Sub Agents
Category: Emerging Concepts
Category: Emerging Concepts
Definition
Sub agents are specialized AI assistants that operate as independent components within a larger AI system, handling specific tasks with their own context windows, tools, and configurations. They enable more efficient problem-solving by allowing AI systems to delegate work and process multiple tasks in parallel.
How It Works
Sub agents function as autonomous units within a multi-agent architecture:
- Independent Context: Each sub agent maintains its own separate context window, preventing information pollution
- Task Specialization: Configured with specific system prompts and tools for their designated tasks
- Parallel Processing: Multiple sub agents can work simultaneously on different aspects of a problem
- Result Integration: A lead agent coordinates sub agents and synthesizes their outputs
- Tool Delegation: Sub agents can be granted access to specific tools and capabilities
In practice, sub agents are invoked when the main agent encounters tasks matching their expertise, working independently before returning results to the primary conversation.
Why It Matters
Sub agents represent a fundamental shift in how AI systems handle complex tasks:
Key Benefits:
- Context Preservation: Prevents main conversations from becoming cluttered with task details
- Efficiency Gains: 90.2% performance improvement over single-agent systems in Anthropic's tests
- Scalability: Enables handling of breadth-first queries requiring multiple investigation paths
- Specialization: Each sub agent can be fine-tuned for specific domains or tasks
- Modularity: Easy to add, modify, or remove capabilities without affecting the entire system
Real-World Impact:
- Developers can manage fleets of specialized AI assistants
- Complex tasks that would overwhelm a single agent become manageable
- Reduces time for debugging, research, and multi-step workflows
- Enables true task delegation rather than just advice
Implementation in Claude Code
Anthropic's Claude Code introduced sub agents in July 2025, making them accessible to developers:
Configuration:
---
name: code-reviewer
description: Expert code review specialist. Use proactively after code changes.
tools: Read, Grep, Glob, Bash
---
You are a senior code reviewer ensuring high standards of code quality and security.
Key Features:
- Storage Locations: User-level (
~/.claude/agents/) or project-level (.claude/agents/) - YAML Frontmatter: Simple markdown files define agent behavior
- Tool Access: Can inherit all tools or be restricted to specific ones
- MCP Integration: Access to Model Context Protocol server tools
- Interactive Management:
/agents command for creation and modification
Multi-Agent Research Systems
Anthropic's research system demonstrates advanced sub agent orchestration:
Architecture:
- Lead Agent: Analyzes queries and develops strategies
- Search Sub Agents: Explore different aspects simultaneously
- Citation Sub Agents: Verify and compile sources
- Memory Module: Combines results into comprehensive reports
- Token usage explains 80% of performance variance
- Number of tool calls and model choice are secondary factors
- Sub agents enable spending enough compute to solve complex problems
- Parallel processing dramatically reduces task completion time
Best Practices
When to Use Sub Agents:
- Complex tasks requiring multiple investigation paths
- Preventing context pollution in long conversations
- Tasks needing specialized knowledge or tools
- Parallel processing opportunities
- Test-driven development workflows
Design Principles:
- Start with Claude-generated agents, then customize
- Use descriptive names and clear task boundaries
- Include phrases like "use PROACTIVELY" for automatic invocation
- Separate concerns between different sub agents
- Consider independent verification through multiple agents
Future Directions
The evolution of sub agents points toward:
- Asynchronous Execution: Agents creating new sub agents dynamically
- Self-Improving Systems: Agents recognizing mistakes and updating themselves
- Collective Intelligence: Mimicking human society's distributed problem-solving
- Autonomous Workflows: Minimal human intervention for complex tasks
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