{
  "id": 13696,
  "url": "https://arxiv.org/abs/2607.22400v1",
  "title": "A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation",
  "summary": "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",
  "authors": "Fin Gentzen, Marla Grunewald, Iulisloi Zacarias, Mounir Bensalem, Admela Jukan",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-24T15:21:54.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13696",
  "original_url": "https://arxiv.org/abs/2607.22400v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}