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What “multi-agent” means in an application
A multi-agent system is an orchestration design, not a requirement for every AI application. It combines agents with distinct responsibilities and a mechanism for deciding which one acts next. That mechanism may be application code, a model making routing choices, or a mix of both.
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OpenAI’s official Orchestration and handoffs documentation recommends: “Start with one agent whenever you can.” An extra agent adds another set of instructions, tools, traces, and approval surfaces. It is worthwhile when a task branch needs meaningfully different capabilities or rules—not simply because the project is described as multi-agent.
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Design the workflow before adding agents
Write down the workflow’s contract before choosing an orchestration pattern. The contract should make clear what the user wants, what information each step may access, which tools it may call, and what form its output must take. Also separate deterministic application logic from work that actually requires model reasoning.
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- User outcome: State what a successful result should do for the user.
- Inputs and access: Specify which data each step receives and which resources it may use.
- Tools and actions: List the permitted tool calls, including actions that can change external state.
- Output contract: Define what the next step or the user-facing response expects.
- Control flow: Decide which transitions are fixed application logic and which, if any, should be selected dynamically by a model.
Structured outputs or ordinary code can validate classifications and other outputs before they are allowed to choose a later step. Keeping the outer workflow explicit can make its behavior, speed, and cost more predictable than asking a model to route every step. The choice is not all-or-nothing: code can control the overall process while a model handles a decision inside a bounded step.
Build a first version with one agent
- Implement one narrowly instructed agent. Give it only the tools needed for the task and a defined output shape.
- Validate its result in application code. Check that classifications and outputs meet the expected contract before using them to trigger another step.
- Map the actual branches. Identify where different instructions, tools, or policies are required—not just where a task can be divided into smaller prompts.
- Add a specialist for a justified branch. Keep its responsibility and interface narrow enough that the orchestrator can use the result reliably.
This approach makes it easier to see whether the additional specialist solves a real routing or capability problem. The available official architecture guidance does not establish that adding agents automatically improves accuracy, speed, or cost.
Choose who controls the final answer
Two common patterns differ mainly in who remains responsible for responding to the user. In the manager-with-specialists pattern, the manager calls a specialist for bounded work and then synthesizes the result. In a handoff, the current agent transfers control to a specialist that should handle the routed branch itself.
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Use a specialist as a tool when the manager should synthesize
This fits bounded subtasks such as classification, research, or summarization when one outer agent should retain ownership of the user-facing response. The specialist returns work to the manager; it does not take over the conversation.
Use a handoff when the specialist should take over
A handoff fits a routed branch that a specialist should handle directly. Make the routing description concrete so the system can distinguish when that specialist is appropriate, and keep the specialist’s assignment focused.
Choose code-directed or model-directed orchestration
Code-directed orchestration is appropriate when the application should determine the sequence or enforce explicit transitions. It can chain steps, run an evaluator loop with a defined stopping condition, or execute independent work in parallel. Model-directed orchestration is useful when the next step benefits from dynamic planning or routing. A workflow can combine the two: keep critical boundaries in application code and allow model choices within the parts that need flexibility.
Parallelize only work that is independent. If one task depends on an earlier result, run it after that result is available rather than launching it alongside its prerequisite. OpenAI’s Python SDK guidance describes parallel work for independent tasks; it does not support treating parallel execution as a shortcut around dependencies.
Connect agents and tools at the right boundary
MCP and A2A address different connections. MCP connects an agent with tools, APIs, and resources. A2A supports communication and task delegation between independent agents. The A2A documentation describes the protocols as complementary and says A2A is not an agent development kit or a replacement for MCP.
Keep a specialist local when it belongs inside one orchestrator
An in-process subagent is a fit when it is tightly coupled to the orchestrator and should operate within the same application boundary. The Google ADK example describes local subagents as avoiding network latency and protocol serialization overhead.
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Use a remote agent when it needs an independent boundary
A remote agent can run as an independent service and communicate over A2A. This is useful when agents need to collaborate across frameworks or organizational boundaries, or when a service should be deployed independently. It also introduces network communication and protocol handling that a local subagent avoids.
The Google ADK example demonstrates the distinction with a local weather subagent, a currency MCP server, and a currency agent exposed through A2A for a travel agent to consume; its components are deployed to Cloud Run. It illustrates one implementation, not a universal recommendation.
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| Decision | Prefer the first option when | Prefer the second option when |
|---|---|---|
| Model-directed or code-directed | Model-directed: dynamic planning or routing is useful for selecting the next step. | Code-directed: a fixed sequence, explicit control, or predictable behavior matters. |
| Specialist as tool or handoff | Specialist as tool: the manager should retain final-answer ownership and use the specialist for bounded work. | Handoff: the specialist should take over and handle the routed branch directly. |
| Local subagent or remote A2A agent | Local subagent: it belongs inside one orchestrator and low communication overhead matters. | Remote A2A agent: it needs an independent service boundary or cross-framework communication. |
These are choices between documented orchestration patterns, not a ranking of frameworks or a guarantee that one option performs better.
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Monitor behavior and define failure handling
Trace the workflow and evaluate its outcomes as you change the design. Give each specialist a narrow role, a clear input and output contract, and a specific routing description. Measure whether the workflow meets its intended task requirements rather than assuming that a larger number of agents is a sign of better results.
Set explicit limits and error handling in application code for retries, timeouts, and actions with side effects. The appropriate values depend on the application; the architecture guidance cited here does not establish universal numeric limits. In particular, decide in advance how the application handles a failed or invalid specialist result before that result can trigger another step or an external action.
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