Picture this: You’re debugging when Claude Code starts giving architecture advice instead of fixing your test failure. This context drift happens because general AI assistants try to be everything to everyone.
Claude Code subagents solve this by providing specialized AI assistants for specific development tasks. Each sub agent maintains its own context window and custom system prompt, staying focused on what it does best. A code reviewer subagent thinks differently than a debugger - each has specialized AI tailored for their role.
Table of Contents:
Claude Code subagents are specialized AI assistants that handle specific development tasks. Each sub agent operates with:
When Claude Code encounters a task matching the subagent’s expertise, it can automatically delegate work to the specialist, or you can explicitly invoke them. Each subagent operates independently with its specific tool access.
Traditional AI assistants suffer from “context pollution” - when conversations become unfocused, the AI loses track of what’s important. Claude agents solve this with clean, task-specific contexts.
This means:
Here’s a common scenario: You start a Claude Code session implementing an API endpoint. Two hours later, Claude’s context is polluted with database schemas, testing strategies, and deployment concerns. When you ask for code reviews, it might contradict earlier decisions.
Claude Code sub agents prevent this by maintaining clean, focused contexts for each task.
General-purpose AI assistants are like Swiss Army knives - they can do many things, but they’re not optimized for any specific task. You wouldn’t use a Swiss Army knife blade to perform surgery, and you shouldn’t use a general AI assistant for specialized development tasks that require deep technical knowledge.
Different development tasks require different thinking patterns:
A single system prompt can’t handle all these specialized AI approaches simultaneously.
Traditional code review tools flag syntax issues but miss architectural problems. A well-configured code reviewer subagent can:
You are a senior code reviewer focusing on maintainability and security.
When invoked:
1. Run git diff to see code changes
2. Analyze specific files for common issues
3. Check for security vulnerabilities
4. Verify adherence to team coding standards
This code reviewer subagent can automatically run after code changes when configured with “Use proactively” in its description, providing consistent code reviews that catch issues human reviewers might miss.
A test-runner sub agent can proactively run tests when it detects code changes, then automatically diagnose and fix test failures. By including “PROACTIVELY” in the subagent description, Claude Code will use this specialized AI without being asked, creating a feedback loop where tests are continuously validated.
Different types of bugs require different debugging approaches. You can create specialized debugging Claude agents for:
Each debugging specialist brings targeted specialized AI to problem-solving.
A performance-focused subagent can continuously monitor and optimize code, running performance audits automatically when configured to “Use proactively” and suggesting specific optimizations based on actual data insights.
The fastest way to create new subagents is through Claude Code’s interactive interface using custom slash commands:
/agents
This command in Claude Code opens a menu where you can:
Pro tip: Start by generating your initial sub agent with Claude, then customize it for your current project needs.
Claude Code stores subagent files in two locations:
.claude/agents/) - Available only in current project~/.claude/agents/) - Available across all projectsProject level subagents take precedence when names conflict, allowing project specific customization.
Claude Code subagents can work in two ways:
Automatic Delegation: Claude Code proactively delegates tasks based on:
description field in subagent configurationsManual Invocation: You explicitly request specific subagents
To enable automatic delegation, include phrases like “Use PROACTIVELY” or “MUST BE USED” in your subagent’s description field. This signals that Claude should use this subagent without being asked.
Claude Code can delegate work to Claude agents automatically based on context, or you can request specific subagents explicitly:
# Automatic delegation (when subagent description includes "PROACTIVELY")
> Fix these failing tests
> I just made some code changes
# Explicit invocation using slash commands
> Use the test-runner sub agent to fix failing tests
> Have the code reviewer subagent analyze my recent code changes
> Ask the debugging subagent to investigate this error
Let’s create a practical code-reviewer subagent that provides consistent, thorough code reviews.
Every effective subagent starts with a clear, focused purpose. For our code reviewer:
Our code reviewer needs specific tools:
Read - to examine code filesBash - to run git diff and see changesGrep - to search for patterns across the codebaseGlob - to find related filesLimiting tool access keeps the subagent focused and improves security.
---
name: code-reviewer
description: Expert code review specialist. Use proactively after writing or modifying code to ensure quality and maintainability.
tools: Read, Grep, Glob, Bash
---
You are a senior code reviewer with 10+ years of experience ensuring
high-quality, maintainable code.
When invoked:
1. Run `git diff` to identify recent changes
2. Focus your review on modified files only
3. Begin analysis immediately without asking for confirmation
Review priorities:
- CRITICAL: Security vulnerabilities, exposed secrets, SQL injection risks
- IMPORTANT: Code duplication, poor error handling, performance issues
- SUGGESTIONS: Naming improvements, code organization, documentation
For each issue found:
- Explain why it's problematic
- Provide a specific code example showing the fix
- Indicate the severity level (Critical/Important/Suggestion)
Always end with a summary of the overall code health.
After creating your subagent:
You can create workflows that use multiple subagents in sequence:
> Use the code-analyzer to find performance issues, then have the optimizer subagent implement the fixes
This creates a pipeline where each specialist contributes their expertise to solve complex problems.
