The recent developments in the research area of language model augmentation and workflow integration have shown significant advancements. Researchers are focusing on creating more modular and adaptable frameworks for enhancing language models' capabilities. A notable trend is the decoupling of tool usage from both the model and its prompts, allowing for greater flexibility and generalization across tasks. This approach, exemplified by the introduction of 'language hooks,' enables the interleaving of text generation with modular program execution, thereby enhancing the model's ability to leverage external tools and auxiliary models. Additionally, there is a growing interest in predicting emergent abilities of large language models through the use of proxy tasks, which helps in forecasting performance on target tasks more accurately. On the workflow integration front, efforts are being made to bridge the gap between the Python and Common Workflow Language (CWL) ecosystems, facilitating the execution of CWL workflows using Python-based libraries like Parsl. This integration not only enhances the usability of CWL tools within the Python environment but also showcases improved performance in executing complex workflows. Overall, these innovations are pushing the boundaries of what language models and workflow management systems can achieve, making them more versatile and efficient.