AI-first observability is revolutionizing the approach to troubleshooting in complex IT environments by addressing the challenges posed by AI-generated code and modern cloud infrastructure. As AI tools increasingly contribute to code development, they introduce new complexities, such as understanding AI logic and addressing testing coverage gaps, which add to existing visibility issues for DevOps teams. Traditional observability solutions are insufficient for monitoring dynamically generated code and correlating data from numerous sources, leading to blind spots and delayed responses to system alerts. The future of observability is centered around autonomous AI, which will serve as embedded agents that perform root cause analysis and provide actionable insights before alerts are triggered, thus reducing mean time to resolution (MTTR) and simplifying processes. Organizations are advised to start integrating AI into their observability practices by setting realistic expectations, focusing on specific tasks AI can handle, and avoiding common pitfalls like ignoring KPIs and underestimating the transformative potential of AI. A successful AI observability implementation can lead to cost reductions and enhanced efficiency, as demonstrated in a webinar by Logz.io founders, where AI agents automate investigations, streamline root cause analysis, and enhance incident response.