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

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

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

CRITICAL: All Task calls must be in a single message to run in parallel.

// Use the Task tool with these parameters:

Task 1:
  description: "Fix abort test failures"
  prompt: "Fix the 3 failing tests in agent-tool-abort.test.ts..."
  subagent_type: "general-purpose"

Task 2:
  description: "Fix batch completion failures"
  prompt: "Fix the 2 failing tests in batch-completion-behavior.test.ts..."
  subagent_type: "general-purpose"

Task 3:
  description: "Fix race condition failures"
  prompt: "Fix the failing test in tool-approval-race-conditions.test.ts..."
  subagent_type: "general-purpose"

// All three in ONE message = parallel execution

Available subagent_type options:

• general-purpose – For most tasks (searching, coding, multi-step work)
• Bash – For command execution tasks
• Explore – For codebase exploration (specify thoroughness: “quick”, “medium”, “very thorough”)
• Plan – For designing implementation plans
• Custom agents from agents/ directory (e.g., your defined agents)

Using Explore for parallel codebase analysis:

Task 1:
  description: "Find auth implementation"
  prompt: "Find how authentication is implemented. Thoroughness: medium"
  subagent_type: "Explore"

Task 2:
  description: "Find API endpoints"
  prompt: "Find all API endpoint definitions. Thoroughness: quick"
  subagent_type: "Explore"

Task 3:
  description: "Find database models"
  prompt: "Find all database model definitions. Thoroughness: medium"
  subagent_type: "Explore"

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 from Session

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

Advanced Task Tool Features

Background Execution

Run agents in background while you continue working:

Task:
  description: "Run slow analysis"
  prompt: "Analyze the entire codebase for..."
  subagent_type: "general-purpose"
  run_in_background: true

// Returns immediately with output_file path
// Use Read tool or `tail` to check progress later

Agent Resumption

Resume a previous agent to continue its work:

Task:
  description: "Continue previous analysis"
  prompt: "Continue from where you left off..."
  subagent_type: "general-purpose"
  resume: "<agent-id-from-previous-run>"

// Agent continues with full previous context preserved

Model Selection

Choose appropriate model for task complexity:

Task:
  description: "Quick formatting check"
  prompt: "Check if files follow naming convention..."
  subagent_type: "general-purpose"
  model: "haiku"  // Fast, low-cost for simple tasks

Task:
  description: "Complex architecture analysis"
  prompt: "Design the migration strategy..."
  subagent_type: "general-purpose"
  model: "opus"  // Most capable for complex reasoning

Using Custom Agents

Define agents in agents/ directory, then use them:

Task:
  description: "Security review"
  prompt: "[plan content]"
  subagent_type: "security-reviewer"  // From agents/security-reviewer.md

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

Real-World Impact

From debugging session (2025-10-03):

• 6 failures across 3 files
• 3 agents dispatched in parallel
• All investigations completed concurrently
• All fixes integrated successfully
• Zero conflicts between agent changes