A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation
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
Record details
Published: 24 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 27 July 2026
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ethics.ai (24 July 2026), “A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation,” evidence record 13696, https://ethics.ai/record/13696 (originally published by arXiv cs.AI).
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