A Self-Evolving Agentic System for Automated Generation and Execution of Biological Protocols

2026-06-30

AI Summary

This work addresses the challenge of aligning natural language experimental protocols, scientific intent, and executable device instructions in automated biological experimentation by introducing ProtoPilot, a self-evolving multi-agent system. ProtoPilot leverages a hierarchical verifiable architecture, collaborative multi-agent coordination, and runtime skill library updates to automatically translate natural language protocols into executable code, establishing the first closed-loop autonomous wet-lab workflow with experimental validation. Empirical evaluation demonstrates that ProtoPilot achieves a 90.2% top-3 agreement with expert preferences, an 89.5% end-to-end protocol-to-code success rate, and an 88.24% execution success rate on the Opentrons platform—significantly outperforming baseline methods. The system successfully produced DNA constructs validated by Sanger sequencing and generated interpretable experimental outcomes.

Abstract

Autonomous wet-lab experimentation requires more than plausible protocol text: biological intent, quantitative procedures, device constraints and experimental feedback must remain aligned from protocol and SOP design to code and physical execution. We developed ProtoPilot, a self-evolving multi-agent system, together with an expert-grounded benchmark and evaluation framework for testing this conversion as an experimental automation problem. The framework spans 294 synthetic-biology and molecular-biology tasks derived from 98 gold-standard protocols, wet-lab expert rubrics, device-level validity gates and real experimental tests. ProtoPilot incorporates layer-wise verifiability, multi-agent orchestration and a runtime-updated skill library to generate protocols, expand SOPs, synthesize SDK-compliant code and revise workflows from wet-lab feedback. It achieved a Top@3 expert-preference rate of 90.2%, an overall protocol-to-code gate pass rate of 89.5% and an Opentrons pass rate of 88.24%, compared with 32.35% for OpenTrons-AI. Wet-lab validation produced interpretable readouts, Sanger-confirmed products and feedback-corrected PCA-assembled DNA targets, establishing a verifiable route to autonomous experimentation. Together, these results show that the evaluation framework captures execution-relevant requirements for autonomous wet-lab automation, and that ProtoPilot can meet them by converting protocol and code generation into validated execution and feedback-guided revision.

Problem

• autonomous wet-lab experimentation
• biological protocols
• protocol-to-code conversion
• execution alignment
• experimental automation

Innovation

• self-evolving agentic system
• autonomous wet-lab experimentation
• protocol-to-code translation
• multi-agent orchestration
• feedback-guided revision