From Prompts to Protocols: An AI Agent for Laboratory Automation

TL;DR

This paper introduces an AI agent that leverages large language models to automate laboratory protocols, improving efficiency, accuracy, and user interaction in scientific experiments.

Contribution

It presents a novel AI agent architecture integrated with laboratory orchestration systems, enabling natural language interaction for creating, monitoring, and analyzing lab protocols.

Findings

97% success rate in first-attempt protocol generation

Order of magnitude reduction in interface actions

Effective across chemistry, biology, and materials science labs

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.