Claude Code Planning and Execution Workflows: From Built-in Modes to Parallel Agents

1 October 2025 • 12 min read • AI

Effective AI-assisted development requires separating planning from execution. Claude Code version 2 (2025) provides multiple approaches to planning workflows, from simple built-in modes to sophisticated parallel agent architectures that can handle complex, multi-repository projects.

Built-in Plan Mode: The Foundation

Plan Mode is Claude Code's core feature for safe, read-only code analysis. It creates a deliberate boundary between research and execution, preventing accidental changes while exploring codebases.

Activating Plan Mode

The fastest way to enter Plan Mode is with Shift+Tab. This keyboard shortcut cycles through permission modes:

Normal Mode: Standard tool permissions
Auto-Accept Mode: Claude executes tools without prompts (Shift+Tab once)
Plan Mode: Read-only analysis only (Shift+Tab twice)

When active, you'll see ⏸ plan mode on at the terminal bottom.

Alternative Activation Methods

Start a new session directly in Plan Mode:

claude --permission-mode plan

Run headless planning queries without interactive sessions:

claude --permission-mode plan -p "Analyze the authentication system architecture"

Setting Plan Mode as Default

Configure Claude Code to always start in Plan Mode by editing .claude/settings.json:

{
  "permissions": {
    "defaultMode": "plan"
  }
}

When to Use Built-in Plan Mode

Plan Mode excels at:

Exploring unfamiliar codebases: Understanding architecture before making changes
Multi-step implementation planning: Breaking down complex features into actionable steps
Code review and analysis: Examining code without modification risk
Security audits: Analyzing code without executing it
Interactive refinement: Iterating on plans before execution

Limitations of Built-in Plan Mode

While powerful, built-in Plan Mode has constraints:

No persistent artifacts: Plans exist only in the conversation
Single-threaded: Cannot parallelize research across multiple areas
Context mixing: Planning and execution share the same context window
No structured tracking: No standardized format for progress tracking

These limitations become apparent in complex projects that require formal planning, team collaboration, or parallel workstreams.

Formal Planning Workflows: CLAUDE/plan Structure

For complex tasks requiring structured planning, persistent documentation, and team collaboration, a formal planning workflow provides significant advantages over built-in Plan Mode.

Directory Structure

Formal planning workflows use a standardized directory structure:

project/
├── CLAUDE.md                 # Main project instructions
├── CLAUDE/
│   ├── PlanWorkflow.md      # Workflow documentation
│   ├── Core/                # Core documentation
│   │   ├── CodeStandards.md
│   │   └── TestingStandards.md
│   ├── Tools/               # Tool-specific docs
│   │   └── PHPStan.md
│   └── plan/                # Individual plan files
│       ├── feature-auth-system.md
│       ├── refactor-database-layer.md
│       └── archive/         # Completed plans

The Two-Mode Workflow

Formal workflows enforce strict separation between planning and execution:

Planning Mode (Default)

NO CODE CHANGES are permitted. Planning mode focuses on:

Full research of relevant files, database tables, and dependencies
Terse but detailed plan of required actions
Code snippets for particularly relevant implementations
Verification against project documentation and standards
Creation of structured TODO lists with progress tracking

Execution Mode

Triggered only by explicit instruction (e.g., "execute plan", "proceed with implementation"). Execution mode:

Works through plan tasks systematically
Updates progress tracking as tasks complete
Runs quality tools (linters, static analysis) continuously
Resolves issues before moving to next task
Marks plan as "ALL DONE!" only when complete and validated

Documenting Your Plan Workflow

Every project using formal planning should document its workflow in CLAUDE/PlanWorkflow.md. This serves as the contract between developers and Claude Code, defining how planning and execution work in your project.

Basic PlanWorkflow.md Template

Start with the fundamental two-mode structure:

# Task Planning and Execution

## Two Modes

We are always in 1 of 2 Modes.
You are in planning mode by default. Execution mode only when made absolutely explicit.

