Dispatching Parallel Agents

Parallel agent dispatching lets you assign one AI agent per independent problem domain to investigate and fix failures concurrently instead of sequentially. Each agent works on a specific test file or subsystem with clear scope and constraints, drastically reducing total debugging time when root causes are unrelated.

Use parallel agent dispatching when you have 3+ independent failures with different root causes—such as multiple failing test files after a refactoring or unrelated subsystem bugs. The approach works best when problems share no state and each agent's scope is clearly defined.

Problems are suitable for parallel agents when they are independent, share no state between investigations, have clearly defined scopes, and come with explicit expected outputs. If failures are tightly coupled or depend on sequential fixes, sequential debugging is more appropriate.

After parallel agents complete their investigations, review each agent's summary, verify that fixes don't conflict, and integrate the changes systematically. This structured review step ensures non-conflicting solutions are properly merged into your codebase.

Yes, parallel agents excel at debugging failures across different subsystems or separate components. As long as each subsystem failure is independent and requires no shared state between investigations, you can dispatch one agent per subsystem for concurrent problem-solving.

Define explicit constraints for each agent: specific scope (which file or subsystem), clear goal (what to fix or investigate), expected outputs (what the agent should produce), and any limitations on changes or dependencies they can modify. Clear constraints ensure focused, efficient, and non-conflicting work.