oimiragieo/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

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

// In Claude Code / AI environment
Task('Fix agent-tool-abort.test.ts failures');
Task('Fix batch-completion-behavior.test.ts failures');
Task('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
• 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

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

X No context: "Fix the race condition" - agent doesn't know where V Context: Paste the error messages and test names

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

X Vague output: "Fix it" - you don't know what changed V 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

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

Iron Laws

1. NEVER parallelize tasks that share mutable state or write to the same files — concurrent writes cause race conditions, merge conflicts, and lost changes.
2. ALWAYS define forbidden paths and owned paths for each parallel agent before spawning — agents without explicit path ownership will step on each other.
3. ALWAYS synthesize all agent findings before implementing any changes — acting on partial parallel results before synthesis creates conflicting fixes.
4. NEVER spawn more parallel agents than there are truly independent investigation domains — forced parallelism on related issues creates coordination overhead without benefit.
5. ALWAYS verify zero conflicts exist between all agent changes before marking the parallel dispatch complete — unverified integration is not complete integration.

Anti-Patterns

| Anti-Pattern | Why It Fails | Correct Approach | | -------------------------------------------------------- | -------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | | Spawning parallel agents on the same files | Race condition; last write wins; one agent's fix silently overwrites another's | Define owned_paths per agent; ensure no overlap before spawning | | Acting on the first agent result without waiting for all | Creates partial, conflicting state before synthesis is done | Wait for all parallel agents to complete; synthesize before any implementation | | Parallelizing sequential dependencies | Agent B depends on Agent A's output; parallel execution causes B to work on stale data | Map dependencies first; only parallelize truly independent domains | | No conflict verification step after integration | Conflicting changes are silently accepted; system is left in invalid state | Always run a conflict-check pass after all parallel agents complete | | Using parallel dispatch for simple 2-step tasks | Parallelism overhead exceeds benefit for short tasks | Use parallel dispatch only when each investigation domain requires 3+ independent steps |

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

• New pattern -> .claude/context/memory/learnings.md
• Issue found -> .claude/context/memory/issues.md
• Decision made -> .claude/context/memory/decisions.md

ASSUME INTERRUPTION: If it's not in memory, it didn't happen.