Enterprises face challenges in managing the complexities of their environments and the need for high performance and availability at low costs, leading to the adoption of Chaos Engineering and Autonomous Optimization to enhance system resilience. Chaos Engineering involves controlled experiments to identify and mitigate potential failures, with companies like Gremlin leading in industrializing these practices for cloud-native applications. Meanwhile, Autonomous Optimization leverages AI to automatically determine optimal configurations, reducing the burden on performance engineers and enhancing application performance and cost-efficiency. Akamas, utilizing Reinforcement Learning, exemplifies this by optimizing configurations across various parameters quickly and effectively. The combination of these practices allows organizations to not only preemptively address failure scenarios but also optimize system configurations for resilience and cost savings, ultimately improving service quality and reducing operational costs.