sp-dispatching-parallel-agents

name	sp-dispatching-parallel-agents
description	Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies. Decision framework for when and how to dispatch parallel sub-agent sessions for faster resolution.
tags	["workflow","parallelism","agents","orchestration","efficiency"]
version	1.0.0
risk	safe
triggers	["dispatch parallel agents","run agents in parallel","parallelize tasks","multiple independent failures","work on multiple things at once","parallel investigation","split into parallel agents"]
requirements	{"cli":[],"env":[]}
upstream_ref	skills/dispatching-parallel-agents/SKILL.md
author	AlphaComposite
license	MIT
dependencies	[]

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

Multiple failures?
  └─ Yes → Are they independent?
              ├─ No (related) → Single agent investigates all
              └─ Yes → Can they work in parallel?
                          ├─ No (shared state) → Sequential agents
                          └─ Yes → Parallel dispatch ✓

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 (editing same files, shared resources)

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 sessions_spawn (OpenClaw sub-agents) to launch each agent concurrently:

Spawn agent 1: Fix agent-tool-abort.test.ts failures
Spawn agent 2: Fix batch-completion-behavior.test.ts failures
Spawn agent 3: Fix tool-approval-race-conditions.test.ts failures
# All three run concurrently

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?

Fix the 3 failing tests in src/agents/agent-tool-abort.test.ts:

1. "should abort tool with partial output capture" — expects 'interrupted at' in message
2. "should handle mixed completed and aborted tools" — fast tool aborted instead of completed
3. "should properly track pendingToolCount" — expects 3 results but gets 0

These are timing/race condition issues. Your task:

1. Read the test file and understand what each test verifies
2. Identify root cause — timing issues or actual bugs?
3. Fix by:
   - Replacing arbitrary timeouts with event-based waiting
   - Fixing bugs in abort implementation if found
   - Adjusting test expectations if testing changed behavior

Do NOT just increase timeouts — find the real issue.

Return: Summary of what you found and what you fixed.

Common Mistakes

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

❌ No context: "Fix the race condition" — agent doesn't know where
✅ Context: Paste the error messages and test names

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

❌ Vague output: "Fix it" — you don't know what changed
✅ 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)

Real Example

Scenario: 6 test failures across 3 files after major refactoring

Failures:

• agent-tool-abort.test.ts: 3 failures (timing issues)
• batch-completion-behavior.test.ts: 2 failures (tools not executing)
• tool-approval-race-conditions.test.ts: 1 failure (execution count = 0)

Decision: Independent domains — abort logic separate from batch completion separate from race conditions

Dispatch:

Agent 1 → Fix agent-tool-abort.test.ts
Agent 2 → Fix batch-completion-behavior.test.ts
Agent 3 → Fix tool-approval-race-conditions.test.ts

Results:

• Agent 1: Replaced timeouts with event-based waiting
• Agent 2: Fixed event structure bug (threadId in wrong place)
• Agent 3: Added wait for async tool execution to complete

Integration: All fixes independent, no conflicts, full suite green

Time saved: 3 problems solved in parallel vs. sequentially

Key Benefits

1. Parallelization — Multiple investigations happen simultaneously
2. Focus — Each agent has narrow scope, less context to track
3. Independence — Agents don't interfere with each other
4. Speed — 3 problems solved in time of 1

Verification

After agents return:

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