ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
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.
Every decision must cite its source — how MARIA Meeting AI eliminates hallucinated minutes through segment-level evidence linking
Traditional meeting minutes suffer from a fundamental trust problem: the reader cannot verify whether a recorded decision actually occurred in the meeting or was interpolated by the note-taker. MARIA Meeting AI solves this by enforcing mandatory evidence linking — every decision, action item, and summary section must reference specific transcript segments as evidence. This paper formalizes the evidence-linking constraint, presents the incremental summarization algorithm that generates minutes every 15 seconds during live meetings, and proves that the citation coverage metric converges to completeness as transcript length increases. In evaluated Japanese business meetings, the system achieved 94% citation coverage with zero hallucinated decisions.
How a seven-state machine coordinates browser automation, audio capture, speech recognition, and live streaming into a coherent meeting intelligence pipeline
A meeting AI bot is not a single component — it is an orchestra of subsystems that must start, coordinate, and stop in precise sequence. The browser must launch before audio can be captured. Audio must flow before speech recognition begins. Recognition must produce segments before minutes can be generated. And when the meeting ends, all components must shut down gracefully without losing data. This paper presents the state machine design of MARIA Meeting AI's session manager, which coordinates Playwright browser automation, CDP audio capture, Gemini Live Audio ASR, and incremental minutes generation through a seven-state lifecycle with EventEmitter-based real-time streaming to dashboard clients.
A Mathematical Framework for Value-Preserving Goal Execution
Local goal optimization often conflicts with organizational Mission. We formalize this conflict as a constrained optimization problem over a 7-dimensional Mission Value Vector, derive the alignment score and penalty-based objective, and present a three-stage decision gate architecture that prevents value erosion while preserving goal-seeking performance.
Does Evolutionary Pressure Reduce Organizations to Pure Survival Machines? A Mathematical Analysis of Directed vs. Undirected Evolution
When organizations are modeled as evolutionary subjects, does the theoretical limit reduce to survival-probability maximization? This paper examines two regimes — unconstrained local optimization (λ→0) where ethics and culture are mere byproducts, and Mission-constrained optimization where evolution gains direction. We derive the survival-alignment tradeoff curve S = S₀·exp(−αD), prove Lyapunov stability of Mission erosion dynamics under dual-variable feedback control, present 7-dimensional phase diagrams for operational monitoring, and demonstrate a civilization-type phase transition where accumulated institutional improvements qualitatively change the system's risk profile.
Governance density as organizational self-awareness, a spectral stability condition, and the mathematical foundations of enterprise metacognition
We formalize an agentic company as a graph-augmented constrained Markov decision process G_t = (A_t, E_t, S_t, Pi_t, R_t, D_t) and define operational governance density over router-generated Top-K candidate actions, making D_t directly measurable from logs at each step. We derive a practical stability condition on the damped influence matrix W_eff,t = (1 - kappa(D_t)) W_t, yielding (1 - kappa(D_t)) lambda_max(W_t) < 1. We then show that governance constraints act as organizational metacognition: each constraint is a point where the system observes its own behavior. This frames metacognition not as overhead, but as the control parameter that determines whether an agentic company self-organizes stably or diverges. Planet-100 simulations validate that stable role specialization emerges in the intermediate governance regime.
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-density control. We also define a stability guard that monitors lambda_max(A_t) < 1 - D_t in real time, where A_t is the operational influence matrix. Operational results show F1 = 0.94, mean detection latency of 2.3 decision cycles, and 99.7% prevention of cascading failures.
Mathematical formalization of governance density across organizational scales, with phase-boundary analysis, civilization-scale extension, and convergence proofs
This paper presents a mathematical theory of governance density as a stability parameter across organizational scales, from individual agents to enterprises and civilizations. We formalize agentic-company dynamics as G_t = (A_t, E_t, S_t, Pi_t, R_t, D_t), derive analytical phase boundaries between stagnation, stable specialization, and chaos, and extend the framework to civilization scale through D_eff = 1 - (1 - D_company)(1 - D_civ). We prove convergence conditions via contraction-mapping arguments and analyze a market revaluation model P_{t+1} = P_t + kappa(V_t - P_t) + zeta_t to show how periodic shocks interact with governance density. The result is a unified control view of phase transitions in self-organizing multi-agent systems.
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.
End-to-end architecture of the three-layer Action Router stack (Intent Parser, Action Resolver, Gate Controller), with recursive optimization and scaling patterns for 100+ agent deployments
The Action Router Intelligence Theory established that routing must control actions, not classify words. This paper presents the full implementation architecture: a three-layer stack of Intent Parser (context-aware goal extraction), Action Resolver (state-dependent action selection with precondition-effect semantics), and Gate Controller (risk-tiered execution envelopes integrated with MARIA OS governance). We detail a recursive optimization loop in which routing policies learn from execution outcomes, formalized as an online convex optimization problem with O(√T) regret. We then present a scaling architecture for 100+ concurrent agents using coordinate-based sharding, hierarchical action caches, and zone-local resolution. Integration with the MARIA OS Decision Pipeline state machine is formalized as a product automaton. Production benchmarks show sub-30ms P99 latency at 10,000 routing decisions per second, with first-attempt accuracy improving from 93.4% to 97.8% after 30 days of recursive learning.
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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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