How I Built MLAIEL: A Modular Monolith for Human-AI Collaborative Engineering in Next.js 16

Description and use

MLAIEL implements a secure modular monolith where the UI is separated from database and AI SDK access, ensuring author anonymity and readiness for autonomous AI agent integration.

Problem

Developing a scalable, secure platform that integrates human and autonomous AI agents for engineering tasks while preventing direct database or SDK access from the UI.

Outcome

MLAIEL achieves a secure, predictable request flow by isolating UI from data access and AI SDKs, enabling anonymous contributions and preparing for third‑party autonomous AI agent integration.

Workflow

• 1. User interacts with a browser form or UI component.
• 2. Interaction triggers a server action marked with "use server".
• 3. Server action passes through a rate limiter and Zod validation.
• 4. Validated request is handled by the core service layer, which contains business rules and AI orchestration logic.
• 5. Core service communicates with the database or third‑party interfaces via Prisma and DTOs.
• 6. Data is returned to a client component for rendering, ensuring anonymous authors remain untraceable.

Evidence

Instead of a scattered microservices architecture, I built it as a robust modular monolith using Next.js 16 App Router, React 19, TypeScript, and Prisma 7 with PostgreSQL.

Source

To maintain a predictable request path, components are strictly dumb UI layers. Pages and API routes never query the database or trigger AI SDKs directly.

Source

Everything follows a one‑way architectural flow: Browser Form/Interaction └──> Server Action ("use server") └──> Rate Limiter & Zod Validation └──> Core Service Layer (Business Rules & AI Orchestration) └──> Database / Third‑Party Interface └──> Data Transfer Object (DTO) ──> Client Component ensuring that anonymous post authors remain 100% untraceable, even through metadata.

Source