MVP Studio using OpenClaw

01 / Brief

Solution

MVP Studio using OpenClaw is my internal system to ship MVPs faster with AI agents. Before this setup, I was building full apps during my free time and handling backend and frontend alone. I designed and implemented the workflow as a solo project: a self-hosted OpenClaw agent setup, a deterministic Python orchestrator, and a PM-style approval/escalation flow. The objective was to go from one prompt to a fully working app with minimal coordination overhead. The system worked technically, but I paused the project because coding was no longer the main bottleneck: marketing, launch, and deployment ownership still required significant manual work.

02 / Constraints

Problem

• Preventing race conditions between dev and review agents
• Keeping task dispatch deterministic instead of agent-driven
• Handling escalation cleanly when agents need product or infrastructure decisions
• Making conventions and review rules strict enough to improve with each MVP

03 / System

Architecture / workflow

• 5 specialized AI coding agents plus 1 deterministic Python orchestrator
• Telegram-connected PM workflow for approvals and escalations
• Single VPS setup costing about $6/month
• Around $10 in model usage per MVP
• Template-based project bootstrapping with IDEA, KANBAN, specs, and review loops

04 / Evidence

Results / impact

• ~$10 API cost per MVP
• One prompt to a working app
• Validated that delivery speed alone is not enough without marketing and go-to-market execution