dispatching-parallel-agents

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 (editing same files)

The Pattern

1. Identify Independent Domains

Group failures by what's broken:

• File A tests: Authentication flow
• File B tests: Data validation
• File C tests: API endpoints

Each domain is independent — fixing authentication doesn't affect API endpoint 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

# Using Claude Code's Task tool
Task("Fix test_auth.py failures")         # Agent 1
Task("Fix test_validation.py failures")   # Agent 2
Task("Fix test_api.py failures")          # Agent 3
# All three run concurrently

Alternative: For review tasks, consider using Agent Teams (TeamCreate) which provide built-in cross-validation and shared task lists.

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_auth.py:

1. "test_login_with_expired_token" - expects 401, gets 200
2. "test_refresh_token_rotation" - token not rotated after use
3. "test_concurrent_sessions" - expects 3 sessions, gets 0

These may be timing or state 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 addressing root cause (not just increasing timeouts)

Do NOT change tests in other files.

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

Common Mistakes

Too broad: "Fix all the tests" — agent gets lost Specific: "Fix tests/test_auth.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: 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 — 3 problems solved in time of 1