Your AI Dev Team: Ship Products 10x Faster with OpenClaw Sub-Agents

Describe what you want to build in plain English. Your AI dev team picks the right tools, pulls a boilerplate, writes the code, runs tests, fixes bugs, and deploys. You go from idea to live product in hours, not weeks. This isn't science fiction — it's how OpenClaw's sub-agent architecture works right now.

The Sub-Agent Model

OpenClaw doesn't just use one AI for everything. It can spawn sub-agents — dedicated AI sessions that run independently on specific tasks. Think of it as a team:

• Lead agent — Your main OpenClaw instance. It understands the project, breaks it into tasks, and coordinates.
• Coding agents — Claude Code, Codex, or other specialized models that write and debug code.
• Review agent — Checks code quality, runs tests, catches issues.
• Deploy agent — Handles builds, pushes to production, monitors errors.

Each sub-agent runs in its own session with its own context. The lead agent assigns work, monitors progress, and escalates problems.

A Real Example

You say to your agent via Telegram:

"Build me a landing page for a SaaS product called MetricFlow. It tracks business KPIs with AI-powered insights. Next.js, Tailwind, deploy to Vercel."

Here's what happens:

Minute 0-2: Lead agent breaks this into tasks:

1. Scaffold Next.js project with Tailwind
2. Build hero section with headline, subheadline, CTA
3. Build features section (3 feature cards)
4. Build pricing section (3 tiers)
5. Build footer with links
6. Add responsive design
7. Deploy to Vercel

Minute 2-15: Coding agent spawns, works through each component. It has access to:

• Pre-loaded boilerplates (Next.js starter, component libraries)
• Your design preferences (from SOUL.md: "clean, minimal, lots of whitespace")
• Reference sites you've specified

Minute 15-20: Review agent checks the output:

• Does it build without errors?
• Is it responsive?
• Are there accessibility issues?
• Does it match the spec?

Minute 20-25: Deploy agent runs vercel --prod, gets the live URL.

Minute 25: You get a Telegram message:

"MetricFlow landing page is live: metricflow.vercel.app — Hero, features, pricing, footer. Mobile responsive. Let me know if you want changes."

25 minutes. No terminal. No code editor. Just a conversation.

The Technical Architecture

Spawning Sub-Agents

OpenClaw's sessions_spawn creates isolated sessions:

sessions_spawn(
  task: "Build the hero component for MetricFlow...",
  mode: "run",        // one-shot task
  model: "sonnet",    // cost-effective for coding
  cleanup: "keep"     // preserve files after completion
)

The sub-agent gets its own workspace, its own context, and runs independently. When it finishes, results flow back to the lead agent.

Pre-Loaded Boilerplates

The real power is in preparation. Your workspace includes:

boilerplates/
├── nextjs-saas/          # Full SaaS starter
├── landing-page/         # Marketing page template  
├── api-starter/          # REST API boilerplate
├── chrome-extension/     # Browser extension template
└── mobile-expo/          # React Native starter

When you say "build a SaaS dashboard," the agent picks the right boilerplate and starts from 80% done instead of zero.

Coding Agent Configuration

Each coding tool has optimized settings:

## Claude Code Settings
- Model: claude-sonnet-4-5 (fast, good at code)
- Context: full project files + package.json + README
- Style: functional components, TypeScript strict, minimal dependencies
- Testing: write tests alongside implementation
- Commits: meaningful messages, small atomic commits

The Coordination Layer

The lead agent manages everything:

1. Breaks project into sequential/parallel tasks
2. Assigns each task to the right sub-agent
3. Monitors progress (no polling loops — push-based completion)
4. Reviews output and requests fixes if needed
5. Handles deployment when all tasks pass

What You Can Build

Projects people have shipped with this setup:

• Landing pages — 20-30 minutes from description to live URL
• Chrome extensions — Simple ones in under an hour
• API backends — CRUD APIs with auth, database, and docs
• Shopify apps — Custom functionality, submitted to app store
• Internal tools — Dashboards, admin panels, data pipelines
• Mobile apps — Expo/React Native, basic functionality

What It Can't Do (Yet)

Let's be honest about limitations:

• Complex UI design — It builds functional UIs, not award-winning designs. You'll want a designer for pixel-perfect work.
• Large-scale architecture — Great for MVPs and features. Not great for architecting a distributed system from scratch.
• Domain-specific logic — If your product needs deep domain knowledge (medical, legal, financial), the agent needs detailed specs.
• Debugging production issues — It can fix code it wrote. Debugging legacy spaghetti code is harder.

The sweet spot: MVPs, prototypes, landing pages, internal tools, and new features on existing projects. Things that are well-defined and can be broken into clear tasks.

The Economics

Traditional development timeline for a landing page:

• Freelancer: $500-2000, 1-2 weeks
• Agency: $5000+, 3-4 weeks
• DIY (no-code): $0, 2-3 days of your time

AI dev team:

• Cost: ~$2-5 in API calls
• Time: 20-30 minutes
• Quality: Good enough to ship, iterate from there

For MVPs and prototypes, the ROI is insane. Ship fast, validate with real users, then invest in polish.

Setting It Up

The full AI dev team setup requires:

1. Coding tools installed — Claude Code (claude), optionally Codex
2. Boilerplate library — Pre-made project starters in your workspace
3. Deploy pipeline — Vercel CLI, or SSH to your server
4. Lead agent configuration — Task breakdown logic in SOUL.md
5. Review standards — What "done" means (builds, passes lint, responsive)

Or grab the Dev Team persona on Lobsterlair — coding tools pre-configured, boilerplates loaded, deploy pipeline ready. Describe what you want to build, and let your team handle it.

The best code is the code you didn't have to write yourself.