Time series data—those sequences of data points collected over time—are everywhere, from the stock market to your Fitbit. But extracting meaningful insights from this complex data can be challenging. While Large Language Models (LLMs) excel at text, their ability to interpret time series data remains limited. Often, they capture only basic trends or even hallucinate, missing crucial nuances. A new research paper introduces TESSA, a groundbreaking multi-agent system designed to overcome these limitations. TESSA leverages the power of multiple LLMs working together. A "general annotation agent" identifies common patterns across different datasets, translating specialized terminology into everyday language. Meanwhile, a "domain-specific annotation agent" learns the specific jargon of a target domain, like finance or healthcare, to provide targeted insights. This innovative approach allows TESSA to generate both general and domain-specific annotations for time series data. In tests, TESSA significantly outperformed existing methods, generating annotations that were clearer, more comprehensive, and contextually relevant. Imagine automatically generating reports on stock market trends or predicting equipment failures based on sensor data. TESSA opens doors to powerful new applications of AI in time series analysis. However, challenges remain. Fine-tuning these models requires substantial computational resources. Additionally, ensuring the consistency and accuracy of domain-specific terminology across diverse fields remains an ongoing area of research. As LLMs continue to evolve, we can anticipate even more sophisticated methods for decoding the hidden stories within time series data, unlocking deeper insights into the world around us.