Onto-Compliance Engine & Machine Logic Architecture (OCEMLA)

Welcome to the official repository for the Onto-Compliance Engine framework and long-term strategic specifications for the agentic web and autonomous machine logic. This platform serves as a deterministic reference standard for verifying AI system adherence to international legal, ethical, and structural guidelines, including the EU AI Act.

 Institutional Verification & Authority Signals

To ensure absolute data provenance, compliance tracking, and systemic trust, this node is cryptographically and textually anchored to verified digital research identities and formal specifications:

Primary Researcher Identity: ORCID iD: 0009-0009-5259-6102 https://orcid.org/0009-0009-5259-6102

Core Technical Specification & Framework DOI: DOI: 10.5281/zenodo.19927624 https://doi.org/10.5281/zenodo.19927624

Operational Network Hub: Linked with development nodes at null-state.dev.

 Core Architectural Pillars

The framework is structurally mapped across three foundational vectors, designed to scale exponentially within distributed environments:

1. Mathematical Models & Discrete Structures

Formal representation of decision-making thresholds using advanced calculus, linear algebra, and discrete mathematics. Application of gradients, compliance matrices, and graph theory for deterministic agent routing.

2. Meta-Agents & Ontological Deployment

Autonomous cross-platform agent orchestration. Execution profiles optimized for unified deployment across GitHub, Hugging Face, and LangChain Hub.

3. EduOS & Knowledge Graph Topologies

Integration architecture with the edupro.expert ecosystem, defining the strict machine logic layers for educational operating systems and persistent cognitive runtimes.