Building and updating complex AI systems, like coding assistants or robot controllers, traditionally involves a lot of manual tweaking and fine-tuning. It's like trying to assemble a giant jigsaw puzzle in the dark. But what if there was a way to illuminate the process, making it easier to see how each piece fits together and how to adjust them for the best overall result? Researchers have introduced a groundbreaking framework called "Trace," which does just that. Inspired by the automatic differentiation (AutoDiff) used for training neural networks, Trace brings a similar level of automation to the optimization of general computational workflows. Think of AutoDiff as a powerful tool that helps train neural networks by automatically calculating gradients, indicating how to adjust the network's parameters to improve its performance. Trace extends this idea beyond the realm of purely differentiable systems. It tackles messy, real-world workflows that often involve non-differentiable components, diverse parameters like prompts and code snippets, and complex objectives that go beyond simply maximizing a score. The core innovation of Trace lies in its use of "execution traces," which are records of the intermediate steps and computations within a workflow. These traces act like a roadmap, showing exactly how different parts of the system interact and contribute to the final outcome. By analyzing these traces, Trace can provide valuable insights into how to adjust the various parameters – be it code, prompts, or hyperparameters – to achieve the desired behavior. The researchers also developed OptoPrime, a generative optimizer designed to work seamlessly with Trace. OptoPrime leverages the power of large language models (LLMs) to interpret the execution traces and suggest parameter updates based on feedback received from the system's performance. In essence, OptoPrime acts like an intelligent assistant, providing guidance on how to improve the AI system being trained. Trace and OptoPrime have been successfully applied to various tasks, from numerical optimization and hyperparameter tuning to training robot controllers and optimizing complex LLM agents. The results show that this framework can not only automate the design and update of AI systems but also achieve performance competitive with, or even exceeding, specialized optimizers designed for specific domains. This opens up exciting new possibilities for the development of more sophisticated and adaptive AI systems, capable of learning and improving their behavior autonomously.