Decompose Large Work Into Specialized Subagents

A large project can be handled by a team of agents rather than one agent carrying every responsibility sequentially. The key pattern is specialization: assign each subagent a narrow task, allow the tasks to proceed in parallel where possible, and use an orchestrating process to combine the work.

There are two levels of parallelism.

Several independent conversations or tasks can run at the same time. One task may build a website, another may prepare a presentation, another may generate an application, and another may create media. The operator starts each job, leaves it working in the background, and returns to inspect the results.

A swarm recruits subagents, gives them names or roles, and assigns distinct responsibilities. In the website example, one subagent was assigned keyword research, other agents wrote posts, and another agent handled design work. The interface exposed each subagent's progress, allowed the operator to inspect individual work, and provided controls to stop or hide agents.

The important design choice is task separation. A subagent should have a clear, bounded responsibility. The keyword research agent had one task: produce the keywords needed for the planned website. Other agents focused on separate content or design responsibilities.

The primary workflow remains responsible for the overall objective. Specialized outputs are useful only when they contribute to the shared deliverable.

Complex outputs often contain several different kinds of work. A full site may require research, writing, design, assembly, and verification. Running all of these in one undifferentiated stream can make the process slow and difficult to inspect.

Subagents make the work visible and separable. The operator can see which part is in progress, which role is responsible, and where a correction may be needed. Parallel execution also allows independent work to proceed without waiting for a single long sequence.

Consider a project to create a niche blog site with several posts.

A useful decomposition is:

1. Research responsibility.

One subagent identifies the relevant keywords for the niche.

1. Content responsibilities.

Several subagents write different posts rather than one agent writing every post in sequence.

1. Design responsibility.

A design-focused subagent develops the visual presentation of the blog.

1. Primary orchestration.

The main workflow tracks progress and brings the pieces into the complete site.

The operator should monitor the team rather than treating it as invisible. The interface exposes progress for each agent and provides a stop control. That pattern is important for any long-running multi-agent system: delegation should not eliminate observability.

A simpler project may not need a formal swarm. Manual parallelism across several independent chats can be sufficient when the tasks do not need a shared orchestrator.

A swarm uses more tokens than a normal single-agent task and can take much longer. In the site-building case, the swarm ran for hours and remained only partway through its planned stages. This makes swarms appropriate for substantial work, not for every request.

Multi-agent work can also produce a large amount of intermediate activity. Progress indicators, per-agent inspection, and stop controls are therefore part of the operational design.

The central mistake is to equate more agents with a better result. Use swarms for massive tasks that require substantial work. Use a single focused agent when it can complete the job more efficiently.

Use subagents when a project contains several substantial, separable responsibilities; give each subagent one clear task and keep the overall workflow observable.