Multi-Agent Orchestration with Claude
As projects become more complex, a single AI agent is no longer enough. The real revolution in AI-assisted development comes with multi-agent orchestration: the ability to coordinate multiple instances of Claude Code working simultaneously on different aspects of the same project.
In this article we will explore the complete ecosystem of orchestration tools for Claude Code, from official solutions to community innovations. We will see how Claude Squad, Claude Task Master, Auto-Claude, Claude Swarm and other tools allow you to multiply productivity through intelligent agent parallelism.
What You Will Learn
- Coordinate multiple Claude Code instances in parallel with Claude Squad
- Manage complex tasks with Claude Task Master
- Implement autonomous frameworks with Auto-Claude
- Connect agents in collaborative swarms with Claude Swarm
- Use Git Worktrees for parallel development
- Apply orchestration patterns: divide-and-conquer, pipeline and swarm
- Avoid common pitfalls in multi-agent orchestration
Series Overview
| # | Article | Focus |
|---|---|---|
| 1 | Claude as Technical Partner | Setup and first steps |
| 2 | Context and Project Setup | CLAUDE.md and configuration |
| 3 | Ideation and Requirements | MVP and user personas |
| 4 | Backend and Frontend Architecture | Spring Boot and Angular |
| 5 | Code Structure | Organization and conventions |
| 6 | Advanced Prompt Engineering | Advanced techniques |
| 7 | Testing and Quality | Strategies and test generation |
| 8 | Professional Documentation | README, API, ADR |
| 9 | Deploy and DevOps | Docker, CI/CD, monitoring |
| 10 | Evolution and Maintenance | Refactoring and scalability |
| 11 | Real Project Integration | Claude Code in production |
| 12 | Claude Code CLI Deep Dive | Advanced commands and workflows |
| 13 | Agent Skills | Automation with custom commands |
| 14 | Specialized Subagents | Delegating tasks to sub-agents |
| 15 | Hooks and Automation | Event-driven automation |
| 16 | The Ralph Wiggum Method | Advanced prompt chaining |
| 17 | The BMAD Method | Structured project management |
| 18 | Multi-Agent Orchestration (you are here) | Parallel and coordinated agents |
| 19 | Claude Cowork for Knowledge Workers | AI for non-developers |
| 20 | Security and Privacy | Data protection and compliance |
| 21 | Monitoring and Productivity | Metrics and optimization |
1. Why Multi-Agent Orchestration
Modern software development requires simultaneously managing multiple aspects: frontend, backend, database, tests, documentation, CI/CD. A single agent working sequentially on each aspect is inherently limited by the serial nature of the process. Multi-agent orchestration breaks this constraint by allowing different agents to operate in parallel on independent tasks, communicating with each other when necessary.
The concept is not new in computer science: distributed systems, microservice architectures and parallel computing follow the same philosophy. The novelty is applying these principles to AI agents, where each Claude Code instance has its own session, its own context and its own specialization.
Serial vs Parallel Development
| Aspect | Single Agent | Multi-Agent |
|---|---|---|
| Speed | Sequential, one task at a time | Parallel, multiple simultaneous tasks |
| Context | Shared context window for everything | Dedicated context for each agent |
| Specialization | Generalist across all tasks | Each agent can be specialized |
| Resilience | An error blocks the entire workflow | Failure isolated per agent |
| Scalability | Limited by the context window | Scales with the number of agents |
| Complexity | Simple to manage | Requires coordination |
Multi-agent orchestration is particularly effective when the project has independent tasks that can proceed in parallel, or when the complexity of the codebase exceeds the capacity of a single context. In a full-stack project, for example, frontend, backend and test modifications can proceed simultaneously if coordinated intelligently.
2. Claude Squad: Parallel Agents in the Terminal
Claude Squad is a terminal application that allows you to manage multiple Claude Code instances running simultaneously. Developed by the community, it offers a TUI (Text User Interface) for monitoring, coordinating and interacting with different agents, each focused on a specific task.
Claude Squad Architecture
Claude Squad operates by creating independent Claude Code sessions, each in its own isolated environment. Each session has its own context, its own working files and its own state. The TUI interface provides a unified view of all active sessions, allowing you to switch between them, monitor progress and send instructions.
