La Agente Óptima
Purpose and distinctive contribution
Construct and supervise closed-loop Bayesian optimization campaigns for self-driving laboratories.
Persistent optimization state and separation of LLM reasoning from repeated executable optimization loops permit auditable intervention when campaigns need interpretation or revision.
This page describes the specific system in the cited study, not every capability of the wider El Agente platform.
Publication and full author credit
Marcel Müller; Jiaru Bai; Willi Gottstein; Abhijoy Mandal; Mohammad Nazeri; Elia Savino; Yanlin Fang; Sujoy Das; Sergio Pablo García Carrillo; Yeonghun Kang; Juan B. Pérez-Sánchez; Simone Pilon; Martin Fitzner; Timothy Noël; Frank Gu; Varinia Bernales; Alán Aspuru-Guzik. (2026). La Agente Óptima: Towards Agentic Self-Driving Laboratories. arXiv:2609.04564.
Publication or posting date: . Preprint; journal publication not independently verified.
Research roles and contributions
Prof. Varinia Bernales and Prof. Alán Aspuru-Guzik are credited as coauthors alongside the complete team above. Detailed conception, supervision, implementation, and maintainer roles have not been independently verified for this page and are not inferred from author order.
Software, artifacts, and limitations
Paper-linked research artifacts → Paper-linked artifacts; not represented as complete agent source. Availability evidence in the manuscript.
Capabilities are bounded by the tasks, tools, models, and evaluation conditions reported in the study. No universal speedup, reliability, or fully autonomous experimental capability is claimed here. Consult the linked manuscript’s methods and limitations before applying its results to a new workflow.