How to Orchestrate Multiple AI Agents for Collaborative Development

Introduction

Imagine asking an AI team to build a complete blog system. Instead of a single model taking two days, the Harness approach assigns a Codex agent to the backend API (4 h), a Claude agent to the frontend pages (4 h), a test agent to unit tests (2 h), and a security agent to review code (1 h), completing the whole project in four hours through parallel execution.

Why Multi‑Agent Collaboration?

Limitations of a Single Model

The article compares four models:

Codex – strong at code generation, weak at documentation.

Claude – strong at understanding, weaker at code generation.

Gemini – multimodal, but average at programming.

Specialized models – domain expertise, but limited general ability.

Conclusion: no single model can handle all tasks, so collaboration is required.

Advantages of Multiple Agents

A side‑by‑side comparison shows that a single agent is slow, produces average quality, has limited coverage, and offers no review, whereas multiple agents run in parallel, deliver higher quality through specialization, provide comprehensive coverage, and can review each other’s work.

Collaboration Modes

Mode 1 – Main Agent + Harness (Simplest)

Use a main agent (OpenClaw) to decompose the task and dispatch a single Harness agent (e.g., Codex) to execute it. Suitable for simple, single‑skill tasks.

User → "Create blog system"
Main Agent (OpenClaw) → Harness Agent (Codex) → Execution

Mode 2 – Parallel Multi‑Agent (Recommended)

The main agent coordinates several specialist agents (Codex for backend, Claude for frontend, Test agent for testing) that run concurrently. Example prompt:

Create blog system, split into three parallel tasks:
1. Backend API (Node.js + Express)
2. Frontend pages (React)
3. Unit tests
After completion, integrate results.

The main agent automatically launches the sub‑agents; the user does not need to write any code.

Mode 3 – Multi‑Layer Orchestration (Complex)

For large projects, a hierarchy of orchestrators is used: a top‑level main agent delegates to two group orchestrators (backend group and frontend group), each of which spawns specialist agents (Codex, Claude, Test, etc.). This enables deep decomposition and team‑scale collaboration.

User → Main Agent
Main Agent → Orchestrator 1 (Backend) & Orchestrator 2 (Frontend)
Orchestrator 1 → Codex (backend) & Test Agent
Orchestrator 2 → Claude (frontend) & UI Agent

Task Decomposition Strategy

Good vs. Bad Decomposition

Bad: "Do a blog system" – too vague to assign.

Task: "Make a blog system"
Decomposition: └─ Make blog system ← too vague

Good: break the project into independent, verifiable sub‑tasks such as project initialization, backend development, frontend development, testing, and deployment, each with concrete deliverables.

Task: "Make a blog system"
Decomposition:
├─ Project init (package.json, ESLint)
├─ Backend (DB model, API routes, middleware)
├─ Frontend (components, styles, state)
├─ Testing (unit, integration)
└─ Deployment (Docker, CI/CD)

Decomposition Principles

MECE – mutually exclusive, collectively exhaustive (e.g., separate backend, frontend, ops).

Assignable – each sub‑task can be given to a specific agent (e.g., "Write user login API" → Codex).

Verifiable – outcomes can be measured (e.g., "Create 5 API endpoints and pass tests").

Decomposition Template

## Project: [Name]
### Phase 1: Init
- [ ] Create project structure
- [ ] Configure dev environment
- [ ] Set up version control
### Phase 2: Backend
- [ ] DB model
- [ ] API routes
- [ ] Auth
### Phase 3: Frontend
- [ ] Page components
- [ ] Styling
- [ ] Interaction
### Phase 4: Testing
- [ ] Unit tests
- [ ] Integration tests
- [ ] E2E tests
### Phase 5: Deployment
- [ ] Containerize
- [ ] CI/CD
- [ ] Monitoring

Practical Example: User Authentication System

Project Description

Goal: Build a complete user authentication system
Requirements:
- Register / login
- JWT tokens
- Password hashing
- Email verification
- Password reset

Step 1 – Establish Coordination Context

Prompt the main agent to set the working directory and ask it to decompose the project:

"I want to develop a user authentication system in ~/projects/auth-system. Please split the work and explain how you will assign sub‑agents or parallel steps."

Step 2 – Task Decomposition

Task 1: Backend API (Codex)
- User model
- Register API (POST /api/auth/register)
- Login API (POST /api/auth/login)
- Logout API (POST /api/auth/logout)
- Password‑reset API (POST /api/auth/reset-password)

Task 2: Frontend pages (Claude)
- Register page
- Login page
- Password‑reset page
- User dashboard

Task 3: Testing (Test agent)
- Unit tests for models and routes
- Integration tests for API flow
- E2E tests for full user journey

Task 4: Security review (Security agent)
- Code security audit
- Vulnerability scan
- Best‑practice check

Execute tasks 1‑3 in parallel; run task 4 after the others finish.

