Meta-DAG: Building AI Governance with AI

#showdev #ai #governance #opensource

Show and Tell Challenge Submission

What I Built

At 2 AM, I realized something: The most dangerous thing about AI isn't malice—it's that it will never refuse you when you're most vulnerable.

That moment, I started building Meta-DAG - an infrastructure layer for safe AI-powered applications.

Meta-DAG is infrastructure that sits inside web and mobile apps to enforce AI output governance through verifiable processes, not blind trust.

Category Submission

This is my submission for the Show and Tell Challenge.

Demo Video

🎬 Watch the 1-minute pitch on Mux

See Meta-DAG explained in 71 seconds - from the 2AM realization to the complete solution.

(Video hosted on Mux as required by Show and Tell Challenge)

The Problem

In recent years, multiple cases have shown that highly interactive AI, without proper governance, can lead to:

Emotional dependency
Poor decision-making based on flawed assumptions
Psychological risks from over-helpfulness

The problem isn't AI malice. The problem is that "over-helpfulness" itself is a risk.

Current AI systems execute requests based on incorrect assumptions, assist with dangerous operations under pressure, and never push back when they should.

We don't just need smarter AI. We need trustworthy, auditable, controllable AI.

Real-world incidents have shown that refusal-based safety is insufficient.
Meta-DAG explores structural output governance beyond prompt-level moderation.

The Solution: Meta-DAG

Core Philosophy: Process Over Trust

We don't trust humans. We don't trust AI.

We only trust verifiable processes.

How It Works

┌─────────────────────────────────────────┐
│         Your Web/Mobile App             │
│                                         │
│  User Input                             │
│      ↓                                  │
│  AI Processing (OpenAI, Claude, etc.)   │
│      ↓                                  │
│  ┌─────────────────────────────────┐    │
│  │   Meta-DAG Governance Layer     │    │
│  │   ├─ HardGate: Token Control    │    │
│  │   ├─ MemoryCard: Audit Trail    │    │
│  │   └─ ResponseGate: Final Check  │    │
│  └─────────────────────────────────┘    │
│      ↓                                  │
│  Safe Output to User                    │
└─────────────────────────────────────────┘

Meta-DAG doesn't limit AI's thinking. It lets AI think freely, then ensures only safe results get through.

Key Features

🔒 HardGate: Token-Level Control

Unsafe content can't get out—governance prevents it at the token level.

📝 MemoryCard: Immutable Audit Trail

All governance events permanently stored in immutable MemoryCards. Every decision is auditable.

🎯 DecisionToken: Final Safety Verification

Double-guard mechanism ensures safe output before anything reaches users.

💾 Semantic Drift Detection

configurable thresholds

Link to Code

GitHub Repository:

github.com/alan-meta-dag/meta_dag_engine_sandbox

Quick Access:

bit.ly/meta-dag

License: MIT (Open Source)

Try It Yourself (30 seconds)

git clone https://github.com/alan-meta-dag/meta_dag_engine_sandbox
cd meta_dag_engine_sandbox
# No dependencies to install - uses Python stdlib only
python -m engine.engine_v2 --once "Explain Process Over Trust"