How the Future of Systems Looks Like

LLMs and Model Context Protocol (MCP) are weaving intelligence into systems everywhere. This creates an immediate problem: security concerns around prompt injection and data leakage.

But what if we’re approaching this wrong? We keep bolting AI onto existing architectures, then wondering why they break.

Here’s my theory:

The future demands a two-layer system architecture. Not because it sounds elegant, but because it solves the fundamental paradox of AI integration.

Layer 1: User Integration Layer

A robust, predictable system like we build today. It handles user interactions through well-defined interfaces and enforces core business rules.

Layer 2: Intelligence Layer

This isn’t just “AI features.” It’s a completely decoupled plane of operation that supports, enhances, and eventually automates aspects of Layer 1.

The key insight? These layers must communicate through standardized events, not direct access.

Think of it this way: the intelligence layer becomes the new “admin” of the system. But unlike human admins who might get phished or make mistakes, this admin can only interact with the system through a strictly defined, immutable event store.

This event store becomes the single source of truth. The intelligence layer reads these events but cannot modify them. It processes them like a human would - pattern recognition, feature analysis, understanding context - without directly manipulating sensitive data.

The beauty of this approach is prompt injection becomes irrelevant. The AI can’t change the event structure because it’s standardized and immutable. It can only analyze and then request actions through defined tools - similar to how we use the MCP protocol today, but at a system architecture level.

This creates a paradoxical protection: by restricting how intelligence interacts with the system, we actually enable it to do more. It can analyze patterns across the entire event history, suggest optimizations, and even automate repetitive tasks - all without exposing vulnerable access points.

In parallel, we can run monitoring systems (potentially AI themselves) that scan these same event streams for anomalies or potential security issues.

What emerges is something fascinating: a self-learning software ecosystem that coaches and assists users without needing direct system access. It’s the promise of AI without the security nightmare - a system that can evolve and adapt while maintaining its structural integrity.

The contradiction is beautiful: by building more rigid boundaries between layers, we actually create more flexible, intelligent systems.