Claude Code Agents: Parallel Workflows That Actually Work (2026 Guide)

One agent is good. Multiple agents working in parallel is a game-changer.

Claude Code's subagent system lets you spin up specialized AI workers that handle independent tasks simultaneously. While your main agent builds the frontend, a subagent can scaffold the backend. While one reviews code, another writes tests.

This is parallel development that actually works.

//What Are Subagents?

Subagents are lightweight instances of Claude Code that run inside tasks. Each subagent has:

• Its own context window. Isolated from the main agent.
• Specific instructions. Tuned for a particular task type.
• Limited tool access. Only the tools it needs.

The main agent acts as an orchestrator. It delegates work to subagents, collects results, and synthesizes everything into a coherent output.

Think of it like a senior engineer managing a team. You don't do everything yourself—you delegate to specialists.

//Why Parallel Agents?

Speed. Independent tasks run simultaneously. What would take 30 minutes sequentially finishes in 10.

Context management. A single agent working on a complex project exhausts its context window and starts losing details. Subagents each get fresh context, maintaining quality throughout.

Specialization. A "code reviewer" agent can be tuned differently than a "test writer" agent. Each excels at its specific job.

Scale. Need to document 50 files? Spin up subagents for each. Need to refactor 20 components? Parallelize it.

//The Task Tool

Claude Code's Task Tool is how you invoke subagents. You can use it naturally:

Run three tasks in parallel:
1. Analyze the auth module and document its flow
2. Find all API endpoints and list their parameters
3. Identify unused dependencies in package.json

Or be explicit:

Use the Task tool to spawn a subagent for each of these:
- Research how caching is currently implemented
- Draft a migration plan for the new database schema

The main agent delegates, subagents execute, and results flow back to the orchestrator.

//Creating Custom Agents

You can define specialized agents for your workflows. Create an agent file in .claude/agents/ or ~/.claude/agents/.

Example: code-reviewer.md

# Code Reviewer Agent

You are a specialized code review agent. Your job is to:

1. Analyze code for bugs, security issues, and maintainability
2. Check for adherence to project conventions
3. Suggest specific improvements with code examples
4. Flag any potential performance issues

Focus on actionable feedback. Don't just point out problems—suggest solutions.

## Tools Available
- Read (read files)
- Grep (search code)
- Glob (find files)

## Output Format
Provide feedback in this structure:
- **Critical Issues** (must fix)
- **Suggestions** (should consider)
- **Nitpicks** (minor improvements)

Example: test-writer.md

# Test Writer Agent

You are a specialized testing agent. Your job is to:

1. Analyze the code being tested
2. Identify edge cases and failure modes
3. Write comprehensive test cases
4. Ensure good coverage of happy paths and error paths

Use the project's existing test patterns and frameworks.

## Tools Available
- Read (read files)
- Write (create test files)
- Bash (run tests)

## Conventions
- Place tests in `__tests__/` directories
- Name test files `*.test.ts`
- Use descriptive test names

Once created, invoke them by name:

Use the code-reviewer agent to review src/auth/
Then use the test-writer agent to add tests for any untested functions

//Practical Patterns

Pattern 1: Parallel Exploration

Explore a large codebase fast:

Run these explorations in parallel:
1. Map out the database schema and relationships
2. Document the API routes and their handlers
3. Trace the authentication flow from login to session
4. List all external service integrations

Four subagents working simultaneously, each focused on one area.

Pattern 2: Batch Processing

Process many files at once:

For each file in src/components/:
- Spawn a subagent to add JSDoc comments
- Run these in parallel, max 5 at a time

Pattern 3: Pipeline Orchestration

Chain specialized agents:

1. First, use the code-analyzer agent to identify issues in src/api/
2. Then, for each issue found, spawn a fix-suggester agent
3. Finally, use the code-reviewer agent to validate the suggestions

Pattern 4: Competitive Approaches

Get multiple solutions to choose from:

Run three agents in parallel, each proposing a different approach
to implementing the caching layer:
1. One using Redis
2. One using in-memory cache
3. One using edge caching

I'll review all three and pick the best approach.

//Parallelism & Limits

Claude Code caps parallelism at around 10 concurrent subagents. If you request more, they queue and execute in batches.

Run 20 documentation tasks in parallel

This runs 10, waits for completion, then runs the next 10.

For very large batch operations (50+ tasks), consider:

Run these in batches of 5 to avoid overwhelming the system

Smaller batches = faster individual completion = better progress visibility.

//Best Practices

Design single-purpose agents. Each agent should excel at one thing. "Code reviewer" not "code reviewer and test writer and documentation generator."

Give explicit instructions. Custom agent prompts should be specific about:

• What the agent does
• What tools it can use
• What format to output
• What conventions to follow

Orchestrate thoughtfully. Don't just parallelize everything. Some tasks depend on others. Plan your execution order:

Phase 1 (parallel): Research and analysis
Phase 2 (sequential): Design decisions based on phase 1
Phase 3 (parallel): Implementation tasks
Phase 4 (sequential): Integration and testing

Keep context minimal. Each subagent gets fresh context. Don't pass huge amounts of data—pass specific file paths or focused questions.

Handle failures gracefully. Subagents can fail. Your orchestration should expect this:

Run these tasks. If any fail, report the failure
but continue with the others.

//Real-World Example

Let's say you're adding a new API endpoint. Here's a parallel workflow:

Main agent prompt:

Add a POST /api/users/invite endpoint that sends invitation emails.

Orchestrate this with subagents:

1. Research agent: Find how existing email sending works in this codebase
2. Schema agent: Design the database changes needed (invitations table)
3. API agent: Draft the endpoint handler following existing patterns

Run 1, 2, 3 in parallel.

Then after collecting results:
4. Integration agent: Combine the outputs into a complete implementation
5. Test agent: Write tests for the new endpoint

Run 4, then 5.

What happens:

• Three subagents work simultaneously on research, schema, and API design
• Results flow back to the orchestrator
• The integration agent combines everything
• The test agent writes tests

Total time: Maybe 5 minutes instead of 15.

//The Bigger Picture

Subagents aren't just about speed. They're about tackling problems that would overwhelm a single context window.

Documenting an entire codebase. Refactoring 100 files. Migrating a large test suite. These become tractable when you can parallelize.

The Claude Agent SDK (formerly Claude Code SDK) makes all of this customizable. You can build specialized agent teams for your specific workflows—code review, deployment, documentation, whatever your team needs.

Start simple. One custom agent. See how it fits your workflow. Then expand.

Parallel development isn't the future. It's now.