LLMs are restricted by their context limit - how many tokens they can fit in their working memory at any given time. These values have not increased much over the past two years even as the LLMs themselves have seen dramatic improvements in their abilities - they generally top out at around 1,000,000, and benchmarks frequently report better quality results below 200,000.

Carefully managing the context such that it fits within those limits is critical to getting great results out of a model.

Subagents provide a simple but effective way to handle larger tasks without burning through too much of the coding agent’s valuable top-level context.

When a coding agent uses a subagent it effectively dispatches a fresh copy of itself to achieve a specified goal, with a new context window that starts with a fresh prompt.

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