An AI agent architecture integrated with the Experiment Orchestration System converts natural language inputs into validated laboratory protocols, attaining 97% first-attempt success across three simulated labs in chemistry, biology, and materials science while reducing interface actions by an order of magnitude (Alterovitz et al., arXiv:2605.16552, 2026). The system employs an agentic loop with automated validation, error correction, and synchronization to a visual graph editor for protocol construction and closed-loop optimization monitoring. Primary evaluation remains confined to simulation, omitting hardware latency, sensor noise, or physical calibration variances documented in deployed self-driving laboratories. Related systems such as Coscientist demonstrate end-to-end chemical synthesis execution on real robotic platforms (Boiko et al., Nature 624, 2023), while the EOS agent adds explicit natural-language interactivity and graph-based manual override absent from earlier prompt-to-code approaches. Synthesis of these sources reveals the current work extends orchestration layers but does not yet address cross-platform instrument drivers or reproducibility metrics under live experimental conditions reported in autonomous materials platforms (MacLeod et al., Nature 610, 2022).