Imagine a team of AI agents, tirelessly sifting through mountains of materials data, uncovering hidden relationships that could revolutionize how we design everything from stronger alloys to more efficient batteries. This isn't science fiction—it's the reality of a new multi-agent framework powered by large language models (LLMs). Researchers have developed a system where AI agents collaborate, using a depth-first search approach, to find the underlying mathematical laws buried within materials properties. Like detectives solving a complex case, these agents propose formulas, test them against experimental data, and then refine their hypotheses based on feedback from a 'reflection' agent. This reflection process helps the agents learn from past mistakes and avoid getting stuck in unproductive search paths. The framework was put to the test by trying to predict the glass-forming ability (GFA) of metallic glasses—a crucial property determining how easily a molten alloy solidifies into a glass without crystallizing. Using data on characteristic temperatures, the AI agents discovered a formula that outperformed existing methods, accurately predicting GFA even for materials it hadn't seen before. This remarkable success highlights the potential of this multi-agent framework. It's not just about finding a formula; it's about understanding the *why* behind it. The derived formula, unlike a black-box AI model, is interpretable, shedding light on the physical mechanisms governing GFA. This approach can be applied to a vast array of materials problems, potentially accelerating the discovery of new materials with tailored properties. While the current framework relies on numerical data, the future holds exciting possibilities. Researchers envision integrating multimodal data, like images and text, to capture a richer understanding of materials behavior. By incorporating physical constraints and symmetries, they also aim to streamline the search process, making it even more efficient. This multi-agent framework isn't just a tool; it's a partner in scientific discovery, helping us unlock the secrets of materials science and usher in a new era of materials innovation.