What problem does it solve?
It helps you reduce time-to-fix by running multiple independent investigations concurrently when you have several unrelated issues that waste time if handled sequentially.
Core Features & Use Cases
- Agent isolation: Create each agent with only the context it needs so they do not inherit your session state or interfere with each other.
- Independence-based dispatch: Group failures by independent problem domains and dispatch one agent per domain.
- Scope-locked tasks: Provide clear constraints and expected outputs so agents stay focused and produce actionable summaries.
- Safe integration: Review each agent’s results, check for conflicts, and run the full suite to confirm combined correctness.
Use case example: You have 3 failing test files caused by different subsystems (e.g., abort behavior, batch completion behavior, and a race condition). Dispatch three agents—one per file—to identify and fix each root cause in parallel without stepping on shared changes.
Quick Start
Tell your AI to dispatch one agent per independent failing test file, keep each agent’s instructions self-contained and scope-limited, then integrate their findings and verify with a full test run.