dispatching-parallel-agents

Parallel agent dispatching runs multiple AI agents simultaneously on independent failures across different test files or subsystems. Each agent investigates and resolves its assigned problem concurrently, reducing total investigation time compared to sequential debugging.

Use parallel agent dispatching when you have three or more failures with no shared state, isolated to different domains or test files. Sequential investigation works better for interconnected failures requiring shared context or single-domain issues.

Group failures by domain—separate test files, subsystems, or functional areas—ensuring each domain has isolated state. Assign each agent a specific, self-contained task with clear goals and explicit constraints so they work without interfering with one another.

Establish explicit constraints such as do-not-modify production code, limit scope to assigned domain only, and restrict changes to the specific failure being investigated. Clear constraints prevent agents from introducing unintended changes across unrelated systems.

Parallel agent dispatch produces a concise summary per domain containing the root cause analysis and proposed fixes for each independent failure, enabling quick review and validation before implementation.

No. Parallel agent dispatching requires independent failures with no shared state. Failures sharing dependencies or context should be investigated together sequentially to avoid conflicting diagnoses and fixes.