# Installation via Go
go install github.com/smtg-ai/claude-squad@latest
# Or from source
git clone https://github.com/smtg-ai/claude-squad.git
cd claude-squad
go build -o claude-squad
# Verify installation
claude-squad --versionPractical Usage
Once installed, Claude Squad is launched with the claude-squad
command which opens the TUI interface. From here you can create new instances, assign tasks
and monitor each agent's progress.
# Start Claude Squad
claude-squad
# Create a new instance with a specific task
# In the TUI interface:
# [n] - New instance
# [d] - Describe the task
# [Enter] - Start
# Example: 3 parallel agents for a full-stack project
# Agent 1: "Implement the REST endpoint /api/v1/orders with validation"
# Agent 2: "Create the Angular OrderListComponent with pagination"
# Agent 3: "Write integration tests for the orders module"
# TUI Commands
# [Tab] - Switch to next session
# [1-9] - Select session by number
# [Enter] - Send input to active session
# [q] - Exit Claude SquadKey Features of Claude Squad
- Multi-session: Manage up to 10+ Claude Code sessions simultaneously
- TUI Interface: Unified view with split-pane for each agent
- Isolation: Each agent operates in an independent context
- Monitoring: Real-time status of each session (idle, working, waiting)
- Git Integration: Each agent can work on a different branch
- Session Persistence: Sessions survive terminal closure
Use Case: Parallel Feature Development
The most common use case for Claude Squad is parallel development of independent features. Imagine having to implement three functionalities for an e-commerce application: order management, the notification system and the reporting module.
# Session 1: Order Management
Prompt: "Implement the complete CRUD for orders.
- Entity Order with fields: id, customerId, items, total, status, createdAt
- OrderRepository with queries by customer and status
- OrderService with business rules validation
- OrderController with REST endpoints
- Unit tests for service and controller"
# Session 2: Notification System
Prompt: "Create the email notification system.
- NotificationService with template engine (Thymeleaf)
- Templates for: order confirmation, shipping, payment
- Event listener for OrderCreatedEvent and OrderShippedEvent
- SMTP configuration with fallback
- Integration tests with MailHog"
# Session 3: Reporting Module
Prompt: "Implement the order reporting module.
- ReportService with sales data aggregation
- Endpoint /api/v1/reports/sales with date filters
- CSV and PDF export (with iText)
- Redis cache for frequent reports
- Tests with simulated data"With Claude Squad, these three features are developed simultaneously by three independent agents. The total time is not the sum of the three individual times, but approximately the time of the longest task plus coordination overhead.
3. Claude Task Master: AI-Driven Task Management
Claude Task Master (25.3k+ stars on GitHub) is an AI-driven task management framework. Unlike Claude Squad, which focuses on parallel execution, Task Master excels at decomposition, prioritization and tracking of complex tasks.
How It Works
Task Master operates on a fundamental principle: every complex project can be decomposed into atomic tasks with explicit dependencies. The AI analyzes the project description, identifies the necessary tasks, establishes dependencies and suggests the optimal execution order.
# Global installation
npm install -g task-master-ai
# Project initialization
task-master init
# This creates the structure:
# .task-master/
# config.json - Project configuration
# tasks/ - Generated tasks directory
# dependencies.json - Dependency graph
# progress.json - Progress statusAutomatic Task Decomposition
The most powerful feature of Task Master is the ability to take a high-level project description and automatically decompose it into manageable atomic tasks. Each generated task includes a clear description, acceptance criteria, dependencies and complexity estimate.
