Running tasks in parallel with the /fleet command

The /fleet slash command lets CLI de Copilot break down a complex request into smaller tasks and run them in parallel, maximizing efficiency and throughput.

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Introduction

The /fleet slash command in CLI de Copilot is designed to take an implementation plan and break it down into smaller, independent tasks that can be executed in parallel by subagents. This allows for faster completion of complex requests that involve multiple steps.

This article gives an overview of the /fleet slash command. For details of how to use it, see Speeding up task completion with the /fleet command.

How /fleet works

When you use the /fleet command, the main Copilot agent analyzes the prompt and determines whether it can be divided into smaller subtasks. It will assess, based on the nature of the subtasks and their dependencies, whether these can be efficiently executed by subagents. If it decides to assign some or all of the subtasks to subagents, it will act as orchestrator, managing the workflow and dependencies between the subtasks. Where possible, the orchestrator agent will run the subagents in parallel, allowing the whole task to be completed more quickly.

Benefits of using /fleet

  • Speed of task completion: The main benefit of using the /fleet command is that a large, multi-part task can be completed more quickly by running subtasks in parallel. Whether parts of a large task can be worked on in parallel will be determined by the dependencies between the subtasks. Some tasks, such as creating a suite of tests for a new feature, are well suited to parallelization and will typically complete faster when you use the /fleet slash command.

  • Specialization: If you've defined custom agents that are specialized for certain types of work, these may be used by the subagents. This allows for specialization, with the subagents using the custom agents best suited to the specific subtask they are working on.

    By default, subagents use a low-cost AI model. However, you can tell Copilot to use a specific model for part of the work. For example, within a larger prompt, you could specify ... Use GPT-5.3-Codex, to create ... Use Claude Opus 4.5, to analyze .... If a subagent uses a custom agent profile that specifies a particular AI model, then that model will be used by the subagent. Using a specific model may produce better quality results for particular types of subtask.

    If custom agents are available, Copilot will decide whether to use one to complete a particular subtask. However, if you know that a specific custom agent is well-suited to a particular subtask, you can specify this in your prompt by using @CUSTOM-AGENT-NAME. For example, within a larger prompt: ... Use @test-writer to create comprehensive unit tests for ....

    For more information, see Creación y uso de agentes personalizados para CLI de GitHub Copilot.

  • Context window: Each subagent has its own context window, separate from the main agent and other subagents. This allows each subagent to focus on its specific task without being overwhelmed by the full context of the larger task.

When should you use /fleet?

  • Large or complex tasks: When your request involves multiple independent steps, such as refactoring several files, updating dependencies, or running tests across modules.
  • Parallelizable work: If your task can be split into subtasks that don’t depend on each other.
  • Automated workflows: When you want the quickest possible completion of a large task—for example, when you're using autopilot mode to allow Copilot to work autonomously.

Points to consider

  • Premium request usage: When you submit a prompt in the CLI and Copilot interacts with the selected large language model (LLM) to generate a response, this consumes premium requests. The number of premium requests consumed depends on the model that's currently selected. More interactions with the LLM result in more premium requests being consumed.

    Each subagent can interact with the LLM independently of the main agent, so splitting work up into smaller tasks that are run by subagents may result in more LLM interactions than if the work was handled by the main agent. Using /fleet in a prompt may therefore cause more premium requests to be consumed.

    The billable premium request usage is determined using a multiplier. The multiplier varies depending on which model you use. Use the /model slash command to see the currently selected model and its multiplier, and change the model if required. For more information, see Solicitudes en GitHub Copilot and Acerca de la facturación para planes individuales de GitHub Copilot.

  • Task composition: Work is best suited to execution by multiple subagents if it can be decomposed into independent subtasks. If your request is inherently sequential, using the /fleet slash command mode may not provide any benefit.

Relationship between /fleet and autopilot mode

The /fleet slash command is often used in autopilot mode, but these are distinct features that can be used independently:

  • Autopilot mode allows Copilot to continue working autonomously until a task is complete, auto-responding to requests that would otherwise require user intervention.
  • /fleet is all about using subagents to execute tasks in parallel, while the main agent manages the overall workflow. You can use the /fleet slash command in interactive sessions independently of autopilot mode.

A typical workflow for using /fleet in autopilot mode might look like this:

  1. Press Shift+Tab to switch into plan mode and work with CLI de Copilot to create an implementation plan.
  2. Recognize that the completed plan contains multiple elements and looks like a good candidate for /fleet.
  3. Select the Accept plan and build on autopilot + /fleet option that's displayed when the plan is complete.

For more information about autopilot mode, see Permitir que la CLI de GitHub Copilot funcione de forma autónoma.

Further reading