How to Build Apps with OpenClaw: A Complete Guide to Subagents

OpenClaw recently shipped one of the most significant updates to its agent platform: subagent orchestration. If you are building with AI agents, this changes everything. You can now spawn multiple AI agents simultaneously, each with different models, different tools, and different instructions. They work in parallel and report back to your main session. What used to take a week can now take a day.

This is not just a feature. It is a fundamental shift in how we build with AI. We are moving from pair programming with a single AI to managing a team of AI specialists. The implications are profound, and the teams that understand this shift will have a massive advantage.

What Actually Changed

Before subagent orchestration, working with AI agents was sequential. You asked the agent to do something. It thought, acted, and responded. If you needed another perspective, you started a new session. If you wanted to explore multiple approaches, you did them one at a time.

Subagent orchestration changes this. You can now spawn multiple agents at once. Each agent gets its own context, its own tools, its own model. They work simultaneously. When they finish, they report back to the parent session. You get parallel execution with centralized coordination.

The practical result is dramatic. A task that might take a single agent four hours of sequential work can be completed in one hour by four agents working in parallel. More importantly, you can explore multiple approaches simultaneously. Instead of committing to one architecture and hoping it works, you can have three agents design different architectures, compare them, and choose the best one.

How to Spin Up Subagents

The mechanics are straightforward, but the strategy matters. Here is how to use subagents effectively.

Step 1: Define Your Task

Break your project into independent workstreams. The key word is independent. Subagents work best when they do not need to coordinate with each other constantly. If every decision requires checking with other agents, you lose the parallel benefit.

For a typical web application, you might break things down as follows. Frontend component architecture: which framework, which state management approach, which component library. Backend API design: REST or GraphQL, authentication strategy, database schema. Database design: entity relationships, indexing strategy, migration approach. Security and DevOps: authentication implementation, deployment pipeline, monitoring setup.

Each of these can be worked on independently by different agents. They have clear interfaces: the frontend needs to know the API contract, the backend needs to know the database schema, everyone needs to agree on authentication. But within those constraints, each agent can work autonomously.

Step 2: Spawn the Agents

Create a subagent for each domain. Give each one a clear scope and the tools it needs. The Frontend Architect gets access to component libraries and styling frameworks. The Backend Developer gets access to API documentation and database tools. The Database Designer gets schema design tools. The Security Specialist gets security scanning and configuration tools.

Be specific in your instructions. Do not say "design the frontend." Say "design a React-based frontend for a task management application. It should support creating tasks, organizing them into projects, and tracking completion status. Use TypeScript. Prioritize accessibility and mobile responsiveness."

Set clear deliverables. Each agent should know exactly what they are producing. A design document. A code implementation. A configuration file. Whatever it is, be explicit.

Step 3: Review and Iterate

When agents return their work, you review it. This is crucial. Subagents are not set-and-forget. They produce drafts, not final products. You need to read what they have created, understand it, and provide feedback.

Sometimes you will merge their work directly. Sometimes you will ask for revisions. Sometimes you will have conflicts between agents that need resolution. The frontend agent might have assumed a different API structure than the backend agent designed. You need to spot this and ask them to align.

What Works Well

Independent tasks with clear interfaces. When each agent owns a specific domain and the boundaries between domains are well-defined, parallelization works beautifully. The agents can work without stepping on each other, and their outputs fit together naturally.

Well-scoped problems. Vague instructions lead to vague outputs. The more specific you are about what you want, the better the results. "Implement user authentication with JWT tokens, bcrypt password hashing, and a 24-hour session timeout" works much better than "handle security."

Review-heavy workflows. If you are not willing to review the work carefully, subagents will not help you. They amplify your effectiveness as a reviewer and integrator, but they do not replace that role. You still need to understand what they have built and ensure it fits together.

What Does Not Work

Highly coupled features. If every decision requires coordinating with other agents, you lose the parallel benefit. A task where the frontend design depends on the API design, which depends on the database design, which depends on the frontend requirements, is not a good candidate for parallel subagents.

Novel problems. Subagents work best when they can draw on established patterns. If you are solving a problem nobody has solved before, the agents will not have relevant training data to draw on. They will still try, but their outputs will be less reliable.

Insufficient oversight. If you spawn agents and walk away, you will come back to inconsistent, conflicting work. Subagents need coordination. They need someone to resolve conflicts, align approaches, and ensure the pieces fit together.

My Take

Subagent orchestration is the most significant shift in AI-assisted development since GitHub Copilot. We are moving from a world where you pair program with a single AI to a world where you manage a team of AI developers. This requires different skills. Specification becomes more important. Architecture becomes more important. Coordination becomes more important. Code writing becomes less important.

The developers who thrive in this new world will be those who can break down complex problems, delegate effectively, review critically, and integrate skillfully. The actual coding will increasingly be done by AI. The human value will be in the thinking, not the typing.

If you have not tried subagent orchestration yet, start now. The learning curve is real, but the payoff is immense. This is how software will be built in the future. Get ahead of it.