“Working on runtime governance and admissibility for agentic AI systems.” “By bounding cumulative state-transition magnitude, we prevent super-linear amplification in long-running agentic systems, converting exponential execution growth into predictable linear envelopes.” As the Founder and AI Systems Architect at Genesis AI Research, I specialize in developing deterministic runtime control layers for mission-critical AI systems. My work emphasizes execution-time governance, ensuring that AI inference is predictable, auditable, and cost-controlled without modifying models, training data, or weights. My expertise in admissible inference and first-principles systems engineering informs a unique approach to building runtime gates for probabilistic models, enabling their safe integration into deterministic systems. With decades of experience in broadcast engineering and startups, I focus on constructing safety architectures for sovereign AI, national security, and DOE-class scientific simulations. My current mission centers on pioneering innovative standards like Valid-FLOPS to advance AI’s role in deterministic infrastructure. Through collaboration and adherence to zero-failure operational principles, I aim to bridge the gap between probabilistic language models and deterministic systems for secure and efficient real-world applications.