### Planning Mode

NO CODE CHANGES

We create/update a plan file which should be stored in CLAUDE/plan/(task-name).md

When we are planning a task, we need to do the following:

* full research of all relevant files/database tables etc
* terse but detailed plan of required actions
* code snippets for particularly relevant items
* check project documentation in CLAUDE folder for anything relevant
* create a simple TODO list at the top of the file
* in planning mode, NO CODE CHANGES
* Make sure you have read
  * @CLAUDE/Core/CodeStandards.md
  * @CLAUDE/Core/TestingStandards.md

## Execution Mode

Only triggered once given explicit instruction to execute/proceed/implement the plan

When we are executing a plan:
* once plan is approved, we are in execution mode
* Make sure you have read the relevant @CLAUDE/plan/(task-name).md plan file
* Work through the list in the Progress section
* update Progress as we go
* Make sure tools are being run and issues resolved as we go
* Once plan is complete, add ALL DONE

## Task Status Tracking

The following symbols MUST be used to indicate task status:
* `[ ]` - Task not started
* `[⏳]` - Task in progress (currently being worked on)
* `[✓]` - Task completed 100%

Advanced: Parallel Execution Workflows

For projects leveraging parallel agents, extend your PlanWorkflow.md with parallel execution structure. This enables sophisticated multi-agent orchestration.

Parallel Execution Plan Structure

## Parallel Execution Plan Structure

Plans should be optimized for parallel agent execution with clear dependency chains.

### Key Principles

1. **Identify Independent Work**: Break tasks into components that can be developed in parallel
2. **Define Clear Interfaces**: Create contracts/types upfront that parallel work depends on
3. **Minimize Blocking Phases**: Reduce sequential dependencies to maximize parallelism
4. **Clear Dependency Notation**: Use BLOCKING/NON-BLOCKING labels explicitly

### Plan Structure for Parallel Execution

```markdown
## Implementation Phases - Parallel Execution Plan

### Execution Overview
```
Phase 1: Foundation (BLOCKING - Must complete first)
├── Agent A: Core contracts/interfaces
└── Agent B: Shared configurations

Phase 2: Parallel Development (NON-BLOCKING - All can run simultaneously)
├── Agent C: Component 1
├── Agent D: Component 2
├── Agent E: Component 3
└── Agent F: Component 4

Phase 3: Integration (BLOCKING - Requires Phase 2)
└── Agent G: Assemble components

Phase 4: Deployment (BLOCKING - Requires Phase 3)
└── Agent H: Deploy to production
```
```

### Phase Definitions

Each phase should include:

```markdown
### Phase N: [Name] [BLOCKING|NON-BLOCKING]
**Dependencies**: What must complete before this phase starts
**Can run in parallel with**: Other phases that can execute simultaneously

#### Agent X: [Task Name]
**Dependencies**: Specific prerequisites (e.g., "Phase 1 types only")
**Output Required**: Exact deliverables
```
[List of files/packages to create]
```
**Libraries/Tools**: External dependencies needed
**Can work independently**: Yes/No
```

### Dependency Types

**BLOCKING Phase**: All agents in prior phases must complete before this phase begins
**NON-BLOCKING Phase**: Agents can work simultaneously once dependencies are met
**Agent Dependencies**: Specific outputs from other agents required

Parallel Execution Matrix

Document the visual matrix pattern in your workflow guide:

### Parallel Execution Matrix

Include a visual matrix showing agent execution flow:

```markdown
## Parallel Execution Matrix

| Phase | Agent A | Agent B | Agent C | Agent D | Agent E |
|-------|---------|---------|---------|---------|---------|
| **1** | Types   | Config  | BLOCKED | BLOCKED | BLOCKED |
| **2** | ✅ Done | ✅ Done | Work    | Work    | Work    |
| **3** | -       | -       | ✅ Done | ✅ Done | ✅ Done |
```

## Critical Dependencies

### Hard Blocks (Must Complete)
1. **Phase 1 → Phase 2**: Foundation required by all components
2. **Phase 2 → Phase 3**: All components needed for integration

### Soft Dependencies (Can Overlap)
- Agent X can start scaffolding while Y completes
- Agent Z can prepare test data during integration

Agent Communication Protocol

Define how parallel agents track and communicate progress:

### Communication Protocol

For parallel agents to track progress:

```yaml
# Each agent maintains status
agent_[x]_status:
  status: "in_progress|completed|blocked"
  progress: "Description of current work"
  blockers: ["Any blocking issues"]
  completed_files: ["List of completed deliverables"]
```

Optimization Strategies

### Optimization Strategies

1. **Front-load Interfaces**: Define all contracts/types in Phase 1
2. **Mock Dependencies**: Allow parallel work using interface mocks
3. **Feature Branches**: Each parallel agent works in isolation
4. **Atomic Deliverables**: Each agent produces complete, testable units

Anti-Patterns to Document

Help Claude Code avoid common mistakes by documenting anti-patterns:

