Imagine a cloud that fixes itself. No more frantic late-night calls for site reliability engineers, no more cascading failures bringing down critical services. This is the promise of autonomous clouds, a vision driven by the rise of AI for IT Operations (AIOps). Recent research explores the exciting—and challenging—world of building AI agents capable of autonomously detecting, diagnosing, and mitigating cloud failures. The core challenge? There’s no standard way to build or evaluate these AI agents. Current AIOps tools often focus on individual aspects of incident management, lacking a holistic framework. This new research proposes a solution: AIOpsLab, a prototype framework designed to rigorously test and train AI agents in realistic cloud environments. AIOpsLab combines live services, realistic fault injection, and detailed observability to create a 'gym' where AI agents can learn the ropes of cloud resilience. The framework leverages real-world incident data, dynamic workload patterns, and detailed application telemetry to test AI agents with a spectrum of complex scenarios. Importantly, AIOpsLab is designed to accommodate diverse AIOps tools, from traditional rule-based systems to cutting-edge LLM-powered agents, via a flexible Agent-Cloud Interface. Early case studies with AIOpsLab and LLM agents offer promising insights. They underscore the crucial role of rich observability data, the need for efficient action APIs, and the surprising complexity of even seemingly simple cloud faults. AIOpsLab represents a significant leap towards standardized evaluation and development of AI-driven cloud management. By training AI agents in realistic, challenging environments, we are paving the way for truly self-healing clouds—a future where automated resilience ensures seamless operation and prevents costly downtime.