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“Agent teams” is a plain-language umbrella rather than a standard product or runtime name. In this guide it means one of two things: a coordinating agent that delegates work to subagents through the OpenAI API, or a developer supervising several coding agents in the OpenAI Codex app. These overlap, but they are different implementations with different controls, so the choice between them matters.
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What the terms mean
In OpenAI’s API multi-agent documentation, a root or coordinating agent hands tasks to subagents. Each subagent has its own context, independent tasks can proceed in parallel, and the coordinator collects and combines the results. The subagent is therefore a delegated worker with a bounded job, not a separate team member that negotiates on its own.
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Decision rule: delegate or keep work in one agent
Delegate when all of the following are true:
- The parts are meaningfully independent, so one part does not need another part’s output to start.
- Each part can be described as a bounded question or a specific deliverable.
- A coordinator can reconcile the results without re-doing the whole job.
- Parallel execution or focused context saves enough time or improves the output to justify the extra orchestration and review.
Official guidance cites examples such as independent document review, comparing release notes, and investigating separate causes of a problem. Those tasks share a shape: each piece can be finished without the others.
Keep work in one agent when:
- The task is short enough that a single pass finishes quickly.
- Each step depends on the result of the previous one.
- Splitting the work would make handoffs and synthesis take more effort than the parallel work saves.
OpenAI’s own phrasing is direct: “Keep short tasks and dependent steps in the main agent.”
What to put in a delegation brief
A subagent works only with what it is given, so the brief determines most of the output quality. Every delegation should state:
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- The task: one question or one deliverable, not a broad goal.
- Boundaries: which files, sources, or data the subagent may touch, and what it must leave alone.
- Expected result: the format, length, and fields the coordinator needs back, such as a list of findings with the file and line for each.
- Stop condition: what counts as done, so the subagent does not expand its scope.
The coordinator then checks each returned output, reconciles disagreements between subagents, and presents a single answer. This review step is editorial practice based on the coordinator’s documented synthesis role. Parallel agents do not guarantee correct results on their own, so the review is part of the design.
Choosing an implementation
The four main options differ mainly in who owns orchestration and session state, and how much integration work you take on.
| Option | Who owns orchestration and state? | Good fit | Trade-offs |
|---|---|---|---|
| Agents API | OpenAI runs the Codex harness, session, orchestration, context compaction, and recovery. Your application supplies the task, tools, and configuration. | Long-running managed agent workflows where lower integration effort and managed session state matter more than owning the runtime. | You own less of the runtime. The multi-agent feature is labeled beta in the OpenAI documentation, and usage is billed (see the cost section). |
| Agents SDK | Your application uses the SDK runner and controls deployment, storage, approvals, and runtime integration. | Product teams building reusable custom workflows with their own tools and application logic. | More integration work, and you are responsible for state and runtime choices. |
| Responses API | Your application works directly with model responses. You can build orchestration yourself or use hosted orchestration features where available. | Direct model access or custom integration where you want control over the agent loop. | More application responsibility. The multi-agent feature on this surface is labeled beta. |
| Codex app | A person manages agent threads and reviews changes. Built-in worktrees give agents isolated copies of a repository. | Developers coordinating parallel coding tasks who want to keep human review in the loop. | This is an app workflow, not API subagent orchestration. Confirm current product availability and plan limits before relying on it. |
When comparing these, evaluate five things: how independent the tasks are and how much synthesis they require; how much control you need versus how much orchestration you want managed; how state and context are kept across turns; where tools run and what isolation exists between agents; and what the cost and feature availability look like for your account.
Limits, defaults, and beta status
Concurrency settings are specific to each API surface and should not be treated as one universal limit.
Agents API
Enable multi-agent orchestration when you create a session, then configure max_concurrent_subagents. When orchestration is enabled, the OpenAI Agents API multi-agent guide describes a default of six.
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Responses API multi-agent feature
The Responses multi-agent guide describes a separate setting, max_concurrent_subagent_turns, which limits active subagent turns across the whole agent tree. Its documented default is three. Do not copy the Agents API value into a Responses design, or the reverse.
Codex app
Agents run in separate threads, and you review each thread’s changes in that thread. Worktrees are what keep those copies apart. Plan limits for the app are a separate matter and should be checked on your account.
Beta labels, model eligibility, defaults, and plan access change over time. Confirm them in the current OpenAI documentation and in your own account before committing a production design.
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Cost has several components
OpenAI’s Agents API overview states: “Model usage is billed at the selected model’s API rates. OpenAI tools use their standard rates, and OpenAI-hosted sandboxes use standard container rates.”
That gives you three cost components to estimate separately:
- Model usage: billed at the selected model’s API rates. Each subagent turn consumes model usage, so fan-out multiplies this component.
- Tools: OpenAI tools are billed at their standard rates.
- Sandboxes: OpenAI-hosted sandboxes are billed at standard container rates.
There is no single cost figure for “agent teams.” The total depends on the model you select, how many subagents you run, how many turns each takes, which tools they call, and whether you use hosted sandboxes. Estimate these for a realistic run before you scale up.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Speed and quality: what the evidence supports
The official documentation does not include a controlled comparison showing that multiple agents are always faster, cheaper, or more accurate than one agent. The benefit depends on the workload. A set of genuinely independent tasks can finish sooner in parallel, while a chain of dependent steps often finishes sooner in one agent because no handoffs are needed. Extra review and reconciliation add time and cost in both cases.
If speed or quality matters for your workflow, measure it on a representative set of your own tasks, comparing one agent against a delegated setup on the same inputs.
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Worked example: reviewing a set of contracts
Suppose you need to check 40 vendor contracts for three clause types. The contracts are independent, so each can be reviewed separately. A workable design:
- The coordinator splits the 40 contracts into batches and gives each subagent a brief: review these files for the three clause types, return each finding with the file name, clause text, and a severity label.
- Subagents run in parallel within your configured concurrency limit.
- The coordinator merges the findings, flags any contract where two subagents disagree on a clause, and sends those cases back for a second look.
- A single agent handles the final summary, because it depends on the merged results.
This is an illustrative pattern, not a benchmark. The same job would be a poor fit for delegation if every contract depended on the outcome of the previous one.
Troubleshooting common problems
- Subagents return conflicting answers. Ask the coordinator to reconcile them and to name which output each claim came from. Tighten the brief so the subagents use the same definitions.
- Work waits in a queue. You have likely reached the concurrency cap. Reduce the number of parallel tasks, or raise the setting within the limit documented for your surface.
- Outputs are too broad to merge. The task was not bounded enough. Split it again, and specify the expected result format in the brief.
- Costs are higher than expected. Count subagent turns and tool calls per run. Fan-out multiplies model usage, so a small reduction in parallel tasks can lower the bill noticeably.
- A dependent step ran in parallel by mistake. Move that step back to the main agent, and make it run only after its inputs are ready.
► Use subagents for independent, bounded, parallelizable work with a clear synthesis step.
► Keep short or dependent work in one agent.
► Choose the surface by how much orchestration and state you want the platform to manage versus manage yourself.
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