Use subagents when a task can be split into independent workstreams that each have a clear question and deliverable. Keep short, sequential, or tightly coupled work with one agent. A coordinator must still compare the results, resolve conflicts, and produce the final answer; parallel work is not automatically faster or better.
What subagents do—and what they don’t
A subagent handles a bounded part of a larger task while a main, or coordinating, agent directs the work. For example, the coordinator might ask separate agents to review different documents or investigate distinct possible causes of a failure. Those agents return findings; they do not remove the coordinator’s responsibility to decide what matters and combine the results.
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OpenAI’s Agents API multi-agent guide puts the delegation test plainly: “Use subagents for independent tasks, such as reviewing separate documents or investigating different causes of a failure. Give each task a clear question and expected result.”
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Parallelism is most promising when the workstreams can make progress without waiting for one another, and when keeping their context separate helps. OpenAI’s Responses API multi-agent guide identifies codebase exploration, documentation, implementation, and testing or review-like work as potential independent streams.
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- Separate inputs: Different agents can inspect distinct documents, modules, or failure hypotheses.
- Clear outputs: Each stream can return a concrete finding, such as a summary, a list of likely causes, or a review of a defined area.
- Useful context boundaries: Splitting the task can keep each agent focused on the evidence relevant to its question.
These are conditions for trying delegation, not a guarantee of better speed or quality. The official guidance describes qualitative benefits and trade-offs; it does not establish a universal productivity gain.
When to keep the work with one agent
If one step must finish before the next can begin, several agents may spend time coordinating rather than advancing the task. The same is true when progress depends on frequent writes to shared state or when one slow operation is the bottleneck. The Responses API guide cautions that multi-agent work can be less beneficial in these cases and can use more tokens.
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- Sequential work: The next action depends on the result of the current one.
- Overlapping edits: Multiple agents would change the same files or shared state without a clear ownership plan.
- Small scope: Delegating a task that is already short adds handoffs without much independent work to unlock.
- High synthesis burden: Combining the outputs would take as much effort as doing the work directly.
For shared files, assign ownership or define how changes will be coordinated before starting. The Agents API guide notes that the coordinator and subagents share an environment filesystem; shared mutable work can reduce the value of parallel execution.
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The following is a practical interpretation of OpenAI’s guidance to use independent tasks with clear questions and expected results—not a prescribed checklist.
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- Find the independent pieces. Identify work that can proceed without another stream’s findings. Keep dependent steps with the coordinator.
- Give each agent one question. Make the boundary explicit: specify which document, component, failure cause, or other input it should examine.
- Name the expected deliverable. Ask for a defined result, such as findings with supporting evidence, rather than an open-ended account of the whole project.
- Provide shared context and limits. Include only the background needed to answer the question, and say what to report if the evidence is incomplete or inconclusive.
- Plan shared-state work. If agents may edit the same environment, assign non-overlapping ownership or establish a coordination method first.
- Synthesize centrally. Compare the outputs, investigate disagreements, and decide what belongs in the final result. Do not treat separate reports as a finished answer.
Choose the right runtime
“Subagents” can refer to different OpenAI products and capabilities. The setup, availability, and responsibilities vary, so first identify which runtime you mean.
Codex client features
OpenAI’s Help Center article, Using Codex with your ChatGPT plan, describes an agent view and multi-agent tools for opening, reading, or forking tasks. For installation, updates, commands, and configuration, use the live Codex CLI guide. Controls may differ across clients or accounts, so do not assume a feature appears identically for every user.
Managed Agents API
The Agents API provides a managed Codex harness. OpenAI manages sessions, orchestration, context compaction, and recovery; the application supplies tools and chooses the execution environment. The documentation describes an agent in terms of its model, instructions, tools, and MCP servers. It also defines an optional sandbox or computer environment, a durable session, and events or items as inputs and outputs. Consult the current API guide for implementation details.
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Responses API multi-agent capability
The Responses API multi-agent guide describes a separate beta capability with its own enablement and model constraints. Its root agent synthesizes subagent responses. The reviewed guide states that the feature is beta for GPT-6.1 Sol and GPT-5.6 models, and recommends a max_concurrent_subagents default of 3 for most workloads. Beta availability, supported models, and request formats can change; check the live guide before relying on those details in an implementation.
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