dispatching-parallel-agents

You can dispatch parallel agents by assigning one agent per independent problem domain with focused prompts and isolated contexts. This prevents cross-contamination of information and preserves your coordination context for follow-up work.

The best way to orchestrate parallel AI agents for unrelated subsystems is to assign one agent per independent domain with clear scopes and constraints. They execute simultaneously and provide aggregated summaries for efficient review.

Yes, you can use parallel agents to diagnose multiple test files with independent root causes concurrently. Each agent receives a focused prompt and isolated context to work simultaneously without shared state interference.

You should dispatch parallel agents when multiple unrelated issues arise simultaneously, such as failures in test files or subsystems with different root causes. Sequential investigation wastes time when problems exist in independent domains.

Prevent cross-contamination by designing focused prompts and isolated contexts for each parallel agent. Assigning one agent per independent problem domain ensures clear scopes and constraints without shared state interference.

Dispatching parallel agents requires truly independent problem domains to be effective. If issues share root causes or require shared state, isolated contexts may prevent agents from identifying cross-domain dependencies.