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Use multiple Codex agents when a task can be split into independent, bounded workstreams whose results can be combined with manageable effort. Keep short tasks and tightly dependent steps with one agent. Because delegation can add token use and coordination work, judge it against your own tasks rather than assuming more agents mean faster or better results.
What makes a task a good fit for multiple agents?
The key question is whether separate agents can make useful progress without waiting on, repeating, or overwriting one another. OpenAI’s Agents API guide recommends subagents for independent work, such as reviewing separate documents or investigating distinct causes of a failure. Its Responses API guide also points to work like exploring separate code areas, comparing proposals, researching different sources, implementing independent components, or investigating different causes.
- Clear boundaries: Each agent has a distinct question, responsibility, and expected result.
- Parallel opportunity: Work can happen at the same time instead of following one required sequence.
- Useful specialization: Separate contexts may help agents focus on different parts of a broad task.
- Manageable integration: The coordinator can combine the results without excessive conflict or rework.
For example, a coordinator might assign agents to inspect separate modules, then review their findings and produce one implementation plan. The split is less useful if each module depends on a decision the other agents have not made yet.
When is one agent the better choice?
Keep the work with one agent when progress depends on a single ordered chain of reasoning, when the task is short, or when multiple agents would frequently change the same files or shared state. OpenAI’s Agents API guidance says to keep short tasks and dependent steps in the main agent. The Responses API guide also warns that subagents can be less beneficial when work relies on shared mutable state or when a slow external operation dominates the task.
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- One sequence: A later step cannot begin until an earlier decision or result is settled.
- Shared-file contention: Agents would be editing the same files and need constant coordination.
- External bottleneck: Most elapsed time is spent waiting on an operation that parallel agents cannot speed up.
- Overhead outweighs benefit: Delegation, synthesis, and review take more effort than the expected time saved or coverage gained.
The last point is a practical decision rule, not a published numeric cutoff. OpenAI’s reviewed guidance does not specify a task size or duration at which delegation becomes worthwhile.
How should agents divide work?
Give each subagent a bounded assignment and define what a useful handoff looks like. The coordinator should retain responsibility for integration and the final answer.
- State the question: Describe the exact problem the agent should answer or workstream it should complete.
- Set the boundary: Specify the files, components, documents, or causes in scope, and identify what is out of scope.
- Define the deliverable: Request a concrete result, such as findings, a proposed change, or a list of evidence with unresolved issues.
- Coordinate ownership: If implementation work touches the same repository, assign file or component ownership where possible. The Codex app announcement describes worktree support for agents working in isolated repository copies, but isolation does not remove the need to integrate changes.
- Review and synthesize: Check results for conflicts, duplicated effort, missing assumptions, and incompatible changes before accepting them.
For broader agent-design patterns, OpenAI’s practical guide to building agents recommends first making the most of a single agent’s capabilities and discusses manager and decentralized coordination patterns. A manager-style arrangement is a natural fit when one coordinator needs to assign work and consolidate outputs.
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Compare a single-agent setup with a clearly defined multi-agent arrangement on representative tasks. Keep the model, tools, task prompt, available context, and acceptance criteria as consistent as possible. Repeat runs because results can vary. The measures below are a practical evaluation proposal based on documented benefits and costs; they are not a published OpenAI benchmark protocol.
- Define success before starting: Write acceptance criteria for task completion and output quality so that “better” is not judged only by speed.
- Choose representative task classes: Include independent research or review, independent implementation areas, sequential reasoning, shared-file editing, and work dominated by an external wait.
- Run both arrangements: Compare one agent with the specific multi-agent setup you expect to use, holding other conditions constant where practical.
- Record the full cost: Measure elapsed time through orchestration, review, and integration; include token or usage cost for delegated work and synthesis.
- Track rework: Note duplicated work, conflicts, defects, and human coordination effort, not just the time agents spend producing initial outputs.
- Repeat and compare by goal: Evaluate the runs separately for speed, quality, coverage, or independent review. The preferred arrangement can differ depending on which outcome matters most.
At minimum, record task success and quality against the criteria, end-to-end elapsed time, token or usage cost, coordination and integration effort, and defects or rework. A setup that finishes agent work sooner may still lose overall if review and conflict resolution erase the time saved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do OpenAI’s published examples establish?
OpenAI’s official guidance provides selection principles and trade-offs, not a controlled performance threshold for Codex multi-agent work. In its Codex app announcement, OpenAI says Codex built a sample racing game using an image-generation skill and a web-game development skill, working independently from one initial prompt. The project used more than 7 million tokens, according to OpenAI’s 2026 account. That is an anecdote about one project—not a single-agent comparison, and not evidence that multiple agents are faster, cheaper, or higher quality.
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The announcement also describes separate agent threads and worktree support. These are product capabilities that can support concurrent, isolated work; they do not guarantee improved results. The reviewed official sources publish no named statistic comparing single-agent and multi-agent Codex outcomes, and no universal break-even point for delegation.
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