  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.
