dispatching-parallel-agents

name	dispatching-parallel-agents
description	Use when facing 2+ independent tasks (different test files, subsystems, bugs) that can be worked on without shared state or sequential dependencies. Decide when to split work into parallel agents vs keep it sequential.

Dispatching Parallel Agents

Harness-owned skill for the decision of when to split work across concurrent agents. For worktree mechanics and multi-instance scaling see parallelization; for per-agent context briefing see subagent-orchestration. Under ultracode, prefer the Workflow tool's parallel()/pipeline() (see CLAUDE.md → Ultracode Orchestration).

Overview

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

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

digraph when_to_use {
    "Multiple failures?" [shape=diamond];
    "Are they independent?" [shape=diamond];
    "Single agent investigates all" [shape=box];
    "One agent per problem domain" [shape=box];
    "Can they work in parallel?" [shape=diamond];
    "Sequential agents" [shape=box];
    "Parallel dispatch" [shape=box];

    "Multiple failures?" -> "Are they independent?" [label="yes"];
    "Are they independent?" -> "Single agent investigates all" [label="no - related"];
    "Are they independent?" -> "Can they work in parallel?" [label="yes"];
    "Can they work in parallel?" -> "Parallel dispatch" [label="yes"];
    "Can they work in parallel?" -> "Sequential agents" [label="no - shared state"];
}

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, same resources)

The Pattern

1. Identify Independent Domains

Group failures by what's broken. Each domain is independent — fixing one doesn't affect the others.

2. Create Focused Agent Tasks

Each agent gets:

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

3. Dispatch in Parallel

Issue all dispatches in the same response — they run concurrently. One dispatch per response = sequential.

Agent (general-purpose): "Fix agent-tool-abort.test.ts failures"
Agent (general-purpose): "Fix batch-completion-behavior.test.ts failures"
Agent (general-purpose): "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 (did agents edit the same code?)
• Run the 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 (paste the error messages and test names)
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)

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 — N problems solved in the time of one