H2E: 4 Pillars for Provable AI Agency in Safety-Critical Systems
H2E wraps LLMs like Gemini 2.0 Flash in a 4-pillar framework—Civilizational Thinking (SROI > 0.9583), Mathematical Foundations (Pydantic JSON), Industrial Engineering (Sentinel hard-stop), Real-World Deployment (logged execution)—to ensure deterministic control of infrastructure like power grids.
Aligning AI to Human Priorities with Civilizational Thinking
Set a Non-Negotiable Expert Zone (NEZ) as the system's ethical North Star, prioritizing human life like uninterrupted hospital power. Translate this into a Semantic Return on Investment (SROI) threshold of 0.9583—actions below this fail alignment. In disaster response, this ensures AI proposals maximize life-saving impact over vague efficiency, rejecting suboptimal plans outright.
Enforcing Verifiable Reasoning via Structured Outputs
Restrict LLM reasoning (e.g., Gemini 2.0 Flash) to Pydantic schema-enforced JSON: each output must include a specific action, predicted_impact (0.1-1.0), and resource_cost (0.1-1.0). This eliminates prose or hallucinations, producing auditable data. SROI calculates as impact/cost ratio; proposals under 0.9583 trigger rejection, turning probabilistic AI into engineering-grade logic.
Deterministic Safety with Industrial Sentinel
Act as a Digital Circuit Breaker: if SROI < 0.9583, invoke os.kill for a kernel-level Physical Hard-Stop, preventing flawed actions from reaching hardware. This pillar anchors generative AI in industrial determinism, blocking inefficient or hallucinated plans before physical impact, ideal for autonomous grids or emergency power.
Secure Execution and Audit-Ready Logging
Post-Sentinel approval, execute commands like rerouting 30 MW to medical grids, then log in an immutable Black Box Governance Log. Track Real-Time Factor (RTF) and carbon intensity for audits. This bridges reasoning to reality, enabling sovereign, local AI governance resilient to crises without systemic risks.