AI agents can be used to set up private research assistants, as demonstrated by running OpenClaw with Ollama, which includes configuring context length, enabling web search, and deploying headlessly in Docker
(https://www.kdnuggets.com/running-openclaw-with-ollama). The role of data scientists is shifting from building models to managing them, indicating a move towards more agent-driven workflows (https://www.kdnuggets.com/data-scientists-are-becoming-ai-managers-not-model-builders).
Small Language Models (SLMs) are crucial for powering next-generation agents, offering various applications from research backing to practical tools (https://www.kdnuggets.com/5-ways-small-language-models-are-powering-next-gen-agents). Tools like Hugging Face ML Intern allow users to describe a model, and it writes the code, runs the training, and ships the checkpoint, acting as an ML agent (https://www.kdnuggets.com/getting-started-with-hugging-face-ml-intern-your-first-ml-agent).