dispatching-parallel-agents

name	dispatching-parallel-agents
source	adapted from obra/superpowers (MIT License)
trigger	auto+manual
version	1.0.0
description	Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies. Chat triggers: 'parallel agents', 'run in parallel', 'split this into tasks', 'do these at the same time', 'concurrent work', 'spawn multiple agents', 'independent tasks', 'run multiple things at once', 'split work', 'parallelize this', 'multiple subagents', 'fan out', 'distribute work', 'parallel execution'.

Dispatching Parallel Agents

Overview

Delegate tasks to specialized agents with isolated context. By precisely crafting their instructions and context, ensure they stay focused and succeed at their task. They should never inherit the session's context or history — construct exactly what they need. This also preserves context for coordination work.

When multiple independent problems exist (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 was found and fixed

3. Dispatch in Parallel

Agent 1 → Fix test_agent_tool_abort.py failures
Agent 2 → Fix test_batch_completion.py failures
Agent 3 → Fix test_tool_approval_race.py 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 tests/test_agent_tool_abort.py:

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 pending_tool_count" - 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 was found and what was fixed.

Common Mistakes

❌ Too broad: "Fix all the tests" - agent gets lost ✅ Specific: "Fix test_agent_tool_abort.py" - 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: 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

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