Agentic genomics: From pipeline automation to autonomous validation
Manuel Corpas1, Heinner Guio2, Segun Fatumo3
1School of Life Sciences, University of Westminster, London, UK.
Cell Genomics
|July 21, 2026
Summary
Agentic genomics uses AI agents for autonomous multi-step genomic analysis. This approach shifts computational biology bottlenecks from pipeline creation to validation, requiring robust infrastructure for trustworthy results.
Area of Science:
- Computational Biology
- Genomics
- Artificial Intelligence
Background:
- Genomic analysis traditionally involves complex, multi-step bioinformatics pipelines.
- The increasing complexity of genomic data necessitates more efficient and automated analysis methods.
Purpose of the Study:
- To introduce and define the paradigm of "agentic genomics."
- To explore the implications of agentic genomics for computational biology bottlenecks.
- To assess current systems and propose a framework for validation and trustworthy implementation.
Main Methods:
- Review and analysis of emerging agentic genomics systems (CellAtria, AutoBA, Bio-Copilot, ClawBio).
- Conceptualization of a tiered validation framework (research-grade, benchmarked, clinical-grade).
- Identification of infrastructure requirements for trustworthy agentic genomics.
Main Results:
- Agentic genomics enables autonomous discovery, configuration, execution, and chaining of bioinformatics operations via natural language.
- The primary bottleneck in computational biology is shifting from pipeline construction to validation.
- Divergent architectures exist among current agentic genomics systems.
Conclusions:
- Agentic genomics represents a significant advancement in automating complex genomic analyses.
- A robust, tiered validation framework and equity-aware design are crucial for trustworthy agentic genomics.
- Developing essential infrastructure is key to realizing the full potential of agentic genomics.
