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 Parallelize vs Serialize

Parallelize when:

• •3+ tasks with different root causes
• •Multiple subsystems broken independently
• •Each problem can be understood without context from others
• •No shared state between investigations

Serialize when (sequential):

• •Failures are related (fix one might fix others)
• •Need to understand full system state
• •Agents would interfere with each other (editing same files, using same resources)
• •Exploratory debugging where you don't know what's broken yet

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 Instructions

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

# In Amplifier: use delegate() to dispatch concurrent agents
# Each agent gets its own clean context via context_depth="none"

delegate(
    agent="foundation:bug-hunter",
    instruction="Fix agent-tool-abort.test.ts failures. [full context here]",
    context_depth="none"
)
delegate(
    agent="foundation:bug-hunter",
    instruction="Fix batch-completion-behavior.test.ts failures. [full context here]",
    context_depth="none"
)
delegate(
    agent="foundation:bug-hunter",
    instruction="Fix tool-approval-race-conditions.test.ts failures. [full context here]",
    context_depth="none"
)
# All three run concurrently

Use context_depth="none" to give each agent a clean slate — no bleed-through of parent context. Include all necessary context in the instruction itself.

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 2

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

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

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

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

Bad: Vague output: "Fix it" — you don't know what changed Good: Specific: "Return summary of root cause and changes"

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