We will introduce the current efforts of using vision language model (VLM) agents for automated and low-barrier beamline operations and data processing algorithm research. We first present Experiment Automation Agents (EAA), an agent capable of controlling beamline instruments and making decisions based on image semantics, with a few cases demonstrating how it automates and democratizes experimental operations at APS beamlines. We will then introduce works on agentic data processing, which includes PEAR, a domain-expert system that tunes ptychographic reconstruction hyperparameters using reconstructed image as feedback, and Pty-Chi-Evolve, an auto-research agent that autonomously searches for regularization operators during iterative reconstructions to enhance result quality.