La Agente Óptima: Towards Agentic Self-Driving Laboratories
Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery. Their operation nevertheless often depends on human specialists who translate scientific objectives into executable closed-loop campaigns. Specialists adjust them as data and operating conditions change. Here, we present La Agente Óptima, an agentic framework that constructs and supervises Bayesian optimization campaigns across computational and experimental systems while maintaining a persistent optimization state. By separating large language model (LLM) reasoning from executed campaigns, Óptima runs repetitive optimization loops consistently, returns cont...
| Authors | Marcel Müller, Jiaru Bai, Willi Gottstein et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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