Agentic AI-driven high-reliability deterministic IPv6+ routing for low-altitude power economy networks

The rapid emergence of the power low-altitude economy, characterized by wide-area drone-assisted grid inspections and dynamic aerial dispatching, demands microsecond-level determinism and ultra-high reliability across complex air-ground communication networks. However, traditional time-sensitive networking (TSN) suffers from severe scalability limitations over extensive distances, while standard deterministic networking (DetNet) lacks the autonomous adaptability required to manage highly dynamic traffic and stochastic topological perturbations during low-altitude flight operations. To address these bottlenecks, this paper proposes an agentic artificial intelligence (AI)-driven, high-reliability deterministic Internet Protocol version 6 Plus (IPv6+) routing architecture tailored to power low-altitude-economy networks. The architecture combines explicit segment routing over IPv6 (SRv6) with agentic AI-enabled autonomous orchestration in an intelligent closed loop. Specifically, we formalize an intent translation function that maps high-level, multi-dimensional low-altitude dispatching requirements into segment identifier (SID) lists and uses periodic mapping to obtain an approximately linear hop count trend for the variable processing, scheduling, and queuing component of delay in the evaluated setting. To support continuous operation after unpredictable physical link disruptions, an agentic AI module performs real-time state prediction and autonomous path re-optimization alongside an intent-driven packet replication and elimination function (PREF). The evaluation compares intent-driven large-scale deterministic networking (Intent-LDN) with representative SRv6–DetNet hybrid and graph deep-reinforcement-learning approaches and examines the contributions of the AI controller, PREF, flexible ethernet (FlexE) isolation, and topology-independent loop-free alternate (TI-LFA) recovery. In the modeled 2000 km jitter scenario, Intent-LDN records a maximum end-to-end (E2E) jitter of 18.2 \(\) s, compared with 842.5 \(\) s for TSN. Pre-installed TI-LFA repair remains below 15 ms across the evaluated 20–200-node range, and the full agentic policy achieves a 99.9% overall intent-fulfillment ratio in the controller perturbation experiment.