The Adversarial Grid: Why Your AI Needs a Red Team
Trust comes from verification. We built a dedicated node whose only job is to try to break the system.
In most AI deployments, the model is treated as an oracle. You ask a question, it gives an answer, and you trust it. This is dangerous. Models hallucinate. Models drift. Models can be manipulated.
At Active Mirror, we don't trust the model. We trust the Grid.
The M4 and the M1
Our standard deployment uses two primary nodes:
- Node 1 (M4 Max): The "Hub". Runs the primary heavy models (Llama 3 70B, Qwen 2.5 72B). It tries to be helpful.
- Node 2 (M1 Max): The "Adversary". Runs smaller, faster models (Llama 3.2 3B). It tries to be skeptical.
Every time the Hub generates a significant claim (marked as FACT), the Adversary wakes up. It runs the Truth Scanner Protocol. It checks the claim against the Vault. It checks the claim against known constraints. If it finds a discrepancy, it flags it.
Self-Healing Loops
When the Adversary flags an error, the system enters a "Self-Healing Loop". The Hub is forced to regenerate the response, this time with the Adversary's critique as context. It's an automated debate, happening in milliseconds, before the user ever sees the output.
This is "Governance as Code". We don't just prompt the model to be accurate. We architect the system to enforce accuracy.
Security by Design
The Adversary also runs Red Team attacks. It tries prompt injection. It tries to exfiltrate data. It tries to override kernel rules. Because it runs on a physically separate machine, even if the Hub is compromised, the Adversary remains a sentinel.
We believe this dual-node, adversarial architecture is the only way to safely deploy autonomous agents. You need a second pair of eyes, even if they are silicon.