# Generate tasks from a project description
task-master parse-prd --input=docs/requirements.md
# Example output:
# Generated 24 tasks from PRD:
#
# Task 1: Base project setup (priority: high, complexity: 3)
# Dependencies: none
# Subtasks: init repo, configure build, setup CI
#
# Task 2: Database schema (priority: high, complexity: 5)
# Dependencies: Task 1
# Subtasks: design ERD, create migrations, seed data
#
# Task 3: User authentication (priority: high, complexity: 8)
# Dependencies: Task 1, Task 2
# Subtasks: JWT setup, login/register endpoints, middleware
#
# ...
# Visualize the dependency graph
task-master graph
# Get the next task to work on
task-master next
# Output:
# Next recommended task: Task 4 - Orders API
# Reason: All dependencies met, highest priority among available tasks
# Estimated complexity: 5/10
# Estimated time: 2-3 hoursTask Master Features
| Feature | Description | Value |
|---|---|---|
| PRD Parsing | Generates tasks from requirements documents | Saves hours of planning |
| Dependency Graph | Automatic dependency mapping | Optimal execution order |
| Complexity Estimation | Automatic complexity estimation | Realistic sprint planning |
| Smart Prioritization | Prioritization based on value and risk | Focus on highest-impact activities |
| Progress Tracking | Progress status monitoring | Real-time visibility |
| Context Generation | Generates context for each task | Agents receive precise instructions |
Integration with Claude Code
Task Master integrates natively with Claude Code. After generating the tasks, you can pass them directly to Claude Code for implementation. The context for each task includes resolved dependencies, relevant files and acceptance criteria.
# 1. Generate tasks from the PRD
task-master parse-prd --input=docs/requirements.md
# 2. Get the next task with full context
task-master next --with-context
# 3. Pass the context to Claude Code
claude "Implement the following task:
$(task-master next --with-context)
Relevant files to consult:
$(task-master files --task=current)
Acceptance criteria:
$(task-master acceptance --task=current)"
# 4. Mark the task as completed
task-master complete --task=4
# 5. Regenerate dependencies for subsequent tasks
task-master update-deps4. Auto-Claude: Autonomous Framework
Auto-Claude takes orchestration to a higher level by implementing a fully autonomous agent framework. Unlike the previous tools that require human intervention for coordination, Auto-Claude autonomously manages the agent lifecycle, from creation to termination.
Auto-Claude Architecture
Auto-Claude uses a hierarchical architecture with an orchestrator agent that supervises one or more worker agents. The orchestrator receives the high-level objective, decomposes it into sub-tasks, assigns each sub-task to a worker and monitors progress, intervening when necessary.
# Auto-Claude Architecture
#
# +---------------------+
# | Orchestrator |
# | (Claude Instance) |
# +---------------------+
# | | |
# v v v
# +------+ +------+ +------+
# |Worker| |Worker| |Worker|
# | #1 | | #2 | | #3 |
# +------+ +------+ +------+
# | | |
# v v v
# [Files] [Files] [Files]
#
# The orchestrator:
# 1. Receives the objective from the user
# 2. Analyzes the codebase
# 3. Generates an execution plan
# 4. Creates a worker for each sub-task
# 5. Monitors progress
# 6. Manages conflicts between workers
# 7. Validates results
# 8. Produces the final reportAutonomous Management Loop
The heart of Auto-Claude is the autonomous loop that governs execution. The orchestrator does not just distribute tasks: it actively monitors progress, detects blocks and failures, and makes corrective decisions without human intervention.
# Auto-Claude loop pseudocode
while objective_not_reached:
# 1. Evaluate current state
state = analyze_progress(workers)
# 2. Identify blocks
blocks = find_blocks(state)
# 3. If there are blocks, intervene
for block in blocks:
if block.type == "missing_dependency":
reorder_tasks(block.worker)
elif block.type == "compilation_error":
assign_fix(block.worker, block.error)
elif block.type == "merge_conflict":
resolve_conflict(block.workers)
elif block.type == "timeout":
restart_worker(block.worker)
# 4. Verify completed tasks
for worker in completed_workers:
if validate_output(worker):
mark_completed(worker)
release_dependencies(worker)
else:
reassign_task(worker, feedback="Invalid output")
# 5. Assign new tasks if there are free workers
for worker in free_workers:
next_task = get_next_task()
if next_task:
assign_task(worker, next_task)
# 6. Verify global objective
if all_tasks_completed():
execute_final_validation()
break
sleep(check_interval)Task Distribution Algorithms
- Round-Robin: Fair cyclic distribution among available agents, ideal for tasks of uniform complexity
- Least-Loaded: Assigns to the worker with the lowest current load, optimal for balancing times
- Skill-Based: Assigns based on agent specialization (frontend, backend, test), maximizes quality
- Priority-Weighted: Considers task priority and worker capacity, balances urgency and competence
- Dependency-Aware: Respects the dependency graph, assigning tasks with no pending prerequisites first
5. Claude Swarm: Connectivity Between Agents
Claude Swarm adopts a radically different approach from hierarchical orchestration: agents operate as a swarm, communicating with each other in a peer-to-peer manner without a centralized orchestrator. This model is inspired by the collective intelligence of natural swarms (bees, ants, starlings).
