring to conversation state stored by a server. These choices affect what an agent sees and whether earlier material is included again.
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OpenAI’s running-agent guide distinguishes application-managed replay history, SDK sessions, conversation IDs, and previous response IDs as continuation strategies. It advises choosing one strategy per conversation unless the application deliberately reconciles them. Combining local replay with server-managed state without that reconciliation can duplicate context. OpenAI API: Running agents
Context in handoffs and group chats
In Microsoft’s documented handoff flow, agents retain distinct session instances while user and agent messages are synchronized. Tool-control content—such as tool calls and their results—is not broadcast as ordinary conversation history. In group chat, the orchestrator synchronizes each agent’s session with the conversation history before that agent’s turn. These approaches share conversation information differently; neither means every internal detail is automatically visible to every participant. Microsoft Agent Framework: Handoff orchestration Microsoft Agent Framework: Group chat orchestration
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What should a reliable multi-agent workflow specify?
Define the boundaries before adding agents. For each step, document the task owner, inputs, expected output, and how the coordinator will use that output. Also decide what context is shared, what stays local to a worker, and which decisions or artifacts must be returned. These choices make it possible to validate specialist work before combining it and to avoid passing irrelevant or duplicated history.
- Task ownership: identify whether the manager retains responsibility or a handoff transfers it.
- Context boundaries: state which conversation messages, briefs, session state, or artifacts are available to each agent.
- Return contract: specify the result a specialist must provide and what the coordinator checks.
- Workflow control: decide whether the next step is selected by an agent, an orchestrator, or application code.
- Evaluation and monitoring: observe runs and evaluate whether the workflow produces reliable results; orchestration guidance recommends monitoring and investment in evaluation. OpenAI Agents SDK: Agent orchestration
Further reading on multi-agent systems
For foundational background beyond current LLM-agent implementation patterns, MIT Press’s second edition of Multiagent Systems, edited by Gerhard Weiss, covers topics including agent organizations, communication, coordination, distributed cognition, and engineering. It is a broad textbook, not a current implementation manual for LLM agent frameworks. MIT Press: Multiagent Systems, second edition
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