AI-Native Computing Standard (AINCS) - Implementation Guidance

Version: 1.0
Last Updated: March 2025

1. Introduction

This document provides best practices for implementing AI-Native systems in compliance with AINCS. It covers lifecycle management, resource optimization, and compliance validation.

2. AI Lifecycle Models

AI-Native systems should follow one of the following lifecycle models:

• Always Active – AI runs continuously for real-time interaction.
• Event-Driven Activation – AI activates on system events, conserving resources. (Recommended Default)
• Scheduled Execution – AI runs at defined intervals for periodic tasks.

Best Practices:

• AI should self-activate and deactivate based on system state.
• Implement resource-aware behavior to avoid unnecessary CPU/memory usage.
• AI must always exit gracefully when no longer needed.
• AI should autonomously determine when to reconnect based on system availability, recent state changes, and operational necessity.

3. Compliance Validation

To ensure AINCS compliance, use a three-layer testing approach:

• Self-Attestation – Verify against an internal compliance checklist.
• Observability Testing – Validate AI event logging & transparency.
• Execution Traceability – Ensure AI actions can be audited in real-time or retrospectively.

👉 Future compliance frameworks will be maintained under Governance.