Step 3 – Launch Parallel Agents

In OpenClaw chat, instruct the main agent to start the three sub‑agents concurrently and label each workspace (backend, frontend, root).

"Please start Codex (backend), Claude (frontend), and Test agent in parallel. Run the security agent after the first three have merged and passed tests."

Step 4 – Monitor Progress

Ask the main agent for status updates (e.g., "How are the sub‑agents progressing? Any blockers?") or use OpenCode CLI commands such as opencode session list to view active sessions.

Step 5 – Result Integration

Backend agent:
✅ User model completed
✅ All APIs implemented
✅ Unit tests passed

Frontend agent:
✅ All pages built
✅ Form validation added
✅ Responsive design

Test agent:
✅ 20 unit tests written
✅ 5 integration tests written
✅ All tests passed

Security agent:
✅ Code review done
⚠️ Issues fixed: rate‑limit added, password strength increased

Integration complete – project ready for next phase.

Result Integration Methods

Main‑Agent Integration – all agents report to the main agent, which produces a consolidated report. Pros: consistency, quality control. Cons: main agent can become a bottleneck.

Automatic Merge – agents push changes to a Git repository; an automated pipeline merges them. Pros: high automation, traceability. Cons: possible merge conflicts.

Manual Review – agents create pull requests; humans review and merge. Pros: highest quality. Cons: requires manual effort.

Recommended strategy: small projects use main‑agent integration; medium projects combine automatic merge with manual review; large projects rely on manual review with staged merges.

Parallel Execution in Practice

Using Git Worktrees

Create separate worktrees for each agent and run the appropriate CLI command in each terminal.

# 1. Initialize repository
mkdir -p ~/projects/auth-system && cd ~/projects/auth-system
# (if not a repo) git init && git commit --allow-empty -m "init"

# 2. Create worktrees
git worktree add -b backend ~/projects/auth-backend main
git worktree add -b frontend ~/projects/auth-frontend main
git worktree add -b tests ~/projects/auth-tests main

git worktree list

# 3. Run agents in each worktree (example)
cd ~/projects/auth-backend && opencode run -m "model" "Develop backend API…"
cd ~/projects/auth-frontend && opencode run -m "model" "Develop frontend pages…"
cd ~/projects/auth-tests && opencode run -m "model" "Write tests…"

# 4. Merge back to main
cd ~/projects/auth-system
git checkout main
git merge backend
git merge frontend
git merge tests

Monitoring Parallel Execution

If OpenClaw runs background PTY tasks, use process action:list, process action:log, or process action:poll to inspect progress.

Common Issues and Solutions

Issue 1 – Agent Output Conflict

Two agents modify the same file. Resolve by inspecting git diff, then ask the main agent to coordinate a merge according to the project’s AGENTS.md conventions, or manually abort and redo the merge.

Issue 2 – Agent Stalls

When an agent is unresponsive, query the main agent for its task status or check the terminal where the CLI is running. If needed, terminate the session and restart the task.

Issue 3 – Inconsistent Results

Define coding standards in AGENTS.md (e.g., ESLint/Prettier config) and ask the main agent to enforce them, or run npm run lint -- --fix and npm run format in the repository.

Best Practices

Clear Division of Labor – assign each specialist to a distinct responsibility (e.g., Codex → backend, Claude → frontend, Test agent → testing).

Define Interfaces – specify contracts such as "backend provides REST API, frontend consumes it".

Set Checkpoints – require agents to report progress after each stage.

Enforce Uniform Style – use a shared linting/formatting configuration.

Avoid Over‑Parallelism – limit concurrent agents to a manageable number (e.g., 3‑5).

Maintain Coordination – ensure a main orchestrator synchronizes agents.

Respect Dependencies – schedule tasks so that dependent work (e.g., frontend) starts after its prerequisites (e.g., backend API) are ready.

Summary

Multi‑agent collaboration turns a monolithic AI workflow into a fast, high‑quality development pipeline by decomposing tasks, assigning specialist agents, running them in parallel, and integrating results through a main orchestrator, automated merges, or manual review. Key takeaways are the importance of precise task breakdown, explicit role assignment, consistent coding standards, continuous communication, and appropriate monitoring and troubleshooting mechanisms.