## Anti-Patterns to Avoid

1. **Hidden Dependencies**: Agent D secretly needs output from Agent C
2. **Overly Sequential**: Making phases blocking when they could run in parallel
3. **Missing Interfaces**: Starting parallel work without clear contracts
4. **Vague Deliverables**: "Implement feature" instead of specific file outputs
5. **No Status Tracking**: Parallel agents with no progress visibility

Plan Document Structure

Individual plan documents follow a standardized format:

# Feature Implementation Plan

Ensure all the following have been read:
- @CLAUDE/PlanWorkflow.md
- @CLAUDE/Core/CodeStandards.md
- @CLAUDE/Core/TestingStandards.md

## Progress

[ ] Research existing implementation
[⏳] Design new architecture
[✓] Write unit tests
[ ] Implement feature
[ ] Run static analysis
[ ] Update documentation

## Summary

Brief description of the task and goals.

## Details

### Current State

Analysis of existing code and architecture...

### Proposed Solution

Detailed implementation approach...

### Testing Strategy

Test coverage and validation approach...

Parallel Execution Plan Example

For complex features requiring parallel execution, structure plans like this:

## Implementation Phases - Parallel Execution Plan

### Execution Overview
```
Phase 1: Foundation [BLOCKING]
├── Agent A: API Contracts
└── Agent B: Database Schema

Phase 2: Implementation [NON-BLOCKING]
├── Agent C: User Service
├── Agent D: Auth Service
├── Agent E: API Routes
└── Agent F: Frontend Client

Phase 3: Integration [BLOCKING]
└── Agent G: End-to-end tests
```

### Phase 1: Foundation [BLOCKING]
**Why blocking**: All subsequent work depends on these interfaces

#### Agent A: API Contracts
**Output**: `api/contracts.ts` defining all interfaces
**No dependencies**: Can start immediately
**Deliverables**:
- User interface
- Auth interface
- Response types

#### Agent B: Database Schema
**Output**: `schema.sql` with all tables
**No dependencies**: Can start immediately
**Deliverables**:
- Users table
- Sessions table
- Migrations

### Phase 2: Implementation [NON-BLOCKING]
**Dependencies**: Phase 1 complete
**Parallel execution**: 4 agents can work simultaneously

#### Agent C through F: Various Components
**Dependencies**: Phase 1 interfaces only
**Can work independently**: Yes - each implements against interfaces

## Parallel Execution Matrix

| Phase | Agent A | Agent B | Agent C | Agent D | Agent E | Agent F |
|-------|---------|---------|---------|---------|---------|---------|
| **1** | API     | Schema  | BLOCKED | BLOCKED | BLOCKED | BLOCKED |
| **2** | ✅      | ✅      | UserSvc | AuthSvc | Routes  | Client  |
| **3** | -       | -       | ✅      | ✅      | ✅      | ✅      |

Task Status Symbols

Progress tracking uses three states:

[ ] - Task not started
[⏳] - Task in progress (currently being worked on)
[✓] - Task completed and validated

Benefits of Formal Planning

Structured planning workflows provide significant advantages:

Persistent documentation: Plans survive across sessions and team members
Version control integration: Plans tracked in Git alongside code
Progress transparency: Clear status tracking for stakeholders
Knowledge preservation: Research and decisions documented for future reference
Team collaboration: Multiple developers can reference and update plans
Standards enforcement: Links to project standards ensure consistency

Linking Plan Workflows to CLAUDE.md

Reference the planning workflow in your main project documentation (CLAUDE.md):

## Planning Workflows

This project uses structured planning for complex features. See [@CLAUDE/PlanWorkflow.md](CLAUDE/PlanWorkflow.md) for detailed workflow instructions.

All plans are stored in `CLAUDE/plan/` and must follow the standardized format.

### Quick Start

1. **Planning Mode** (default): Research and create plan in `CLAUDE/plan/feature-name.md`
2. **Execution Mode**: Explicitly trigger with "execute plan" or "proceed with implementation"
3. **Status Tracking**: Update progress markers `[ ]`, `[⏳]`, `[✓]` as you work

For infrastructure projects with deployment requirements, add safety rules:

## Planning Workflows

This project uses structured planning with strict deployment controls.

### Deployment Safety Rules

- Claude must NEVER execute deployment commands
- ALL deployment commands must be run by humans only
- Plans MUST conclude with exact deployment instructions section
- Commands must be researched and tested for accuracy

See [@CLAUDE/PlanWorkflow.md](CLAUDE/PlanWorkflow.md) for complete workflow documentation.

