mlsanityData to 7 Oct 2026

Engineering LLM Powered Multi-agent Framework for Autonomous CloudOps

arXiv 2501.08243 ↗First posted 14 Jan 2025cs.SE cs.AI cs.LGcited by 3

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Cloud Operations (CloudOps) is a rapidly growing field focused on the automated management and optimization of cloud infrastructure which is essential for organizations navigating increasingly complex cloud environments. MontyCloud Inc. is one of the major companies in the CloudOps domain that leverages autonomous bots to manage cloud compliance, security, and continuous operations. To make the platform more accessible and effective to the customers, we leveraged the use of GenAI. Developing a GenAI-based solution for autonomous CloudOps for the existing MontyCloud system presented us with various challenges such as i) diverse data sources; ii) orchestration of multiple processes; and iii) handling complex workflows to automate routine tasks. To this end, we developed MOYA, a multi-agent framework that leverages GenAI and balances autonomy with the necessary human control. This framework integrates various internal and external systems and is optimized for factors like task orchestration, security, and error mitigation while producing accurate, reliable, and relevant insights by utilizing Retrieval Augmented Generation (RAG). Evaluations of our multi-agent system with the help of practitioners as well as using automated checks demonstrate enhanced accuracy, responsiveness, and effectiveness over non-agentic approaches across complex workflows.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 19 references that are in the map. First posted on arXiv 14 Jan 2025.

Builds on

9 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug

Other references · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  5. 2024LLM Multi-Agent Systems: Challenges and Open Problemscited by 119
  6. 2021Software Engineering for AI-Based Systems: A Surveycited by 68
  7. 2024Building Guardrails for Large Language Modelscited by 61
  8. 2020Engineering AI Systems: A Research Agendacited by 37

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds
  2. 2024Building AI Agents for Autonomous Clouds: Challenges and Design Principles
  3. 2026AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis
  4. 2025Cloud Infrastructure Management in the Age of AI Agents
  5. 2025EcoAgent: An Efficient Device-Cloud Collaborative Multi-Agent Framework for Mobile Automation
  6. 2025Multi-Agent Code-Orchestrated Generation for Reliable Infrastructure-as-Code
  7. 2024Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications
  8. 2025Toward Agentic Environments: GenAI and the Convergence of AI, Sustainability, and Human-Centric Spaces