Building AI Agents for Autonomous Clouds: Challenges and Design Principles
This work tackles the problem of reducing human effort and improving resilience in cloud operations for IT professionals, but it is incremental as it lays groundwork rather than presenting a fully developed solution.
The paper addresses the lack of standardized frameworks for building AI agents to automate operational tasks in cloud services, such as fault localization and resolution, and proposes a prototype called AIOpsLab that shows promising results in orchestrating applications and handling faults.
The rapid growth in the use of Large Language Models (LLMs) and AI Agents as part of software development and deployment is revolutionizing the information technology landscape. While code generation receives significant attention, a higher-impact application lies in using AI agents for operational resilience of cloud services, which currently require significant human effort and domain knowledge. There is a growing interest in AI for IT Operations (AIOps) which aims to automate complex operational tasks, like fault localization and root cause analysis, thereby reducing human intervention and customer impact. However, achieving the vision of autonomous and self-healing clouds through AIOps is hampered by the lack of standardized frameworks for building, evaluating, and improving AIOps agents. This vision paper lays the groundwork for such a framework by first framing the requirements and then discussing design decisions that satisfy them. We also propose AIOpsLab, a prototype implementation leveraging agent-cloud-interface that orchestrates an application, injects real-time faults using chaos engineering, and interfaces with an agent to localize and resolve the faults. We report promising results and lay the groundwork to build a modular and robust framework for building, evaluating, and improving agents for autonomous clouds.