Parallel Agent Execution

Claude Code's parallel agent architecture enables sophisticated orchestration of multiple specialized agents working simultaneously. This represents a significant evolution from sequential, single-agent workflows.

Understanding Parallel Agents

Subagents operate in separate context windows, each with their own expertise and tool access. This provides:

Context isolation: Each agent uses full context for specialized tasks
Parallel execution: Multiple research or implementation streams run concurrently
Specialized focus: Agents can be experts in specific domains
Additive capacity: Multiple agents provide more total reasoning capacity

Activating Parallel Agent Execution

Tell Claude Code to execute your plan with parallel agents:

# Simple parallel execution
"Execute plan with sub agents"

# Specify parallelism level
"Explore the codebase using 4 tasks in parallel"

# Targeted parallel execution
"Use parallel agents to research authentication, database, and API layers simultaneously"

Multi-Agent Orchestration Patterns

Pattern 1: Parallel Research

Deploy multiple agents to research different aspects of a codebase simultaneously:

Agent 1: Database schema and query patterns
Agent 2: API endpoints and routing
Agent 3: Authentication and authorization
Agent 4: Frontend integration points

Each agent produces a report, which the lead agent synthesizes into a comprehensive plan.

Pattern 2: Parallel Implementation

For feature development with independent components:

Backend specialist: Implement server-side API endpoints
Frontend specialist: Build client-side UI components
QA specialist: Generate integration tests
Documentation specialist: Draft API documentation

Pattern 3: Sequential Handoffs

Create an automated assembly line for complete feature implementation:

Product manager agent: Creates detailed requirements and acceptance criteria
Architect agent: Designs technical approach and data structures
Implementation agent: Writes code following architecture
Review agent: Analyzes code quality and suggests improvements
Refinement agent: Applies review feedback
QA agent: Validates implementation against requirements

Performance Considerations

Parallel agent architectures have trade-offs:

Token usage: Multi-agent systems use approximately 15× more tokens than single-agent chats
Quality improvement: Multi-agent Claude Opus 4 (lead) + Sonnet 4 (subagents) outperformed single-agent Opus by 90.2% on internal research evaluations
Cost vs. speed: Parallel execution completes faster but consumes more resources
Non-determinism: AI behavior varies across runs; test agent prompts thoroughly

When to Use Parallel Agents

Parallel agent execution is most effective for:

Large codebase exploration: Researching multiple modules simultaneously
Multi-component features: Implementing backend, frontend, and tests in parallel
Cross-cutting changes: Refactoring patterns across multiple files
Complex investigations: Researching multiple potential solutions concurrently

Example: Plan Segment Execution

Given a comprehensive plan with multiple independent sections, Claude Code can execute segments in parallel. For example, given this plan:

## Progress

[ ] Research authentication system (Auth Service, JWT handling)
[ ] Research database layer (ORM patterns, query optimization)
[ ] Research API layer (routing, middleware, error handling)
[ ] Research frontend integration (state management, API clients)
[ ] Design unified architecture
[ ] Implement changes

You can trigger parallel execution:

"Execute the four research tasks in parallel with sub agents"

Claude Code will:

Launch four specialized agents, each researching one area
Each agent produces a detailed research report
The lead agent synthesizes findings into a unified design
Updates the plan document with research results
Marks research tasks as complete

Custom Subagents

While built-in parallel agents are powerful, custom subagents provide fine-grained control over agent behavior, tool access, and specialization. This is a deep topic that warrants its own dedicated article.

Quick Overview

Custom subagents enable:

Specialized system prompts: Tailored instructions for specific tasks
Tool access control: Limit agents to relevant tools only
Model selection: Use different models for different tasks (e.g., Opus for planning, Sonnet for implementation)
Reusable configurations: Share agent definitions across projects
Project-specific agents: Create agents that understand project conventions

Creating Custom Agents

Use the /agents command to create a new subagent:

/agents

Define the agent's characteristics:

Name: Unique identifier (e.g., code-reviewer, php-expert)
Description: Purpose and expertise area
System prompt: Detailed instructions and behavioral constraints
Tool access: Which tools the agent can use
Model: Which Claude model to use

Agent Configuration Hierarchy

Subagents can be configured at three levels:

Project-level: .claude/agents/ (highest priority, version-controlled)
User-level: ~/.claude/agents/ (personal agents across projects)
CLI-based: Dynamic configuration for one-off tasks

Best Practices for Custom Agents

When creating custom subagents:

Separation of concerns: One responsibility per agent
Provide examples: Include positive/negative examples in system prompts
Progressive tool expansion: Start with minimal tools, expand as needed
Detailed system prompts: LLMs excel at pattern recognition; be specific
Version control: Commit project-level agents to Git

Future Coverage

Custom subagents deserve comprehensive coverage, including:

Detailed agent configuration syntax
System prompt engineering strategies
Tool permission patterns and security considerations
Real-world agent examples (code reviewers, test generators, documentation writers)
Multi-agent coordination patterns
Debugging and iterating on agent behavior

This will be covered in a future dedicated article on advanced Claude Code agent architectures.

