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Use parallel subagents in ChatGPT Work for a complex research task

Split independent research questions across specialist subagents, keep writing centralized, then make the main thread reconcile conflicts before producing the final answer.

Reviewed September 20, 2026Independent tutorialFeatures may vary
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Before you start

Parallel agents are useful when the work can truly be decomposed. They cost more tokens and coordination, so use them for independent exploration, testing, triage or evidence gathering—not for several agents editing the same thing at once.

1. Break the task into independent questions with non-overlapping deliverables

Split the research by questions that can be answered independently, not by arbitrary page counts. Good partitions produce different evidence sets, such as regulation, market data and implementation constraints.

2. Assign one role and evidence standard to each subagent

Give every subagent the same evidence contract: preferred source types, date range, geography, citation requirements and how to express uncertainty. This makes later comparison meaningful.

3. Keep irreversible actions and shared-file edits in the main thread

Keep shared-file edits and consequential external actions in the parent task. Subagents should primarily inspect, calculate or draft isolated findings so parallel work cannot overwrite one another.

4. Wait for all requested subagents, then compare overlaps and disagreements

Wait for all branches, then compare where they agree, disagree or use incompatible definitions. A disagreement is a signal to inspect source quality or assumptions, not something to average away.

5. Ask the main thread to synthesize only after conflicts are resolved or clearly labeled

Synthesize only after conflicts are resolved or explicitly labeled. The final report should preserve provenance so a reader can trace a major conclusion back to the branch and source that produced it.

Try this prompt

Use parallel subagents for this research. Agent A: [question]. Agent B: [question]. Agent C: [question]. Each agent must return evidence, source dates and uncertainties, and must not edit shared files. Wait for all agents, compare disagreements, then produce one synthesis with a conflict table.

Common mistakes

Parallelizing dependent steps; giving every agent the same vague prompt; allowing several agents to write the same file; merging findings before checking contradictions; using subagents for tiny tasks that are faster serially.

Finish check

Each agent has a distinct scope, the main thread can trace every important claim to evidence, and unresolved disagreements are visible instead of being silently averaged away.

开始之前

只有任务确实能拆成相互独立的部分时,并行智能体才值得使用。它会增加 Token 和协调成本,所以更适合独立探索、测试、分诊和证据收集,不适合多个智能体同时修改同一个结果。

1. 把任务拆成互相独立、交付物不重叠的问题

按“能够独立回答的问题”拆分研究,而不是随便按页数切块。好的拆分会形成不同证据集合,例如监管规则、市场数据和落地约束,彼此不会重复做同一件事。

2. 给每个子智能体分配一个角色和统一的证据标准

给每个子智能体同一套证据标准:优先来源类型、时间范围、地区、引用要求,以及不确定性该怎么表达。只有标准一致,最后的横向比较才有意义。

3. 不可逆操作和共享文件修改都留在主线程

共享文件修改和有后果的外部动作留在主任务中。子智能体主要负责独立阅读、计算和形成结论,避免并行任务互相覆盖文件或制造冲突。

4. 等待所有子智能体完成后,对照重复结论和分歧

等所有分支结束后,再比较它们在哪些地方一致、冲突,或使用了不同定义。出现矛盾时要回到来源质量和假设检查,而不是简单取平均。

5. 只有在冲突被解决或明确标记之后,才让主线程生成最终综合结果

只有在冲突已解决或被明确标记后才做总结合并。最终报告要保留来源链,让关键结论能够追溯到具体子任务和原始证据。

可以直接套用的提示词

为这次研究使用并行子智能体。A:负责 [问题];B:负责 [问题];C:负责 [问题]。每个智能体都必须返回证据、来源日期和不确定性,且不得修改共享文件。等待全部完成后比较分歧,再输出一份统一结论和冲突表。

常见错误

把有前后依赖的步骤硬拆并行;每个智能体都收到同一个模糊任务;允许多个智能体同时写同一文件;没检查矛盾就直接合并;很小的任务也强行用子智能体。

完成检查

每个智能体都有清晰边界,主线程能把重要结论追溯到证据,未解决的分歧会明确显示,而不是被“平均掉”。

開始之前

只有任務確實能拆成相互獨立的部分時,並行智能體才值得使用。它會增加 Token 和協調成本,所以更適合獨立探索、測試、分診和證據收集,不適合多個智能體同時修改同一個結果。

1. 把任務拆成互相獨立、交付物不重疊的問題

按“能夠獨立回答的問題”拆分研究,而不是隨便按頁數切塊。好的拆分會形成不同證據集合,例如監管規則、市場數據和落地約束,彼此不會重復做同一件事。

2. 給每個子智能體分配一個角色和統一的證據標準

給每個子智能體同一套證據標準:優先來源類型、時間範圍、地區、引用要求,以及不確定性該怎麼表達。只有標準一致,最後的橫向比較才有意義。

3. 不可逆操作和共享檔案修改都留在主線程

共享檔案修改和有後果的外部動作留在主任務中。子智能體主要負責獨立閱讀、計算和形成結論,避免並行任務互相覆蓋檔案或製造衝突。

4. 等待所有子智能體完成後,對照重復結論和分歧

等所有分支結束後,再比較它們在哪些地方一致、衝突,或使用了不同定義。出現矛盾時要回到來源質量和假設檢查,而不是簡單取平均。

5. 只有在衝突被解決或明確標記之後,才讓主線程生成最終綜合結果

只有在衝突已解決或被明確標記後才做總結合併。最終報告要保留來源鏈,讓關鍵結論能夠追溯到具體子任務和原始證據。

可以直接套用的提示詞

為這次研究使用並行子智能體。A:負責 [問題];B:負責 [問題];C:負責 [問題]。每個智能體都必須回傳證據、來源日期和不確定性,且不得修改共享檔案。等待全部完成後比較分歧,再輸出一份統一結論和衝突表。

常見錯誤

把有前後依賴的步驟硬拆並行;每個智能體都收到同一個模糊任務;允許多個智能體同時寫同一檔案;沒檢查矛盾就直接合併;很小的任務也強行用子智能體。

完成檢查

每個智能體都有清晰邊界,主線程能把重要結論追溯到證據,未解決的分歧會明確顯示,而不是被“平均掉”。

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