Communication Protocols
In a swarm system, each agent must be able to communicate with others to share discoveries, report conflicts and coordinate work. Claude Swarm implements different communication protocols depending on the type of interaction needed.
Communication Protocols in the Swarm
| Protocol | Type | Use | Latency |
|---|---|---|---|
| Broadcast | One-to-all | Global announcements, state changes | High |
| Direct Message | One-to-one | Specific requests, handoff | Low |
| Publish/Subscribe | Topic-based | Updates by area (frontend, backend) | Medium |
| Shared State | Shared Read/Write | Project state, modified files | Variable |
| Request/Reply | Synchronous | Validation, code review between agents | Medium |
Shared Context Management
The main challenge of a swarm system is managing shared context. When multiple agents modify the same codebase, it is essential that each one is aware of the others' changes to avoid conflicts and duplications.
# swarm-config.yaml
swarm:
name: "e-commerce-project"
max_agents: 5
communication:
protocol: "pub-sub"
shared_state: true
conflict_resolution: "last-writer-wins"
agents:
- name: "frontend-agent"
role: "Frontend Developer"
focus: ["src/app/**/*.ts", "src/app/**/*.html", "src/app/**/*.css"]
subscribe: ["api-changes", "schema-changes"]
publish: ["ui-updates"]
- name: "backend-agent"
role: "Backend Developer"
focus: ["src/main/java/**/*.java"]
subscribe: ["schema-changes", "ui-requirements"]
publish: ["api-changes"]
- name: "database-agent"
role: "Database Engineer"
focus: ["migrations/**", "schema/**"]
subscribe: ["data-requirements"]
publish: ["schema-changes"]
- name: "test-agent"
role: "QA Engineer"
focus: ["src/test/**", "e2e/**"]
subscribe: ["api-changes", "ui-updates", "schema-changes"]
publish: ["test-results", "bug-reports"]
- name: "devops-agent"
role: "DevOps Engineer"
focus: ["docker/**", ".github/**", "deploy/**"]
subscribe: ["build-requirements"]
publish: ["deploy-status"]
rules:
- "frontend-agent does not modify Java files"
- "backend-agent does not modify Angular files"
- "test-agent has read access to everything"
- "Each agent commits on its own branch"
- "Merge only after automatic review"6. The Startup: Orchestrator for Production Code
The Startup is an orchestration tool specifically focused on writing production-ready code. Unlike other tools that prioritize development speed, The Startup emphasizes code quality, test coverage and compliance with team standards.
Quality Pipeline
The Startup implements a mandatory pipeline for every change produced by agents. No generated code is considered complete until it passes all stages of the quality pipeline.
The Startup Quality Pipeline
| Stage | Verification | Threshold |
|---|---|---|
| 1. Compilation | Code compiles without errors | 0 errors |
| 2. Linting | Adherence to ESLint/Checkstyle rules | 0 violations |
| 3. Unit Test | All unit tests pass | 100% pass rate |
| 4. Coverage | Sufficient code coverage | 80% minimum |
| 5. Code Review | Automatic AI review | Score 7/10+ |
| 6. Security Scan | No known vulnerabilities | 0 critical/high |
7. Git Worktrees + Claude Code for Parallel Development
Git Worktrees is a native Git feature that allows you to have multiple working directories connected to the same repository. Combined with Claude Code, worktrees become a very powerful tool for parallel development: each worktree can host its own independent Claude Code session working on a different branch without interference.
Creating Worktrees
Creating a worktree is simple and instantaneous. Unlike cloning, a worktree shares the Git history of the original repository, saving disk space and setup time.
# Project structure with worktrees
# /projects/my-app/ (main worktree, branch main)
# /projects/my-app-feature-a/ (worktree for feature A)
# /projects/my-app-feature-b/ (worktree for feature B)
# /projects/my-app-bugfix/ (worktree for bugfix)
# Create worktree for feature A
git worktree add ../my-app-feature-a feature/user-auth
# Creating branch 'feature/user-auth'
# Preparing worktree (new branch 'feature/user-auth')
# Create worktree for feature B
git worktree add ../my-app-feature-b feature/payment-system
# Creating branch 'feature/payment-system'
# Preparing worktree (new branch 'feature/payment-system')
# Create worktree for urgent bugfix
git worktree add ../my-app-bugfix hotfix/login-timeout
# Creating branch 'hotfix/login-timeout'
# Preparing worktree (new branch 'hotfix/login-timeout')
# Verify active worktrees
git worktree list
# /projects/my-app abc1234 [main]
# /projects/my-app-feature-a abc1234 [feature/user-auth]
# /projects/my-app-feature-b abc1234 [feature/payment-system]
# /projects/my-app-bugfix abc1234 [hotfix/login-timeout]One Claude Session per Worktree
The key advantage is that each worktree gets its own independent Claude Code session. Claude reads the CLAUDE.md and the project context, but operates in isolation on its own worktree files. One agent's changes do not interfere with the others' work until merge time.