GitHub Integration for High-Level Tracking

For project management at scale, integrate Claude Code workflows with GitHub CLI for issue tracking and PR management.

Issue-Driven Development Workflow

A complete workflow that integrates planning, execution, and tracking:

1. Create Issue

Ask Claude Code to create an issue describing the feature or bug:

"Use gh to create a GitHub issue for implementing JWT authentication with refresh tokens"

Claude Code will execute gh issue create with appropriate title and body, then return the issue number for reference.

2. Create and Commit Plan

Request plan creation and commit it with issue reference:

"Create a detailed plan for JWT authentication in CLAUDE/plan/feature-auth-system.md
and commit it with message referencing issue #123"

Claude Code will create the plan file, commit with proper message format, and push to the remote repository.

3. Update Issue with Plan Link

Link the plan document to the issue for tracking:

"Use gh to add a comment to issue #123 linking to the plan file and the commit SHA"

Claude Code will get the commit SHA and add a formatted comment to the issue with a GitHub permalink to the plan file.

4. Execute Plan

Start implementation by referencing the plan document:

"Execute the plan in CLAUDE/plan/feature-auth-system.md"

Claude Code will read the plan, break down the tasks, and systematically implement each component with proper error handling and testing.

5. Commit Implementation

After execution, commit the changes with detailed summary:

"Commit the implementation with a detailed message referencing issue #123
and summarizing the changes"

Claude Code will stage relevant files, create a descriptive multi-line commit message with issue reference, and push to remote.

6. Update Issue with Completion Summary

Document the implementation results in the issue:

"Use gh to add a comment to issue #123 with an implementation summary including
the commit SHA, test coverage results, and confirmation that it's ready for PR"

Claude Code will retrieve the execution commit SHA, format a comprehensive summary with markdown, and post it to the issue.

7. Create Pull Request

Finally, create a PR directly from the completed work:

"Use gh to create a pull request for the JWT authentication implementation"

Claude Code will analyze the commits, generate PR title and body with summary/changes/testing sections, and create the PR targeting the main branch with proper issue closure reference.

Key Benefits: This workflow keeps all context in GitHub issues, provides clear audit trails, and enables team collaboration. Claude Code handles all the gh CLI complexity behind natural language requests, maintaining consistent formatting and following repository conventions automatically.

PR Workflow Integration

Use Claude Code to create PRs directly from plan completion:

# After completing plan execution
"Create a pull request for this feature implementation"

Claude Code will:

Generate a PR title from the work completed
Create a detailed PR description with summary and test plan
Push the current branch to remote
Open the PR via GitHub CLI
Return the PR URL

Tracking Plan Status via GitHub

Use GitHub Projects to visualize plan progress:

Backlog: Plans not yet started
Planning: Plans being researched and designed
Ready: Plans approved and ready for execution
In Progress: Plans currently being implemented
Review: PRs open for review
Done: PRs merged, plans archived

Cross-Repository Planning

For features spanning multiple repositories:

# Create tracking issue in each repo
gh issue create -R org/backend --title "Auth API endpoints"
gh issue create -R org/frontend --title "Auth UI components"
gh issue create -R org/infrastructure --title "Auth service deployment"

# Link related issues
gh issue comment 123 --body "Related: org/frontend#456, org/infrastructure#789"

Extended Thinking Mode

Claude Code supports extended thinking, where Claude uses additional reasoning tokens before responding. This is particularly valuable during the planning phase.

Triggering Extended Thinking

Use specific phrases to request deeper reasoning:

"think" - Standard extended thinking
"think hard" - More extensive reasoning
"think harder" - Increased reasoning budget
"ultrathink" - Maximum reasoning capacity

When to Use Extended Thinking

Extended thinking is most effective for:

Architecture decisions: Evaluating multiple design approaches
Complex refactoring: Understanding interconnected code changes
Security analysis: Identifying subtle vulnerabilities
Performance optimization: Analyzing algorithmic complexity

Example: Planning with Extended Thinking

"Think hard about the best approach to refactor the authentication system.
Consider security implications, backward compatibility, and migration strategy."

