Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks

As telecommunications networks evolve toward full autonomy (Levels 4-5), AI and machine learning agents are increasingly responsible for real-time network management, such as resource allocation and configuration adjustments. However, current standards lack a runtime mechanism to intercept and validate these AI-driven decisions before they are applied to the live network. This gap creates significant risks, as poisoned, compromised, or simply erroneous model outputs can cause catastrophic service disruptions, such as the accidental deactivation of cells serving emergency services.

The proposed GRV framework acts as a mandatory safety layer positioned between an AI agent's inference output and the network execution function. It evaluates every decision across six weighted dimensions: action scope, action type, service criticality, agent autonomy level, reversibility, and temporal behavioral patterns. Based on this evaluation, the framework assigns a criticality level (LOW, MEDIUM, HIGH, or CRITICAL) and applies a corresponding validation mechanism:

• LOW: Execute with logging.
• MEDIUM: Bounds checking against operator-defined safety ranges.
• HIGH: Independent validation by a secondary, diverse AI agent.
• CRITICAL: Multi-agent consensus (M-of-N) to ensure safety for high-impact actions.

Beyond individual decision validation, the GRV framework addresses multi-agent environments by detecting and resolving conflicts where different agents propose contradictory actions for the same network entity. It uses a priority-based resolution system that favors higher-criticality services (e.g., EMERGENCY over eMBB). Furthermore, the framework generates structured conformance logs for every decision, providing the transparency and auditability required by emerging regulations like the EU AI Act, which mandates human oversight and traceability for high-risk AI systems.