# Terminal 1: Claude Code in feature A worktree
cd /projects/my-app-feature-a
claude "Implement the user authentication system:
- JWT with refresh token
- Login, register, logout endpoints
- Authentication middleware
- Password hashing with bcrypt
- Rate limiting on auth endpoints"
# Terminal 2: Claude Code in feature B worktree
cd /projects/my-app-feature-b
claude "Implement the payment system with Stripe:
- Stripe SDK integration
- Endpoint to create PaymentIntent
- Webhook for payment confirmation
- Error handling and retry
- Transaction logging"
# Terminal 3: Claude Code in bugfix worktree
cd /projects/my-app-bugfix
claude "Fix the timeout bug on the login page:
- Analyze the LoginComponent
- Identify the cause of the timeout after 30 seconds
- Implement the fix while maintaining security
- Add tests for the specific case"Merging Results
After each agent has completed its work in its respective worktree, the results are merged into the main branch. It is important to perform the merge in dependency order and resolve any conflicts.
# 1. Return to the main worktree
cd /projects/my-app
# 2. Merge the bugfix first (highest priority)
git merge hotfix/login-timeout
# No conflicts, direct merge
# 3. Merge feature A
git merge feature/user-auth
# Possible conflicts on shared files (app.module, routes)
# Resolve conflicts manually or with Claude:
# claude "Resolve merge conflicts between main and feature/user-auth"
# 4. Merge feature B
git merge feature/payment-system
# May have conflicts with routes added by feature A
# 5. Cleanup: remove completed worktrees
git worktree remove ../my-app-feature-a
git worktree remove ../my-app-feature-b
git worktree remove ../my-app-bugfix
# 6. Delete local branches
git branch -d feature/user-auth feature/payment-system hotfix/login-timeoutAdvantages of Git Worktrees over Traditional Branches
- No stash needed: No need to stash work-in-progress changes when switching context
- Independent builds: Each worktree has its own compiled files and node_modules
- Isolated Claude sessions: Each agent works on physically separate files
- Shared disk space: Git history is shared between worktrees
- Instantaneous: Creating a worktree is nearly immediate
8. Orchestration Patterns
Regardless of the tool used, there are three fundamental multi-agent orchestration patterns. The choice of pattern depends on the nature of the project, the type of tasks and the level of interdependency between parts.
Pattern 1: Divide-and-Conquer
The divide-and-conquer pattern is the simplest and most direct. A complex task is divided into independent sub-tasks, each assigned to a different agent. The results are then combined at the end of execution. It works best when sub-tasks have minimal mutual dependencies.
# Pattern: Divide-and-Conquer
#
# [Complex Task]
# |
# [Decomposition]
# / | \
# v v v
# [Sub-A] [Sub-B] [Sub-C] <-- Parallel execution
# \ | /
# v v v
# [Combination]
# |
# v
# [Result]
# Practical example: Refactoring a monolith
# Sub-A: Extract the users module
# Sub-B: Extract the orders module
# Sub-C: Extract the payments module
# Combination: Integrate the modules with defined interfaces
# When to use it:
# - Naturally parallelizable tasks
# - Minimal dependencies between sub-tasks
# - Mechanically combinable results
# - Example: generation of independent UI componentsPattern 2: Pipeline
The pipeline pattern organizes agents in a sequential chain where one agent's output becomes the next one's input. It is ideal for workflows where each phase builds on the previous result, like the classic design, implement, test, deploy pipeline.
