REEGS — Enterprise Execution Governance System
Governance Before Execution
REEGS (RoboVisual Enterprise Execution Governance System) is an enterprise AI governance platform being developed by RoboVisual Ltd.
It provides an independent control architecture designed to validate organisational authority, enforce enterprise policies and govern the execution of AI-enabled operations.
Rather than relying on AI models to determine their own operational permissions, REEGS places governance within enterprise-controlled infrastructure, separate from the models themselves.
This approach allows organisations to introduce AI capabilities while retaining control over their existing systems, transactions and operational responsibilities.
Capability is not permission.
How REEGS Works
REEGS provides an independent governance control layer between AI processing and enterprise operations.
A request is submitted through the Governance Console and passed to the Governance Hub, where identity, organisational authority, applicable policies and execution permissions are evaluated.
Only operations that satisfy the required governance conditions are permitted to proceed. Requests that fail validation are blocked or referred for human oversight.
Each transaction is associated with a unique identifier, governance decision and audit record, providing traceability across the processing lifecycle.
The architecture is designed to extend these controls to multi-step enterprise workflows, with authority and policy revalidation at consequential execution points.
REEGS System Components
Working Prototype Architecture
Governance Console — macOS
A dedicated operator interface for submitting AI requests, capturing transaction information and displaying governance decisions, model responses, workflow information and audit records.
Governance Hub — Java Spring Boot
The central governance service responsible for processing requests, validating governance conditions, applying policies, determining permitted operations and coordinating access to AI services.
Identity and Authority Validation
The Governance Hub evaluates identity credentials, organisational roles and authority levels against applicable governance requirements before permitting operations.
AI Model Integration
REEGS has demonstrated end-to-end communication between its Governance Console, Governance Hub and an externally hosted AI model using Ollama.
The architecture separates model processing from enterprise governance decisions.
Transaction Tracking and Audit
Transactions receive unique identifiers, with governance decisions, processing outcomes and associated audit references recorded for traceability.
Human Oversight and Execution Controls
REEGS includes developing capabilities for human intervention, approval management and execution control. Further integration and validation are required before these capabilities can be presented as a production-ready enterprise system.
REEGS in Operation — Working Prototype
RoboVisual has developed a working REEGS prototype demonstrating governed communication between a macOS Governance Console, a Java Spring Boot Governance Hub and an external AI model.
The demonstration shows how enterprise governance decisions can be applied before model invocation, with transaction identification, processing information and audit evidence returned to the operator.
The prototype provides a practical foundation for extending REEGS into consequential enterprise workflows requiring additional execution controls and human oversight.
Screenshot 1 — Governance Console
REEGS macOS Governance Console — request classification, operator identity, authority and governance configuration.
Screenshot 2 — Governance Decision and Model Response
Governed AI transaction — approved request, model response, processing metrics and recorded completion.
Screenshot 3 — Human Oversight
REEGS human oversight interface — supervisory monitoring, workflow status, agent information and exception management.
Human Oversight and Execution Control
REEGS provides a dedicated supervisory interface for reviewing governance decisions, monitoring workflow status, examining transaction evidence and managing exceptions. The architecture separates AI-generated information from authorised enterprise execution, allowing consequential operations to be subjected to additional validation and human intervention before execution.
REEGS currently demonstrates pre-execution governance controls, but the full departmental and transaction-specific authorisation enforcement is still being hardened.