subagent-teams

To prevent context window auto-compaction, you can use AI orchestration to delegate heavy computational work to parallel subagent teams. This isolates complex processing, keeping the main orchestrator's context lean and preserving reasoning quality.

The best way to decompose a large feature implementation is to use subagent teams that break the work into independent subtasks for research, implementation, and testing, assigning optimal models like Haiku, Sonnet, or Opus based on each subtask's complexity.

Parallel task execution with subagents manages codebase context by isolating heavy work in separate subagent sessions. This preserves the main context window, preventing degradation and ensuring consistent performance throughout the task.

Yes, you can assign different models to independent subtasks running in parallel. The orchestration process selects the most cost-effective model—such as Haiku, Sonnet, or Opus—based on the specific complexity requirements of each decomposed subtask.

You should use AI orchestration for task decomposition when your context window is getting large, when independent subtasks can run in parallel, or when performing complex multi-step tasks that would otherwise degrade reasoning quality through auto-compaction.

Your main context window degrades during heavy codebase management tasks because filling it with complex computational work triggers auto-compaction. Delegating these tasks to parallel subagents isolates the processing load and prevents this degradation.