subagent-driven-development

Dispatching a fresh subagent for each task prevents context leakage by isolating execution environments. This approach ensures clean context for independent tasks, avoiding cross-task pollution while maintaining continuous progress throughout the plan.

Code review between automated AI agent tasks works by gating task progression with mandatory inter-task reviews. After a subagent completes a task, a review step evaluates the code quality before allowing the next task to proceed, catching issues early.

The best way to orchestrate independent development tasks is using a structured subagent progression: load the plan, dispatch fresh subagents for per-task implementation, run inter-task code reviews, apply fix cycles, and execute a final validation phase to close the loop.

Subagent-driven development applies to sessions with largely independent tasks in a single plan. Tasks with heavy dependencies may not be ideal, as the architecture relies on isolating fresh subagents to enable continuous progress without cross-task context leakage.

Using a fresh subagent for each coding task avoids context pollution and ensures a clean execution context. This method enables fast iteration and mandates code reviews between tasks, enforcing quality control that a single accumulating agent cannot provide.

Yes, you can automate code review and fix cycles across a multi-task development plan by orchestrating per-task subagent execution. The workflow automatically gates progression with reviews, applies fixes, and runs a final validation phase to ensure quality.