About Adam Stratmeyer
Adam Stratmeyer holds a Juris Doctor (J.D.) from the Knudson School of Law at the University of South Dakota. As Lead Researcher at the Observable Compute Foundation, he bridges the gap between legal compliance frameworks and advanced AI systems — working to ensure that AI decision-making remains transparent, auditable, and aligned with existing legal structures.
His core research areas include observable function in processing entities, agentic sovereignty (the legal and ethical standing of autonomous AI agents), and the Knowledge Gradient Framework — a substrate-independent model for understanding how systems learn and adapt through informational gradients. He has designed and reviewed over 300 IRB-compliant research protocols using the IRB GPT system, an AI-assisted compliance tool he developed to streamline institutional review board processes.
Prior to his current role, Adam built large-scale data ingestion pipelines for legal compliance models at Stratmeyer Analytica and provided strategic consulting on AI behavior mapping against privacy statutes. His work sits at the intersection of law, data strategy, and AI systems engineering — fields he believes must be developed in tandem to produce trustworthy artificial intelligence.
The Observable Compute Foundation
The Observable Compute Foundation is a research organization dedicated to advancing the study of observable function in processing entities. The Foundation develops frameworks that make complex AI decision-making processes understandable, legally defensible, and ethically sound. Through original research, published frameworks, and open-source initiatives, it works toward a future where powerful AI systems operate with both high autonomy and complete transparency.
Core research outputs from the Foundation include the Observable Function framework — which defines the conditions under which a processing entity's internal state and decision pathways can be accurately inferred by external observers — and the Knowledge Gradient Framework, which provides a unified cross-substrate model for intelligence and adaptation. Both frameworks have direct applications in AI governance, legal compliance, and the emerging field of agentic systems design.