# Pattern: Pipeline
#
# [Input] --> [Agent 1] --> [Agent 2] --> [Agent 3] --> [Output]
# Architect Developer Tester
#
# Stage 1 (Architect):
# Input: User requirements
# Output: Design document, interfaces, DB schema
#
# Stage 2 (Developer):
# Input: Design document from Stage 1
# Output: Code implemented according to the design
#
# Stage 3 (Tester):
# Input: Code from Stage 2
# Output: Test suite, coverage report, bug fixes
# Advantages:
# - Each agent is specialized in its stage
# - Well-defined output between stages
# - Easy to debug (which stage failed?)
# - Incremental quality at each step
# Disadvantages:
# - Sequential (not parallelizable)
# - Bottleneck: the slowest stage
# - Errors in stage 1 cascade downstreamPattern 3: Swarm (Collaborative Swarm)
The swarm pattern is the most complex but also the most flexible. Agents operate autonomously toward a common goal, communicating and coordinating in a peer-to-peer manner. There is no central orchestrator: intelligence emerges from the collaboration between agents.
# Pattern: Swarm
#
# [A1] <--> [A2]
# ^ \ / ^
# | \ / |
# v \/ v
# [A3] /\ [A4]
# ^ / \ ^
# | / \ |
# v v
# [A5] <--> [A6]
#
# Each agent:
# 1. Observes the global project state
# 2. Autonomously identifies the next task
# 3. Communicates with relevant agents
# 4. Executes the task
# 5. Publishes results
# 6. Repeats until the objective is reached
# Swarm rules:
# - No agent is the "boss"
# - Decisions made by local consensus
# - Conflicts resolved by predefined rules
# - The objective emerges from collective behavior
# When to use it:
# - Exploratory problems (complex debugging)
# - Very large codebase to analyze
# - When it's unclear how to decompose the task
# - Searching for creative solutionsComparison of Orchestration Patterns
| Characteristic | Divide-and-Conquer | Pipeline | Swarm |
|---|---|---|---|
| Parallelism | High | None | High |
| Coordination | Only at the end | Sequential | Continuous |
| Setup Complexity | Low | Medium | High |
| Suitable for | Independent tasks | Linear workflows | Complex problems |
| Conflict Risk | Low | None | Medium-high |
| Scalability | Excellent | Limited | Good |
9. Best Practices and Common Pitfalls
Multi-agent orchestration introduces significant complexity compared to using a single agent. To maximize the benefits and minimize problems, it is essential to follow some well-established best practices.
Best Practices for Multi-Agent Orchestration
- Define clear boundaries: Each agent must have a well-defined scope of action, with explicitly assigned files and directories
- Minimize dependencies: The more independent the tasks, the more effective the parallelism. Invest time in the initial decomposition
- Use separate branches: Each agent should work on its own Git branch. Merging happens only after validation
- Implement file locks: Prevent simultaneous modifications to the same file using locks or ownership conventions
- Monitor costs: More agents mean more tokens consumed. Monitor costs and set budgets per agent
- Test after merge: Integration of results is the most critical moment. Always run a complete test suite after each merge
- Document interfaces: Define clear contracts between modules that different agents work on
- Start small: Begin with 2-3 agents and scale gradually. Complexity grows exponentially with the number of agents
Common Pitfalls
Frequent Problems and Solutions
| Problem | Cause | Solution |
|---|---|---|
| Merge conflicts | Multiple agents modify the same files | Assign clear file ownership, use stable interfaces |
| Stylistic inconsistency | Each agent adopts different conventions | Shared CLAUDE.md with explicit conventions, automatic linting |
| Code duplication | Agents independently create similar utilities | Define shared libraries before starting, post-merge review |
| Uncontrolled costs | Agents in loops or waiting consume tokens | Timeout per agent, maximum budget, real-time monitoring |
| Dependency deadlock | Agent A waits for B which waits for A | Acyclic dependency graph (DAG), automatic detection |
| Context pollution | Too much shared context slows down agents | Minimal context per agent, only files relevant to the task |
Checklist for Success
# Multi-Agent Pre-flight Checklist
## Preparation
- [ ] Tasks decomposed into independent sub-tasks
- [ ] Dependencies between tasks identified and documented
- [ ] File ownership assigned for each agent
- [ ] Interfaces between modules defined and stable
- [ ] CLAUDE.md updated with team conventions
- [ ] Branch strategy defined (one branch per agent)
## Configuration
- [ ] Token budget per agent set
- [ ] Session timeout configured
- [ ] Monitoring activated (costs, progress, errors)
- [ ] Fallback plan for individual agent failures
- [ ] Test suite ready for post-merge validation
## Execution
- [ ] Start agents in dependency order
- [ ] Monitor progress every 15-30 minutes
- [ ] Intervene manually if an agent is blocked for more than 1 hour
- [ ] Save intermediate checkpoints for each agent
## Post-Execution
- [ ] Merge in dependency order
- [ ] Resolve conflicts carefully
- [ ] Run complete test suite
- [ ] Code review of combined results
- [ ] Document lessons learned10. Practical Example: Orchestrating a Full-Stack Project
Let's put all the concepts together in a complete practical example. Suppose we need to develop an event management module for a web application, with a Spring Boot backend, Angular frontend and PostgreSQL database.
