A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation

As LLM agents move into autonomous, tool-executing roles, reliability suffers from lack of ground truth and operational drift in open-ended environments. This framework introduces a self-calibration mechanism using an ARIMA forecaster to approximate ground truth without continuous human oversight, applied to profiling zero-knowledge workload resource usage in edge computing networks. It improved resource-prediction accuracy by 91.7% and prediction speed by 71.7% over baseline agents, while a novel ARIMA leaping algorithm ran 52% faster than standard ARIMA. Applications include decentralized infrastructure management, edge computing resource allocation, and building drift-resistant autonomous AI systems for infrastructure operations. Authors: Fin Gentzen, Marla Grunewald, Iulisloi Zacarias, Mounir Bensalem, Admela Jukan Paper: https://arxiv.org/abs/2607.22400v1