Plan, Build and Ship a Full-Stack App With Multi-Agent AI in TRAE IDE

Key Takeaways

• Plan before you generate. The first prompt asks the AI to turn a one-sentence idea into a software specification rather than to write code. TRAE returns a product requirements document with core features, a suggested stack (React + TypeScript + Tailwind front end, Node/Express + PostgreSQL back end), user stories, a database design, API endpoints and a phased roadmap.
• Cut scope conversationally. Instead of accepting the first PRD, Kubow asks the agent what is realistic for version one. It strips the plan back to an MVP — simple auth, habit management, daily tracking, streaks, weekly stats, no back end initially — which is what actually gets built first.
• Two development modes. IDE mode behaves like a traditional editor with AI assistance and per-change review. Solo mode is the autonomous path where you describe a goal and the agent plans, codes, tests, previews and prepares deployment. Multi-agent work requires solo mode.
• Custom agents are configured, not coded. A "brand UI developer" agent is created from a plain description of the brand palette; TRAE generates the agent's name, prompt and settings. That agent then handles all styling work while a separate built-in execution agent handles the back end — both running as concurrent tasks.
• MCP extends the agents. TRAE supports Model Context Protocol servers, letting agents reach custom APIs, databases, documentation and internal tooling beyond code generation. See our MCP glossary entry for how the protocol works.
• Safety defaults worth checking. Privacy mode keeps chats and snippets out of model training and leaves project files local; AI-generated terminal commands can be run in a sandbox. Both are opt-in settings, not assumptions — verify them before pointing an agent at real work.

The build sequence shown

1. Install TRAE IDE (macOS 12+, Windows 10/11 64-bit, or Linux x64/arm64 via Debian/RPM), optionally importing existing VS Code or Cursor settings.
2. Describe the app in the AI panel and ask for a software specification; approve or refine the generated PRD.
3. Ask for a task list to get a phased, step-by-step development plan.
4. Scaffold the front-end MVP with local storage — roughly 110 generated tasks, reviewed change by change.
5. Create a custom agent for brand styling; switch to solo mode.
6. Run the styling agent and a back-end agent as two parallel tasks.
7. Deploy through the Vercel integration (requires an existing Vercel account and granting read/write permission).