# Project: Event Management Module
# Stack: Spring Boot + Angular + PostgreSQL
# Strategy: Divide-and-Conquer with Git Worktrees
# Step 1: Create the worktrees
git worktree add ../events-backend feature/events-backend
git worktree add ../events-frontend feature/events-frontend
git worktree add ../events-database feature/events-database
git worktree add ../events-tests feature/events-tests
# Step 2: Start the agents (4 separate terminals)
# Database Agent (starts first - others depend on the schema)
cd ../events-database
claude "Create the database schema for the events module:
- Table events (id, title, description, start_date, end_date, location, capacity, status)
- Table event_registrations (id, event_id, user_id, registered_at, status)
- Table event_categories (id, name, description)
- Table event_category_map (event_id, category_id)
- Indexes for the most frequent queries
- Numbered Flyway migrations
- Seed data for development"
# Backend Agent (after the schema is defined)
cd ../events-backend
claude "Implement the backend for the events module:
- JPA entities mapped to the existing schema in migrations/
- EventRepository with custom queries (findByDateRange, findByCategory)
- EventService with business logic (creation, registration, cancellation)
- EventController with REST CRUD endpoints
- DTOs for request/response
- Input validation with Bean Validation
- Error handling with exception handler
- OpenAPI documentation"
# Frontend Agent (after the APIs are defined)
cd ../events-frontend
claude "Implement the Angular frontend for the events module:
- EventListComponent with pagination and filters
- EventDetailComponent with registration
- EventFormComponent for creation/editing
- EventService for API calls
- Routing with lazy loading
- Responsive design (mobile-first)
- Unit tests for each component"
# Test Agent (works in parallel, writes end-to-end tests)
cd ../events-tests
claude "Write the test suite for the events module:
- Integration tests for EventController (MockMvc)
- Tests for EventService with repository mocks
- Cypress end-to-end tests for the main flows:
- Event creation
- Event registration
- Registration cancellation
- Event list viewing with filters
- Performance tests with JMeter (optional)"
# Step 3: Merge in order
# database -> backend -> frontend -> tests
git merge feature/events-database # Schema first
git merge feature/events-backend # API after schema
git merge feature/events-frontend # UI after APIs
git merge feature/events-tests # Tests at the end
# Step 4: Final validation
mvn test # Backend tests
npm test --prefix frontend # Frontend tests
npm run e2e --prefix e2e # End-to-end testsConclusion
Multi-agent orchestration represents the next evolutionary leap in AI-assisted development. Tools like Claude Squad, Claude Task Master, Auto-Claude and Claude Swarm allow you to leverage parallelism to drastically reduce development times, while maintaining high code quality.
The combination of Git Worktrees with independent Claude Code sessions offers a pragmatic and immediately applicable solution for parallel development, without requiring additional tools. For more complex projects, orchestration frameworks provide the necessary coordination to manage dozens of agents.
Key Takeaways
- Claude Squad is ideal for managing 3-10 parallel agents with an intuitive TUI interface
- Claude Task Master excels at automatic decomposition and prioritization of tasks
- Auto-Claude takes autonomy to the highest level with self-correcting management loops
- Claude Swarm enables peer-to-peer collaboration for exploratory problems
- Git Worktrees are the most pragmatic solution for parallel development with Claude Code
- Divide-and-Conquer is the safest pattern to start with multi-agent orchestration
- Define clear boundaries for each agent: file ownership, interfaces and separate branches
- Monitor costs: more agents means more tokens. Set budgets and timeouts per session
In the next article we will explore Claude Cowork, a solution that brings the power of Claude Code outside the terminal, making it accessible to non-developer knowledge workers.