TAG ARCHIVE
gate-engine
4 MARIA OS blog articles tagged gate-engine, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Gated Meeting Intelligence: Fail-Closed Privacy Architecture for AI-Powered Meeting Transcription
Designing consent, scope, and export gates that enforce data sovereignty before a single word is stored
When an AI bot joins a meeting, the first question is not 'what was said?' but 'who consented to recording?' This paper formalizes the gate architecture behind MARIA Meeting AI — a system where Consent, Scope, Export, and Speak gates form a fail-closed barrier between raw audio and persistent storage. We derive the gate evaluation algebra, prove that the composition of fail-closed gates preserves the fail-closed property, and show how the Scope gate implements information-theoretic privacy bounds by restricting full transcript access to internal-only meetings. In production deployments, the architecture achieves zero unauthorized data retention while adding less than 3ms latency per gate evaluation.
Doctor Architecture: Anomaly Detection as Enterprise Metacognition in MARIA OS
Dual-model anomaly detection, threshold engineering, gate integration, and real-time stability monitoring for autonomous agent systems
The Doctor system in MARIA OS implements organizational metacognition through dual-model anomaly detection, combining Isolation Forest for structural outlier detection and an Autoencoder for continuous deviation measurement. We detail the combined score A_combined = alpha * s(x) + (1 - alpha) * sigma(epsilon(x)), threshold design (soft throttle at 0.85, hard freeze at 0.92), and Gate Engine integration for dynamic governance control. We also define a stability guard that monitors exact loop gain g_t = (1 - D_t) lambda_max(A_t) together with the conservative buffer delta_buffer,t = 1 - D_t - lambda_max(A_t) in real time. Operational results show F1 = 0.94, mean detection latency of 2.3 decision cycles, and 99.7% prevention of cascading failures.
Action Router Intelligence Theory: Why Routing Must Control Actions, Not Classify Words
From keyword detection to action-level control: a formal shift that recasts AI routing from text classification to governance-aware execution control
Traditional AI routers treat routing as text classification: extract keywords, map to categories, and dispatch handlers. For enterprise-grade agentic systems, this approach is often insufficient. We formalize the Action Router as a function R: (Context × Intent × State) → Action, replacing the naive R: Input → Category mapping. The Action Router integrates with the MARIA OS Gate Engine so responsibility is enforced at routing time, not retrofitted afterward. We formalize the action space, define precondition-effect semantics for routable actions, derive routing cost over feasible actions, and show in simulation that action-level routing reduces misrouting by 67%, cuts responsibility-attribution failures by 94%, and achieves 3.2x lower latency than semantic-similarity routing on enterprise decision workloads.
Action Router × Gate Engine Composition: Formal Theory of Responsibility-Aware Routing
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).