The distributed nature of microservice architecture introduces significant resilience challenges. Traditional testing methods often suffer from extensive manual effort and oversimplified test environments, failing to capture the complexity of production systems. To address these limitations, we present Cast, an automated, end-to-end framework for microservice resilience testing in production. Cast achieves high test fidelity by replaying production traffic against a comprehensive library of application-level faults to rigorously exercise internal error-handling logic. To manage the combinatorial test space, Cast employs a complexity-driven strategy to systematically prune redundant tests and prioritize high-value tests targeting the most critical service execution paths. Cast automates the testing lifecycle through a three-phrase pipeline (i.e., startup, fault injection, and recovery) and uses a multi-faceted oracle to automatically verify system resilience against nuanced criteria. Deployed in Huawei Cloud for over eight months, Cast has been adopted by many service teams to proactively identify and address resilience vulnerabilities. Our in-depth analysis on four large-scale applications with millions of traces reveals 137 potential resilience vulnerabilities, 89 of which are confirmed by developers. To further quantify its performance, Cast is evaluated on a benchmark set of 48 reproduced bugs, achieving a high coverage of 90%. The results show that Cast is a practical and effective solution for systematically improving the reliability of industrial microservice systems.