From Prompts to Protocols: An AI Agent for Laboratory Automation
Researchers present an AI agent architecture that integrates large language models with laboratory orchestration, enabling scientists to create and monitor automated lab protocols using natural language. Integrated into the Experiment Orchestration System (EOS), it achieves a 97% first-attempt success rate and an order-of-magnitude reduction in interface actions across three simulated labs.
[2605.16552] From Prompts to Protocols: An AI Agent for Laboratory Automation
[Submitted on 15 May 2026]
Title:From Prompts to Protocols: An AI Agent for Laboratory Automation
View a PDF of the paper titled From Prompts to Protocols: An AI Agent for Laboratory Automation, by Angelos Angelopoulos and 2 other authors
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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.
Subjects:
Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2605.16552 [cs.AI]
(or arXiv:2605.16552v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.16552
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ron Alterovitz [view email] [v1] Fri, 15 May 2026 18:52:41 UTC (4,928 KB)
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