ARM: Autonomous Remediation and Management With LLM Agents for Intent-Driven Control
Vasilis Avgerinos, Kostas Ramantas, Luis Alonso, Christos V. Verikoukis · IEEE Internet of Things Journal · 2025
The growing complexity of cloud-native, edge, and IoT infrastructures has made manual configuration, fault remediation, and lifecycle management increasingly unsustainable. Traditional automation techniques—such as rule-based logic or bespoke machine learning pipelines—struggle with adaptability and explainability in dynamic environments. Recent advances in Large Language Models (LLMs), however, have introduced new opportunities for autonomous, intent-driven infrastructure control. In this work, we present a closed-loop framework that integrates LLM agents for automated Root Cause Analysis (RCA) and mitigation of faults within cloud-edge and IoT systems. When SLA violations are detected, the agent identifies likely root causes and selects corrective actions—such as pod rescheduling, scaling, or configuration updates—executed via a Model Context Protocol (MCP) server exposing management tool functionalities through an API. This RCA-plus-mitigation loop enables fault handling that is both explainable and adaptive. We evaluate our system on a cluster running synthetic IoT workloads under emulated stressors using a reproducible benchmarking setup. Results show that the agent identifies SLA violations with 52.9% accuracy and mitigates 70.7% of them successfully. Notably, the agent incorporates validation steps to ensure system stability after interventions. These findings highlight the feasibility of LLMs for real-time infrastructure healing and their potential role in future AIOps workflows.