A multi-agent system splits work among focused agents and defines how they coordinate. For Node.js developers, the key design choice is not simply how many agents to create: it is whether code or a model controls the workflow, who owns the final response, and how the application manages tools, state, approvals, and observability. Multiple agents can make responsibilities easier to separate, but the available documentation does not establish that they automatically improve quality, speed, or cost.
What does a multi-agent system change?
Think of a general-purpose agent as a “monolith” only as a metaphor: it is one agent asked to handle many kinds of work. A multi-agent design assigns distinct responsibilities to specialist agents and gives them a coordination policy. For example, one agent could gather source material, another could check it, and a coordinator could assemble the response. The split is useful only when those responsibilities are meaningfully separable.
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OpenAI’s Agents SDK documentation defines orchestration as deciding which agents run, in what order, and what happens next. That is the central engineering problem: agents do not coordinate merely because there are several of them. The application or model must decide how work moves between them.
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How do agents hand off work?
There are two common patterns, and they differ in who owns the interaction after a specialist is called.
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Agents as tools: the manager stays in charge
A manager agent can call specialist agents as tools. The specialist returns its result to the manager, which remains responsible for synthesizing the final response. This fits a workflow where specialists provide bounded inputs but a single coordinator should maintain the user-facing conversation and reconcile the results.
Handoffs: the specialist takes over
In a handoff pattern, the coordinating agent routes the interaction to a selected specialist. That specialist becomes the active agent for the next part of the interaction. Use this when the specialist should own the subsequent work rather than simply return a result to a manager.
These patterns can be combined. The important decision is explicit ownership: should a specialist contribute an answer to a manager, or should it become the agent handling the next turn?
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Should you use multiple agents or one agent with tools?
Start with the simplest arrangement that gives each responsibility a clear owner. A single agent with tools may be enough when the task is straightforward and the tool calls are easy to control. Multiple agents are worth considering when the work divides into distinct roles, such as gathering material and independently checking it.
- Keep the flow in code when steps are known in advance, tasks are independent, or the sequence must be predictable. JavaScript can run independent tasks concurrently with
Promise.all; a coordinator can then combine their results. - Let a model route or hand off when the appropriate specialist depends on open-ended input and cannot be determined by a simple fixed rule.
- Mix the two when some steps are fixed but a particular routing decision benefits from model judgment. OpenAI’s orchestration guide explicitly supports mixing code-directed and model-directed patterns.
More agents also mean more coordination choices and operational work. The reviewed vendor documentation provides workflow examples, not a head-to-head benchmark proving that multi-agent systems outperform a single agent.
How do you build a multi-agent system in Node.js?
The OpenAI Agents SDK for JavaScript and TypeScript offers one concrete implementation path in its official quickstart. The example uses npm, defines agents and tools, configures handoffs, invokes a runner, and shows how to inspect traces. Package versions can change, so use the current quickstart for exact code and installation details.
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- Initialize a project. Create or open an npm project for the application.
- Install the SDK and schema library. The quickstart installs
@openai/agentsand Zod. - Define focused agents. Give each agent a bounded responsibility rather than duplicating a general-purpose prompt across several agents.
- Attach the tools each role needs. Keep tool access aligned with that role’s task.
- Choose the coordination pattern. Configure a triage agent’s handoffs when the specialist should take over, or use specialists as tools when the manager should retain final-response ownership.
- Run the workflow and inspect it. Invoke the runner, then review traces to see operations, tool calls, and handoffs.
Follow the SDK’s official JavaScript quickstart for the current syntax and setup. Traces make behavior easier to inspect; they do not by themselves prove that an answer is correct.
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Two current implementation choices in the cited official documentation are the OpenAI Agents SDK for JavaScript/TypeScript and Google’s ADK for TypeScript. Their documentation describes capabilities and prerequisites; it is not an independent comparison of quality or performance.
| Option | Documented fit and workflow details | Runtime responsibility |
|---|---|---|
| OpenAI Agents SDK for JavaScript/TypeScript | Official quickstart covers npm setup, agents, tools, handoffs, runner invocation, and traces. | The SDK runs in the application; the application controls deployment, tools, state storage, and approval decisions. |
| Google ADK for TypeScript | Repository documents Node.js and browser ecosystems, ESM and CommonJS support, and sequential, parallel, loop, and routed workflows, including A2A delegation. It requires Node.js 20.19 or newer. | Deployment and state details are not stated in the cited repository information. |
Google’s workflow and compatibility details are claims in the ADK for TypeScript repository, not an independent feature audit. Choose based on the workflow primitives, runtime target, and operational model your application needs; a longer feature list is not evidence of a better fit.
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Who owns state, tools, approvals, and deployment?
These are application design decisions, and their exact boundaries depend on the framework or managed runtime. With the OpenAI Agents SDK, the application owns deployment, tool configuration, state storage, and approval decisions. That gives the application control, but also leaves those responsibilities with its developers.
Managed-agent products use different models. Anthropic’s cited managed multi-agent documentation describes a beta feature identified by the dated header managed-agents-2026-04-01. It describes separate persistent session threads and per-agent configuration alongside a shared sandbox, filesystem, and vault credentials. These specifics apply to that managed product; they should not be assumed to describe SDK-based systems generally.
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How should you observe and evaluate agent behavior?
Instrument runs so the team can inspect the path an answer took: which agent ran, which tools it called, what it returned, and whether a handoff occurred. OpenAI’s quickstart demonstrates traces for reviewing operations, tools, and handoffs. Use that evidence to investigate failures and evaluate outputs against the application’s requirements.
- Check whether the intended specialist handled each task.
- Review tool calls and returned results for relevance and completeness.
- Inspect handoffs to confirm the next agent had the right responsibility.
- Evaluate final outputs for correctness rather than treating a successful trace as proof of correctness.
Observability helps explain what happened; it does not guarantee that the workflow made the right decision or produced a correct result.
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