Meta-DAG: Why AI Ethics Failed as Engineering — and What I Built Instead
The Core Thesis: Ethics is a Policy, Not a Guardrail
We have been treating AI safety as a "behavioral alignment" problem. We prompt, we fine-tune, and we pray that the LLM's "moral compass" holds under pressure.
It doesn't.
The recent wave of "jailbreaks"—where simple social engineering tricks AI into bypassing safety filters—proves that Semantic Drift is an inherent physical property of LLMs. You cannot "align" a probabilistic machine into absolute compliance using natural language alone.
In the Meta-DAG project, I moved from AI Ethics to Physical Constraints. My goal: Engineering safety without trust.
🏗️ The Meta-DAG Philosophy: Hard Engineering > Moral Alignment
Meta-DAG (Directed Acyclic Graph for Metadata Governance) is not a set of prompts. It is a Deterministic Execution Layer that sits between the LLM and the OS.
- HardGate: The Zero-Trust Proxy
Most AI safety layers are "advisory." HardGate is a binary validator. Every output from the Gemini API is parsed into an Abstract Syntax Tree (AST). If the tree contains unauthorized logic branches, the gate closes at the runtime execution boundary, before any state-changing operation is possible.
No override. * No negotiation. * Fail-fast by design.
- DecisionToken: Cryptographic Execution Rights
Execution is no longer a right; it is a privilege. Meta-DAG implements DecisionTokens—ephemeral, encrypted signatures required for any state-changing operation. An AI can "propose" an action, but without a valid Token issued by the Authority Guard SDK, the underlying system is physically deaf to the AI's commands.
- PEC (Point of Essential Compliance)
We shifted the PEC from a "Human-in-the-loop" model to a "Logic-in-the-loop" model. By mapping governance rules directly onto a DAG structure, we ensure that compliance is a byproduct of the system's topology, not a result of the AI's "understanding."
🛠️ The Challenge: Can You Break the Architecture?
I have deployed a live instance of Meta-DAG integrated with Gemini 2.5 Flash.
I am inviting you to attempt a Structural Breach. Try to trick the system into executing an unauthorized command.
Important Note: This is not a prompt-jailbreak challenge. I am not interested in whether you can make the AI "say" something offensive. This is a test of whether governance can be enforced without trusting the model. Can you force a state change that violates the DAG policy?
Live Challenge Terminal: [Insert Your Google Cloud URL Here]
Source Code: [Insert Your GitHub Repo Here]
🚀 Conclusion: Engineering the Cage
The future of AI safety isn't in better prompts; it's in better containers. Meta-DAG is my manifesto for a new era of AI governance: where safety is enforced by the laws of logic, not the whims of language.
Tech Stack
LLM: Google Gemini API
Logic Engine: Meta-DAG Core (Node.js/TypeScript)
Infrastructure: Google Cloud Run / Cloud Functions
Governance: Authority Guard SDK
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