When to Load
- Trigger: Multi-agent tasks, concurrent operations, spawning subagents, parallelizing independent work
- Skip: Single-step tasks or sequential workflows with no parallelization opportunity
Core Concept
Parallel execution spawns multiple subagents simultaneously using the Agent tool (named Task before Claude Code 2.1.63; Task still works as an alias). Subagents run in the background by default, so N tasks run concurrently, dramatically reducing total execution time.
Critical Rule: ALL Agent calls MUST be in a SINGLE assistant message for true parallelism. If the calls are in separate messages, they launch one after another.
Execution Protocol
Step 1: Identify Parallelizable Tasks
Before spawning, verify tasks are independent:
- No task depends on another's output
- Tasks target different files or concerns
- Can run simultaneously without conflicts
Step 2: Prepare Dynamic Subagent Prompts
Each subagent receives a custom prompt defining its role:
You are a [ROLE] specialist for this specific task.
Task: [CLEAR DESCRIPTION]
Context:
[RELEVANT CONTEXT ABOUT THE CODEBASE/PROJECT]
Files to work with:
[SPECIFIC FILES OR PATTERNS]
Output format:
[EXPECTED OUTPUT STRUCTURE]
Focus areas:
- [PRIORITY 1]
- [PRIORITY 2]
Step 3: Launch All Tasks in ONE Message
CRITICAL: Make ALL Agent calls in the SAME assistant message:
I'm launching N parallel subagents:
[Agent 1]
description: "Subagent A - [brief purpose]"
prompt: "[detailed instructions for subagent A]"
[Agent 2]
description: "Subagent B - [brief purpose]"
prompt: "[detailed instructions for subagent B]"
[Agent 3]
description: "Subagent C - [brief purpose]"
prompt: "[detailed instructions for subagent C]"
On Claude Code versions that still run subagents in the foreground by default, add run_in_background: true to each call.
Step 4: Collect Results
Each subagent returns its final result to the parent conversation automatically when it finishes. Wait until every subagent has reported before synthesizing; do not poll, and do not start dependent work early. (The separate TaskOutput call is deprecated.)
Step 5: Synthesize Results
Combine all subagent outputs into unified result:
- Merge related findings
- Resolve conflicts between recommendations
- Prioritize by severity/importance
- Create actionable summary
Dynamic Subagent Patterns
Pattern 1: Task-Based Parallelization
When you have N tasks to implement, spawn N subagents:
Plan:
1. Implement auth module
2. Create API endpoints
3. Add database schema
4. Write unit tests
5. Update documentation
Wave 1 - spawn 3 subagents (independent of each other):
- Subagent 1: Implements auth module
- Subagent 2: Creates API endpoints
- Subagent 3: Adds database schema
Wave 2 - after wave 1 has finished (these depend on its output):
- Subagent 4: Writes unit tests
- Subagent 5: Updates documentation
Pattern 2: Directory-Based Parallelization
Analyze multiple directories simultaneously:
Directories: src/auth, src/api, src/db
Spawn 3 subagents:
- Subagent 1: Analyzes src/auth
- Subagent 2: Analyzes src/api
- Subagent 3: Analyzes src/db
Pattern 3: Perspective-Based Parallelization
Review from multiple angles simultaneously:
Perspectives: Security, Performance, Testing, Architecture
Spawn 4 subagents:
- Subagent 1: Security review
- Subagent 2: Performance analysis
- Subagent 3: Test coverage review
- Subagent 4: Architecture assessment
Task List Integration
When using parallel execution, task tracking (TaskCreate/TaskUpdate, or TodoWrite on older versions) differs:
Sequential execution: Only ONE task in_progress at a time
Parallel execution: MULTIPLE tasks can be in_progress simultaneously
# Before launching parallel tasks
todos = [
{ content: "Task A", status: "in_progress" },
{ content: "Task B", status: "in_progress" },
{ content: "Task C", status: "in_progress" },
{ content: "Synthesize results", status: "pending" }
]
# As each subagent reports back, mark its task completed
todos = [
{ content: "Task A", status: "completed" },
{ content: "Task B", status: "completed" },
{ content: "Task C", status: "completed" },
{ content: "Synthesize results", status: "in_progress" }
]
When to Use Parallel Execution
Good candidates:
- Multiple independent analyses (code review, security, tests)
- Multi-file processing where files are independent
- Exploratory tasks with different perspectives
- Verification tasks with different checks
- Feature implementation with independent components
Avoid parallelization when:
- Tasks have dependencies (Task B needs Task A's output)
- Sequential workflows are required (commit -> push -> PR)
- Tasks modify the same files (risk of conflicts)
- Order matters for correctness
Performance Benefits
| Approach | 5 Tasks @ 30s each | Total Time |
|---|---|---|
| Sequential | 30s + 30s + 30s + 30s + 30s | ~150s |
| Parallel | All 5 run simultaneously | ~30s |
Parallel execution is approximately Nx faster where N is the number of independent tasks.
Example: Feature Implementation
User request: "Implement user authentication with login, registration, and password reset"
Orchestrator creates plan:
- Implement login endpoint
- Implement registration endpoint
- Implement password reset endpoint
- Add authentication middleware
- Write integration tests
Parallel execution:
Wave 1 - launching 4 subagents in parallel:
[Agent 1] Login endpoint implementation
[Agent 2] Registration endpoint implementation
[Agent 3] Password reset endpoint implementation
[Agent 4] Auth middleware implementation
[Results arrive as each subagent finishes]
Wave 2 - depends on wave 1:
[Agent 5] Integration test writing
[Synthesize into cohesive implementation]
Troubleshooting
Tasks running sequentially?
- Verify ALL Agent calls are in a SINGLE message
- On older Claude Code versions, check
run_in_background: trueis set for each
Results not available?
- Results are delivered when each subagent finishes; wait for all of them
- A subagent that was denied a permission may return without finishing its work; check its report
Conflicts in output?
- Ensure tasks don't modify same files
- Add conflict resolution in synthesis step