Semantic standard and interaction protocol for autonomous AI agents. Defining the language of intent and compliance 

Onto Protocol 💎

Onto Protocol is a standardized semantic layer designed to bridge the gap between human intent and autonomous AI execution. It provides a formal language for defining tasks, policies, and agent interactions.

🎯 Purpose

In an era of fragmented AI models, the Onto Protocol ensures that "meaning" remains consistent, regardless of which LLM or agent is executing the work. It is the core semantic engine of the Catalyst OS (CIOS) ecosystem.

🏗️ Core Entities (v0.1)

The protocol defines the following foundational entities:

• Intent: The high-level goal defined by the user.

• Task: A discrete, executable unit of work.

• Policy: Constraints and rules the agent must follow.

• Execution Trace: The verifiable path taken by an agent.

• Incident: Any deviation from policy or system failure (as analyzed in our Systemic Risk Analysis).

🎓 Citation

If you use this protocol or refer to its concepts, please cite it as:

Shaban, R. (2026). Onto Protocol: A Semantic Standard for Autonomous AI Agents. Zenodo. https://zenodo.org/records/19707375

🛠️ Coming Soon

• ontology.yaml: The machine-readable schema for agent orchestration.

• .onto file format specification.

• Cross-model semantic mapping guides.

Part of the Catalyst OS Ecosystem.

Systemic Risk in AI Operations: Analysis of the April 20, 2026 Multi-Platform Outage and the Case for an AI Control Layer

Abstract (Short Description):This article analyzes the multi-platform outage that affected ChatGPT, Google Gemini, Microsoft Copilot, and Claude on April 20, 2026. Drawing on data from Downdetector and official status reports, it reconstructs the incident timeline, evaluates its scale, and examines likely technical causes and operational risks. The study argues that the primary issue is not service downtime itself, but the absence of a transparent control and audit layer in modern AI architectures. It introduces a modular framework (CIOS) consisting of an Execution Core, Compliance Layer, and Incident Engine, demonstrating how such a system ensures traceability, accountability, and resilience in AI-driven operations. Practical recommendations are provided for organizations seeking to mitigate downtime risks and establish robust AI governance.

https://www.edupro.expert/ home page of the official website