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

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Computer Science > Networking and Internet Architecture

arXiv:2607.22400v1 (cs)

[Submitted on 24 Jul 2026]

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

Authors: Fin Gentzen , Marla Grunewald , Iulisloi Zacarias , Mounir Bensalem , Admela Jukan

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Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents, transitioning from static conversational interfaces to dynamic systems capable of complex reasoning, tool execution, and decision-making. However, the operational reliability of these agentic AI systems is fundamentally challenged by the absence of reliable ground truth in open-ended environments and the risk of increasing operational drift over time. To address this challenge, we propose and experimentally evaluate an agentic AI framework, designed to enforce autonomous integrity within LLM-driven systems. We design a self-calibration mechanism that mitigates drift and dynamically approximates ground truth by incorporating an ARIMA forecaster, without requiring continuous human oversight. To demonstrate the effectiveness and reliability of our methodology, we apply it to the complex domain of profiling the resource usage of ze

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