Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

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 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 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? 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: 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 Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

dispatching-parallel-agents