From Prompts to Protocols: An AI Agent for Laboratory Automation

从提示词到实验方案：用于实验室自动化的 AI 智能体

Abstract: Automating science laboratories enables faster, safer, more accurate, and more reproducible execution of protocols, accelerating the discovery and testing of new materials, drugs, and more. However, setting up and running autonomous labs requires coordinating numerous instruments and robots, forcing scientists to write code, manage configuration files, and navigate complex software infrastructure.

摘要： 科学实验室的自动化能够实现更快速、更安全、更精确且更具可重复性的实验方案执行，从而加速新材料、药物及其他领域的发现与测试。然而，建立和运行自主实验室需要协调大量的仪器和机器人，这迫使科学家们必须编写代码、管理配置文件并应对复杂的软件基础设施。

We present an AI agent architecture that integrates large language models with laboratory orchestration, enabling scientists to interactively create and monitor automated lab protocols using natural language. Integrated into the Experiment Orchestration System (EOS), the AI agent operates under an agentic loop with automated validation and error correction, and supports the complete experimental lifecycle: creating protocols, running and monitoring both protocols and closed-loop optimization campaigns, and analyzing results.

我们提出了一种将大语言模型与实验室编排系统相结合的 AI 智能体架构，使科学家能够使用自然语言交互式地创建和监控自动化实验方案。该 AI 智能体集成在实验编排系统（EOS）中，在包含自动验证和纠错功能的智能体循环下运行，并支持完整的实验生命周期：包括创建方案、运行及监控实验方案与闭环优化任务，以及分析实验结果。

A visual graph editor renders protocols as interactive node-based diagrams synchronized with the AI agent’s protocol representation, enabling seamless alternation between AI-assisted and manual protocol construction. Evaluated on three simulated automated labs spanning chemistry, biology, and materials science, the AI agent achieves a 97% first-attempt protocol generation success rate and an order of magnitude reduction in required interface actions.

一个可视化图形编辑器将实验方案呈现为交互式的节点图，并与 AI 智能体的方案表示保持同步，从而实现了 AI 辅助构建与手动构建之间的无缝切换。在涵盖化学、生物学和材料科学的三个模拟自动化实验室中进行的评估显示，该 AI 智能体在实验方案生成上的首次尝试成功率达到了 97%，并将所需的界面操作次数减少了一个数量级。