From Prompts to Protocols: An AI Agent for Laboratory Automation
Quick Answer
This paper shows that An AI agent architecture integrates large language models with laboratory orchestration, achieving a 97% success rate in protocol generation while reducing interface actions significantly.
Quick Take
This innovation allows scientists to create and monitor automated lab protocols using natural language, enhancing the efficiency of experimental workflows across chemistry, biology, and materials science.
Key Points
- AI agent operates within an agentic loop for automated validation and error correction.
- Supports the entire experimental lifecycle: creation, execution, monitoring, and analysis.
- Visual graph editor allows interactive protocol construction synchronized with AI.
- Evaluated in three simulated labs, achieving significant performance improvements.
- Reduces required interface actions by an order of magnitude.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
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