Claude Subagents is a multi-agent architecture feature in Claude Code that enables distributed AI processing by delegating complex tasks to specialized worker agents operating in parallel with isolated context windows. This orchestrator-worker pattern overcomes the fundamental limitation of single-agent context window constraints, achieving 90%+ performance improvement on complex tasks.

By breaking large problems into discrete subtasks handled by multiple subagents simultaneously, Claude Code can perform comprehensive code reviews, deep research, and multi-dimensional analysis that would be impossible with traditional single-threaded approaches.

Key Features

1. Orchestrator-Worker Architecture

Lead Agent (orchestrator) performs strategic planning and task decomposition, spawning multiple Subagents (workers) that execute in parallel with independent 256K context windows. Results are compressed and aggregated, preventing context pollution while enabling massive parallel processing capacity.

2. Specialized Subagent Types

Different subagent types optimize for specific tasks: Explore for codebase research, Plan for strategic design, general-purpose for flexible operations, and claude-code-guide for documentation queries. Each type has tailored capabilities and tool access for maximum efficiency.

3. Background Execution

Subagents run non-blocking in background with run_in_background parameter, allowing Lead Agent to continue work while subagents process. Multiple subagents execute simultaneously, dramatically reducing total completion time for complex operations.

4. Context Isolation

Each subagent operates with isolated 256K context window, processing detailed information without polluting Lead Agent's context. Subagents return compressed summaries (hundreds of tokens) instead of full transcripts (thousands), maintaining clean context for synthesis.

5. Result Aggregation

TaskOutput tool retrieves completed subagent results, SubagentStop hooks trigger on completion for validation, and Lead Agent synthesizes findings into coherent final output - combining distributed processing with unified user experience.

6. Parallel Processing Capability

Launch multiple subagents simultaneously for independent subtasks: security scan + performance audit + test coverage + style check running in parallel instead of sequentially. Horizontal scaling enables handling problems previously impossible with single-agent constraints.

Use Cases

Who Should Use Subagents?

Enterprise Development Teams: Comprehensive code reviews across large codebases that exceed single context windows
Security Auditors: Multi-dimensional security analysis requiring parallel vulnerability scanning, dependency checks, and compliance validation
ML/AI Engineers: Complex experiment pipelines with multiple configuration permutations tested simultaneously
DevOps Professionals: Infrastructure audits analyzing dozens of services, configurations, and deployment pipelines in parallel
Technical Researchers: Deep investigations requiring synthesis from multiple codebases, documentation sources, and repositories

Problems It Solves

Context Window Limitations: Single-agent approaches hit 256K token limits on large tasks - subagents provide effectively unlimited capacity through parallel context windows
Sequential Bottlenecks: Traditional workflows process files one-by-one - parallel subagents reduce completion time from minutes to seconds
Context Pollution: Intermediate work details clutter main conversation - subagents return only essential findings, maintaining clean synthesis context
Comprehensive Analysis: Impossible to review 50+ files thoroughly single-threaded - multiple subagents each analyze portions and aggregate complete coverage
Time-Critical Operations: Code reviews before deployments can't wait for sequential processing - parallel execution provides immediate comprehensive results

Subagent Types and Capabilities

Explore Subagent

Purpose: Deep codebase exploration and architectural research

Capabilities:

Systematic file reading across large codebases
Relationship and dependency mapping
Architecture comprehension
Compressed summary generation

Example Use Case: User: "Research authentication implementation" Explore Subagent: Reads 20+ files, traces flow Returns: "Uses JWT + OAuth2, core logic in auth/service.ts:120, token refresh issue at line 245, session validation vulnerability at line 892" Lead Agent Context: ~500 tokens vs 50K+ raw

Plan Subagent

Purpose: Strategic planning and multi-step workflow design

Capabilities:

Break complex problems into implementation steps
Architectural decision analysis
Roadmap creation
Trade-off evaluation

Example Use Case: User: "Plan migration from REST to GraphQL" Plan Subagent: Analyzes existing APIs, designs migration strategy, identifies risks Returns: Phased implementation plan with concrete steps

General-Purpose Subagent

Purpose: Flexible task delegation when specialization not required

Capabilities:

Full tool access (Read, Write, Edit, Bash, etc.)
Generic problem solving
Adaptable to diverse tasks
Default fallback option

Example Use Case: Delegate independent research, testing, or automation tasks that don't need specialized expertise.

claude-code-guide Subagent

Purpose: Documentation and best practices consultation

Capabilities:

Official Claude Code documentation access
Skills, Hooks, Plugins guidance
Claude Agent SDK expertise
Claude API usage patterns

Example Use Case: User: "How do I create custom Skills?" claude-code-guide: Retrieves official docs, provides step-by-step guidance with examples.

Lifecycle and Management

Launching Subagents

Via Task Tool:

Task(
    description="Analyze security vulnerabilities",
    prompt="Scan all auth-related files for security issues",
    subagent_type="Explore",
    run_in_background=True  # Non-blocking execution
)

Automatic Spawning: Lead Agent decides when to delegate based on task complexity, context limitations, or parallel processing benefits.

