dispatching-parallel-agents

name	dispatching-parallel-agents
description	Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
metadata	{"openclaw":{"emoji":"⚡"}}

Dispatching Parallel Agents

Overview

When you have multiple unrelated failures (different test files, different subsystems, different bugs), investigating them sequentially wastes time. Each investigation is independent and can happen in parallel.

Core principle: Dispatch one agent per independent problem domain. Let them work concurrently.

When to Use

Use when:

• 3+ test files failing with different root causes
• Multiple subsystems broken independently
• Each problem can be understood without context from others
• No shared state between investigations

Don't use when:

• Failures are related (fix one might fix others)
• Need to understand full system state
• Agents would interfere with each other

The Pattern

1. Identify Independent Domains

Group failures by what's broken:

• File A tests: Tool approval flow
• File B tests: Batch completion behavior
• File C tests: Abort functionality

Each domain is independent - fixing tool approval doesn't affect abort tests.

2. Create Focused Agent Tasks

Each agent gets:

• Specific scope: One test file or subsystem
• Clear goal: Make these tests pass
• Constraints: Don't change other code
• Expected output: Summary of what you found and fixed

3. Dispatch in Parallel

Use openclaw agent spawn to dispatch agents concurrently:

# Dispatch agents for independent problems
openclaw agent spawn --agent forge --task "Fix agent-tool-abort.test.ts failures"
openclaw agent spawn --agent forge --task "Fix batch-completion-behavior.test.ts failures"
openclaw agent spawn --agent forge --task "Fix tool-approval-race-conditions.test.ts failures"

Or in OpenClaw's orchestration context, dispatch via subagent mechanism.

4. Review and Integrate

When agents return:

• Read each summary
• Verify fixes don't conflict
• Run full test suite
• Integrate all changes

Agent Prompt Structure

Good agent prompts are:

1. Focused - One clear problem domain
2. Self-contained - All context needed to understand the problem
3. Specific about output - What should the agent return?

Common Mistakes

BAD: Too broad: "Fix all the tests" - agent gets lost GOOD: Specific: "Fix agent-tool-abort.test.ts" - focused scope

BAD: No context: "Fix the race condition" - agent doesn't know where GOOD: Context: Paste the error messages and test names

BAD: No constraints: Agent might refactor everything GOOD: Constraints: "Do NOT change production code" or "Fix tests only"

BAD: Vague output: "Fix it" - you don't know what changed GOOD: Specific: "Return summary of root cause and changes"

When NOT to Use

Related failures: Fixing one might fix others - investigate together first Need full context: Understanding requires seeing entire system Exploratory debugging: You don't know what's broken yet Shared state: Agents would interfere (editing same files, using same resources)

Verification

After agents return:

1. Review each summary - Understand what changed
2. Check for conflicts - Did agents edit same code?
3. Run full suite - Verify all fixes work together
4. Spot check - Agents can make systematic errors