Claude will display its reasoning process before providing recommendations, helping you understand the thought process behind architectural decisions.

Practical Workflow Examples

Example 1: Simple Feature with Built-in Plan Mode

# Activate Plan Mode
Shift+Tab (twice)

# Research and plan
"Analyze how the current user profile system works and create a plan
to add avatar upload functionality"

# Review plan, exit Plan Mode
Shift+Tab

# Execute
"Implement the plan we just created"

Example 2: Complex Feature with Formal Planning

# Start in Planning Mode (formal)
"Create a plan for implementing OAuth2 integration.
Store it in CLAUDE/plan/oauth2-integration.md"

# Claude researches and creates detailed plan document

# Review plan, give approval
"The plan looks good. Execute the OAuth2 integration plan."

# Claude works through tasks, updating plan document as it progresses

Example 3: Multi-Repository Feature with Parallel Agents

# Create comprehensive plan
"Create a plan for implementing real-time notifications across our stack.
This will require changes to:
- Backend API (Node.js)
- Frontend UI (React)
- Infrastructure (WebSocket server)
- Database schema

Store the plan in CLAUDE/plan/realtime-notifications.md"

# Execute with parallel agents
"Execute the plan using parallel agents. Have separate agents work on:
- Backend WebSocket implementation
- Frontend notification UI
- Infrastructure deployment config
- Database migrations

Coordinate the changes to ensure they work together."

# Create PRs for each component
"Create a PR for each repository with the changes"

Example 4: Large Codebase Exploration

# Research with parallel agents
"Use 5 parallel agents to explore this codebase. Have each agent research:
1. Authentication and authorization patterns
2. Database models and relationships
3. API routes and controllers
4. Frontend architecture and state management
5. Testing patterns and coverage

Create a comprehensive architecture document from the findings."

Best Practices and Anti-Patterns

Do: Separate Planning from Execution

Always complete planning before executing. Switching between modes mid-task leads to confused context and poor decisions.

Do: Update Plans as You Learn

Plans should evolve as implementation reveals new information. Add discovered tasks to the Progress section immediately.

Do: Use Parallel Agents for Independent Work

Deploy parallel agents when tasks are truly independent. Don't parallelize tightly coupled changes.

Don't: Mix Planning and Execution Context

Keep planning conversations separate from execution conversations. Use formal plan documents as the handoff point.

Don't: Parallelize Without Clear Boundaries

Parallel agents need clear, independent scopes. Overlapping responsibilities lead to conflicts and rework.

Don't: Skip Progress Tracking

Update task status immediately after completion. Batch updates lead to lost context and duplicate work.

Choosing Your Workflow

Select the appropriate workflow based on task complexity:

Built-in Plan Mode

Best for:

Quick explorations and investigations
Single-file or small changes
Interactive refinement of ideas
Solo developer work

Formal Planning (CLAUDE/plan)

Best for:

Multi-file refactoring
New feature implementation
Team collaboration
Long-running projects
Work requiring documentation

Parallel Agent Execution

Best for:

Large codebase exploration
Multi-component features
Complex investigations
Cross-cutting changes
Time-sensitive projects

Custom Subagents

Best for:

Repeated specialized tasks
Project-specific workflows
Enforcing coding standards
Multi-stage pipelines
Advanced orchestration

Conclusion

Claude Code's planning capabilities range from simple built-in modes to sophisticated parallel agent architectures. The right approach depends on your project complexity, team structure, and workflow requirements.

Start with built-in Plan Mode for simple tasks. Graduate to formal planning workflows (CLAUDE/plan) as projects grow in complexity. Deploy parallel agents when you need speed and have independent workstreams. Create custom subagents when you have repeated, specialized needs.

Most importantly: always separate planning from execution. This single principle, regardless of which workflow you choose, will dramatically improve the quality and maintainability of your AI-assisted development.

Resources

Claude Code Common Workflows - Official documentation on Plan Mode
Claude Code Subagents - Official subagent documentation
How We Built Our Multi-Agent Research System - Anthropic's deep dive on parallel agents
Claude Code Best Practices - Official best practices from Anthropic
GitHub CLI - Command-line tool for GitHub integration
Extended Thinking - Anthropic's announcement of extended thinking capabilities
ClaudeLog: Plan Mode Mechanics - Community guide to Plan Mode
Production-Ready Subagents Collection - Community-created agent examples