# The Vision of Autonomic Computing: Can LLMs Make It a Reality?

arXiv: https://arxiv.org/abs/2407.14402 · HTML: https://mlsanity.com/paper/2407.14402/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 55 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [TaskWeaver: A Code-First Agent Framework](https://mlsanity.com/paper/2311.17541/) (2023)
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://mlsanity.com/paper/2305.15778/) (2023)
- [Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides](https://mlsanity.com/paper/2402.17531/) (2024)
- [GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation](https://mlsanity.com/paper/2405.19954/) (2024)
- [Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching](https://mlsanity.com/paper/2308.09960/) (2023)
- [Developing Self-Adaptive Microservice Systems: Challenges and Directions](https://mlsanity.com/paper/1910.07660/) (2019)
- [AID: Efficient Prediction of Aggregated Intensity of Dependency in Large-scale Cloud Systems](https://mlsanity.com/paper/2109.04893/) (2021)
- [Self-adaptive, Requirements-driven Autoscaling of Microservices](https://mlsanity.com/paper/2403.08798/) (2024)

## Built on by (most cited first, 4 of 4)

- [AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents](https://mlsanity.com/paper/2409.17140/) (2024)
- [AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure](https://mlsanity.com/paper/2409.17642/) (2024)
- [KORAL: Knowledge Graph Guided LLM Reasoning for SSD Operational Analysis](https://mlsanity.com/paper/2602.10246/) (2026)
- [Enabling Autonomic Microservice Management through Self-Learning Agents](https://mlsanity.com/paper/2501.19056/) (2025)

## Abstract (arXiv)

The Vision of Autonomic Computing (ACV), proposed over two decades ago, envisions computing systems that self-manage akin to biological organisms, adapting seamlessly to changing environments. Despite decades of research, achieving ACV remains challenging due to the dynamic and complex nature of modern computing systems. Recent advancements in Large Language Models (LLMs) offer promising solutions to these challenges by leveraging their extensive knowledge, language understanding, and task automation capabilities. This paper explores the feasibility of realizing ACV through an LLM-based multi-agent framework for microservice management. We introduce a five-level taxonomy for autonomous service maintenance and present an online evaluation benchmark based on the Sock Shop microservice demo project to assess our framework's performance. Our findings demonstrate significant progress towards achieving Level 3 autonomy, highlighting the effectiveness of LLMs in detecting and resolving issues within microservice architectures. This study contributes to advancing autonomic computing by pioneering the integration of LLMs into microservice management frameworks, paving the way for more adaptive and self-managing computing systems. The code will be made available at https://aka.ms/ACV-LLM.