ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
How action routing and gate control compose into a provably safe routing system where each routed action carries complete responsibility provenance
Enterprise AI systems face a core tension: routers must maximize throughput and decision quality, while gate engines must enforce safety constraints and responsibility boundaries. When these subsystems are implemented independently and stacked in sequence, interface failures emerge: routed actions can satisfy routing criteria but violate gate invariants, and gate rules can block optimal routes without considering alternatives. This paper presents a formal composition theory that unifies Gate operator G and Router operator R into a composite operator G ∘ R that preserves safety invariants by construction. We prove a Safety Preservation Theorem showing the composed system maintains gate invariants while maximizing routing quality inside the feasible safety envelope. Using Lagrangian optimization, we derive the constrained-optimal routing policy and show a 31.4% routing-quality improvement over sequential stacking, with zero safety violations across 18 production MARIA OS deployments (1,247 agents, 180 days).
Ingesting regulatory amendments as Policy Set deltas and verifying gate rule consistency through automated compliance checking
Regulatory environments can change faster than manual compliance workflows can absorb updates. When GDPR, CCPA, or Basel-related requirements shift, enterprises face propagation delays between rule publication and operational enforcement. This paper models regulatory updates as algebraic Policy Set deltas, defines a merge operation `P_{t+1} = P_t + DeltaP` with consistency checks, and presents a verification pipeline that detects conflicts between incoming and existing policy rules before deployment to production gates. Benchmarks over 847 regulatory amendments showed 99.2% consistency-verification accuracy with sub-200ms propagation latency.
Ensuring that AI underwriting agents preserve the judgment structure of human experts through formal logic inheritance and responsibility chain verification
When an AI agent takes over underwriting decisions, the organization is transferring expert judgment into algorithmic form, not only automating workflow. Without explicit preservation checks, key decision patterns can be simplified or drift over time. This paper introduces a responsibility-inheritance model that verifies whether AI underwriting agents preserve the logical structure of expert decision-making.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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