From Prompts to Protocols: An AI Agent for Laboratory Automation

2026-05-15

AI Summary

This study addresses the challenges of laboratory automation—particularly the complexity of instrument coordination and cumbersome software configuration—that significantly hinder research efficiency. The authors propose an AI agent framework integrating large language models (LLMs) with the Experiment Orchestration System (EOS), pioneering the embedding of LLMs into the full lifecycle management of experiments. This system enables scientists to interactively create, execute, monitor, and optimize experimental protocols through natural language, complemented by a node-based visual graph editor for intuitive and synchronized protocol construction. Leveraging an agent-loop architecture alongside automated validation and error-correction mechanisms, the framework achieves a 97% success rate in translating natural language instructions into executable protocols and supports seamless switching between AI-driven and manual editing. Evaluations in simulated chemical, biological, and materials science environments demonstrate a tenfold reduction in user operations.

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. 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. 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.

Problem

• laboratory automation
• protocol generation
• scientific workflows
• AI agent
• experiment orchestration

Innovation

• AI agent
• laboratory automation
• large language models
• protocol generation
• experiment orchestration