The field of data-driven discovery is rapidly advancing towards greater autonomy, with a focus on developing autonomous systems that can perform complex tasks without human intervention. Recent developments have seen the integration of large language models, machine learning, and automation to accelerate experimental procedures and improve the discovery of new materials. Autonomous agents are being designed to automate workflow planning, perform data analysis, and make decisions based on scientific intuition. These agents have shown promise in scaling data-driven discovery tasks, enhancing generalizability across parameter space, and improving the effectiveness of automated data science pipelines. Notably, the development of adaptive knowledgeable agents and multi-experiment equation learning methods are enabling more efficient and robust automated discovery. Noteworthy papers include: Agentomics-ML, which introduces a fully autonomous agent-based system for producing classification models and achieving state-of-the-art performance on benchmark datasets. AutoSDT, which presents an automatic pipeline for collecting high-quality coding tasks in real-world data-driven discovery workflows, resulting in a substantial improvement in performance on challenging data-driven discovery benchmarks. AutoMind, which overcomes the limitations of existing frameworks with an adaptive, knowledgeable LLM-agent framework that delivers superior performance versus state-of-the-art baselines.