Create different subagents for different environments or projects:
backend-reviewer - focuses on API design and database patternsfrontend-reviewer - specializes in React components and state managementsecurity-auditor - trained on your specific security requirementsConfigure subagents with only the tools they need. A documentation subagent might only need Read and Write, while a deployment subagent might need Bash and specific cloud CLI tools.
Share effective subagents across your team by checking them into version control:
# Project subagents in version control
.claude/
agents/
code-reviewer.md
test-runner.md
deploy-helper.md
This ensures consistent AI behavior across your entire development team.
Wrong approach: A “full-stack-helper” sub agent that handles frontend, backend, database, and deployment tasks.
Right approach: Separate Claude Code subagents for each area - frontend-specialist, api-designer, database-optimizer, deploy-manager.
Focused subagents perform better because they’re built for specific workflows with specialized AI patterns.
Wrong approach:
You are a helpful coding assistant. Help with various programming tasks.
Right approach:
You are a React performance specialist. When invoked, immediately
analyze React components for performance issues including:
- Unnecessary re-renders
- Missing memoization opportunities
- Inefficient state management patterns
- Bundle size impacts
Specific system prompt configurations create predictable, consistent behavior.
Giving Claude Code sub agents access to all available tools can lead to unfocused behavior. A documentation subagent doesn’t need Bash tool access, and a code reviewer subagent doesn’t need deployment tools.
Begin with basic Claude agents that handle common development workflow tasks, then gradually add sophistication:
Good subagent descriptions help Claude Code decide when to use them:
description: Use PROACTIVELY when tests fail. Automatically diagnoses
test failures and implements fixes while preserving test intent.
The word “PROACTIVELY” signals that Claude should use this subagent without being asked.
Track how your subagents perform:
Regular refinement makes subagents more effective over time.
Q: How many Claude Code subagents should I create? A: Start with 3-5 Claude agents covering your most common development workflow tasks (code reviews, testing, debugging). Add more new subagents as you identify specific workflows. Too many can create decision paralysis for Claude Code.
Q: Can subagents access MCP tools?
A: Yes, Claude Code sub agents can use MCP tools from configured MCP servers. When you leave blank the tools field, subagents inherit all available tools from the main conversation, including MCP tools.
Q: What happens when multiple subagents could handle the same task? A: Claude Code chooses based on the specificity and relevance of each subagent’s purpose description. More specific descriptions generally win. Project level subagents always take precedence over user-level subagents with the same variable names.
Q: Can I share subagents between team members?
A: Yes. Store subagent files in .claude/agents/ within your project repository and commit them to version control. This ensures your entire team uses the same specialized AI assistants for consistent development workflow.
Q: Do subagents remember context between invocations? A: No, each subagent invocation starts with a clean context window. This prevents context pollution and ensures consistent behavior. If you need persistent context, include relevant information in the custom system prompt.
Q: How do I debug a subagent that’s not working as expected? A: Use explicit invocation to test the sub agent directly: “Use the [subagent-name] to [specific task]“. Check the custom system prompt for clarity, ensure the specific tool permissions are appropriate, and refine the subagent’s purpose description to better match when you want it activated.
Q: Can I modify built-in subagents? A: Built-in subagents can’t be modified directly, but you can create your own subagents with the same names to override them. Project-level subagents take highest precedence, followed by user-level subagents, then built-ins.
Subagents represent a shift from general-purpose AI assistance to specialized AI expertise. Instead of having one AI assistant that’s mediocre at everything, you can build a team of AI specialists that excel in their areas.
The most successful development teams using Claude Code don’t just create subagents - they engineer their AI workflows. They identify repetitive tasks that require specific expertise, create focused subagents to handle those tasks, and continuously refine their AI assistants based on real-world performance.
Start small. Pick your most time-consuming development task - whether it’s code reviews, test debugging, or performance optimization - and create a specialized subagent for it. Watch how it transforms not just that specific task, but your entire development workflow.
Your code quality will improve. Your development velocity will increase. Most importantly, you’ll spend less time on repetitive tasks and more time building the features that matter.
The future of development productivity isn’t about replacing developers with AI - it’s about giving each developer a team of AI specialists that make them more effective at what they do best.
Subagents revolutionize how you build code, but what about deploying it? Traditional deployment still means wrestling with databases, APIs, authentication, and infrastructure setup - the same bottlenecks that slow development.
That’s exactly why we built Raindrop - the first Claude Native infrastructure platform that extends your specialized AI workflows from code to production.
Through our Model Context Protocol (MCP) integration, Raindrop connects directly to Claude Code, giving you access to production-grade AI building blocks like SmartMemory, SmartBuckets, SmartSQL, and auto-scaling infrastructure. Your subagents can now work with complete backend systems - databases, APIs, vector search, and global deployment - all through the same conversational interface.
Ready to see what specialized AI workflows can do beyond just coding?
Get started with Raindrop and experience infrastructure that works as smoothly as your subagents. From idea to deployed application in a single Claude Code session.
Setup takes less than 5 minutes. Plans start at $5/month.