dispatching-parallel-agents

Dispatching Parallel Agents

Skill Overview

Dispatching parallel agents is a work pattern that assigns multiple independent tasks to different agents for simultaneous processing. Each agent has an isolated context and focuses on solving a specific problem domain, thereby significantly improving debugging and development efficiency.

Applicable Scenarios

1. Multiple Test Files Failing Simultaneously

When multiple test files fail for different reasons after a code refactor or major change, an independent agent can be assigned to investigate and fix each test file in parallel. For example, the timing issue in agent-tool-abort.test.ts, the tool execution issue in batch-completion-behavior.test.ts, and the race condition in tool-approval-race-conditions.test.ts are independent problems and are well suited for parallel handling.

2. Independent Failures in Multiple Subsystems

When independent failures occur in different system modules—such as the tool approval workflow, batch completion behavior, and abort functionality—each problem domain can be understood separately without requiring context from the others. In such cases, multiple agents can be dispatched to focus on their respective subsystems, avoiding the context switching and confusion that can occur when a single agent handles multiple issues.

3. Independent Debugging Without Shared State

When multiple debugging tasks have no shared state and will not interfere with one another—for example, when they involve different files or resources—agents can be safely dispatched in parallel. The key criterion is that fixing one problem will not affect the others, and that the agents will not conflict by editing the same files or resources.

Core Functions

1. Identifying Independent Problem Domains

Helps developers quickly identify which tasks can be handled in parallel. By determining whether failures are related, whether a complete system context is required, and whether shared state exists, complex multi-task scenarios can be divided into independent problem domains suitable for parallel processing, ensuring that each agent has a clearly defined scope of responsibility.

2. Precise Task Description Templates

Provides a structured approach to writing high-quality task prompts for agents. Each agent task should include a clear scope (one test file or subsystem), a specific goal (make the tests pass), constraints (do not modify unrelated code), and an expected output format (a summary of findings and fixes), avoiding tasks that are overly broad or lack context.

3. Parallel Dispatch and Result Integration

Supports launching multiple agent tasks in parallel in a Claude Code / AI environment and conducting unified review and integration after all agents have returned their results. This includes reading each agent’s fix summary, verifying that the fixes do not conflict, running the full test suite, and integrating all changes to ensure that multiple independent fixes work together properly.

Frequently Asked Questions

When should parallel agent dispatch be used?

Parallel agent dispatch is appropriate when there are three or more independent tasks, different test files are failing for different reasons, multiple subsystems have independent failures, each issue can be understood without additional context, and the tasks have no shared state. The core criterion is that fixing one problem will not affect the others.

How should effective task descriptions be written for parallel agents?

A good agent task description should be focused (covering one clearly defined problem domain), self-contained (providing all the context needed to understand the issue), and explicit about the expected output (specifying what the agent should return). Avoid overly broad instructions such as “fix all tests.” Instead, be specific, such as “fix the three failing tests in agent-tool-abort.test.ts,” and include the error messages and test names.

Can parallel agents interfere with one another?

Not when used correctly. The key is to ensure that the task domains are genuinely independent: the agents should work on different files, have no shared state, and make fixes that do not affect one another. Before dispatching, the task list should be reviewed to ensure that no agents will edit the same code or use the same resources. During the integration review, it is also necessary to check for conflicts between the agents’ fixes.