Monitoring and Control

TaskOutput Tool: Retrieve results from completed background subagents SubagentStop Hook: Execute validation scripts when subagent finishes Real-time Status: Conversation updates show subagent progress Error Handling: Failed subagents report issues to Lead Agent for recovery

Lifecycle Events

UserPromptSubmit: User initiates request
Lead Agent Decision: Determines delegation need
PreToolUse: Before subagent spawn
Subagent Execution: Isolated work with independent context
SubagentStop: Completion notification (Hook triggers)
PostToolUse: After results integrated
Stop: Overall session completion

Performance and Cost Considerations

Performance Improvements

Single-Agent Baseline:

Sequential processing: Files A → B → C → ... → Context overflow
Time: Minutes for large tasks
Completeness: Often incomplete due to limits

Multi-Agent Performance:

Parallel processing: Subagents 1-N simultaneously
Time: Seconds for complex tasks (90%+ improvement)
Completeness: Comprehensive coverage through horizontal scaling

Cost Analysis

Token Consumption: 15x compared to single-agent approach

Single-agent task: 1x baseline
Multi-agent equivalent: 15x tokens
But enables 90%+ performance gain

When Cost Justifies:

✅ High-value enterprise decisions
✅ Critical security audits
✅ Deep technical research
✅ Time-sensitive comprehensive reviews
✅ Problems impossible single-threaded

When to Avoid:

❌ Simple file edits
❌ Code completion
❌ Daily conversations
❌ Budget-constrained operations
❌ Sequential dependent workflows

Effective Context Capacity

Single-Agent: 256K theoretical, <256K practical (context rot) Multi-Agent: 256K × N subagents effectively Lead Agent: Maintains clean context through compressed summaries

Integration with Claude Code Ecosystem

Skills Integration:

Skills provide knowledge to both Lead Agent and Subagents
Subagents execute using Skills' expertise
Combined: Intelligent distributed processing

Hooks Integration:

SubagentStop hook validates subagent completion
PreToolUse/PostToolUse hooks monitor delegation
Enforcement applies to both Lead Agent and Subagents

Plugins Integration:

Plugins bundle Skills used by subagents
MCP servers provide external tool access
Complete workflows leverage multi-agent architecture

MCP Protocol:

Each subagent can access MCP servers independently
Parallel external tool queries (GitHub + JIRA + Slack simultaneously)
Future: Cross-tool subagent sharing through open standard

Real-World Success Stories

Sionic AI - ML Training Pipeline

Running 1000+ daily experiments across GPU clusters, Sionic AI uses subagents to:

Select hardware configurations (NVIDIA A100 x8 clusters)
Set framework parameters (ms-swift + vLLM + DeepSpeed)
Avoid 20+ documented failure paths automatically

Result: Multiplied experimental efficiency through parallel configuration validation.

Enterprise Code Review

Fortune 500 company reviewing microservices codebase (50+ services):

Security subagent: Vulnerability scan → 12 critical findings
Performance subagent: Bottleneck analysis → 8 optimization opportunities
Test subagent: Coverage report → 78% average
Style subagent: Compliance check → 45 violations

Completed in 30 seconds vs 10+ minutes sequentially.

Deep Research (Anthropic Product)

Claude's Deep Research feature uses subagents to:

Query multiple documentation sources simultaneously
Aggregate findings from different codebases
Synthesize comprehensive technical reports
Compress thousands of tokens to actionable summaries

Best Practices

Task Decomposition Strategy

Clear Boundaries: Define non-overlapping subagent responsibilities
Parallel Thinking: Identify independent subtasks explicitly
Success Criteria: Specify expected outputs upfront
Tool Permissions: Apply principle of least privilege per subagent
Error Recovery: Plan for subagent failures and retry strategies

Context Management

Lead Agent Responsibility:

High-level strategy and synthesis
Minimal token consumption
Quality control over subagent outputs
User communication

Subagent Responsibility:

Tactical execution and detail work
Comprehensive analysis in isolated context
Return only critical findings (compression)
Tool-heavy operations

Cost Optimization

Enable Multi-Agent When:

Value of result >> 15x cost
Time savings justify expense
Complex analysis impossible single-threaded
Parallel execution provides critical advantage

Optimize Through:

Precise subagent scoping (avoid overlap)
Result compression (return essentials only)
Selective delegation (not every task needs subagents)
Parallel design (maximize concurrent execution)

Frequently Asked Questions

When should I use subagents vs direct tool calls?

Use subagents for complex tasks exceeding single context window, parallel processing benefits, or when intermediate details would clutter main conversation. Use direct tool calls for simple operations, sequential workflows, or cost-sensitive tasks.

No, each subagent has isolated context. This prevents pollution but requires explicit information passing. Lead Agent provides instructions, subagent returns compressed findings.

Can subagents spawn their own subagents?

Based on current architecture, subagents are workers executing tasks. Hierarchical spawning would introduce complexity but isn't standard pattern. Orchestrator-worker remains primary model.

How many subagents can run in parallel?

No documented hard limit, but practical constraints include cost (15x per subagent), task decomposition granularity, and diminishing returns. Typical: 3-6 subagents for comprehensive tasks.

Do subagents have access to all tools?

Depends on subagent type. Explore has read/search focus, general-purpose has full toolset. Access can be configured for security and efficiency.

How do I debug failed subagents?

SubagentStop hooks capture completion events, TaskOutput shows results/errors, and Lead Agent reports subagent failures. Logs provide execution details for troubleshooting.

Alternatives

If multi-agent architecture isn't suitable:

Single-Agent Sequential Processing: Traditional approach for simple tasks, lower cost
External Orchestration: Tools like LangChain, AutoGPT for custom multi-agent patterns
Manual Decomposition: Break tasks yourself, submit separately to Claude
Competitor Multi-Agent Tools: Emerging alternatives in AI development space

Conclusion

Claude Subagents represents a fundamental architectural advancement in AI-assisted development, applying distributed systems principles to overcome single-agent limitations. The orchestrator-worker pattern enables handling previously impossible tasks through intelligent decomposition, parallel execution, and result compression.

While the 15x token cost is significant, the 90%+ performance improvement and ability to process effectively unlimited information through parallel context windows make subagents essential for high-value, complex operations. Integration with Skills, Hooks, and MCP creates a complete programmable multi-agent platform where control, scale, and intelligence combine to solve problems that single-threaded approaches simply cannot handle.

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Claude Subagents