The article discusses the integration of machine learning (ML), in vivo evolution, and automated biofoundries to accelerate the development of superior biocatalysts. The authors propose an integrated workflow that combines ML-guided automated workflows, including library generation, hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection, to enhance the throughput and efficiency of enzyme engineering. They highlight the importance of high-throughput screening methods and the use of ML to predict and optimize enzyme properties. The article also reviews various techniques for enzyme engineering, such as rational mutagenesis, semi-rational mutagenesis, and directed evolution, and discusses the challenges and limitations of these methods. Additionally, it explores the potential of de novo enzyme design and the role of ML in pathway design and optimization. The authors emphasize the importance of selection strains for in vivo enzyme screening and the use of hypermutation techniques to increase genetic diversity. They also discuss the application of adaptive laboratory evolution (ALE) to further enhance emerging phenotypes. Finally, the article outlines the future prospects of automated in vivo enzyme engineering and the challenges that need to be addressed, such as the detection threshold of growth-coupled selection and the bias in in vivo hypermutators.The article discusses the integration of machine learning (ML), in vivo evolution, and automated biofoundries to accelerate the development of superior biocatalysts. The authors propose an integrated workflow that combines ML-guided automated workflows, including library generation, hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection, to enhance the throughput and efficiency of enzyme engineering. They highlight the importance of high-throughput screening methods and the use of ML to predict and optimize enzyme properties. The article also reviews various techniques for enzyme engineering, such as rational mutagenesis, semi-rational mutagenesis, and directed evolution, and discusses the challenges and limitations of these methods. Additionally, it explores the potential of de novo enzyme design and the role of ML in pathway design and optimization. The authors emphasize the importance of selection strains for in vivo enzyme screening and the use of hypermutation techniques to increase genetic diversity. They also discuss the application of adaptive laboratory evolution (ALE) to further enhance emerging phenotypes. Finally, the article outlines the future prospects of automated in vivo enzyme engineering and the challenges that need to be addressed, such as the detection threshold of growth-coupled selection and the bias in in vivo hypermutators.