Dispatching parallel agents

Dispatching Parallel Agents

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

graph TD
    MultipleFailures{"Multiple failures?"}
    AreIndependent{"Are they independent?"}
    SingleAgent["Single agent investigates all"]
    OneAgent["One agent per problem domain"]
    CanParallel{"Can they work in parallel?"}
    Sequential["Sequential agents"]
    Parallel["Parallel dispatch"]

    MultipleFailures -->|yes| AreIndependent
    AreIndependent -->|"no - related"| SingleAgent
    AreIndependent -->|yes| CanParallel
    CanParallel -->|yes| Parallel
    CanParallel -->|"no - shared state"| Sequential

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

Send ALL agent calls in a single message. This is the only way they run concurrently. Making one Agent call, waiting, then another is sequential.

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

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"

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