Agentic AI for trustworthy synthetic microbial genomics: a perspective on generation, validation, and governance

Synthetic microbial genomic data are becoming increasingly important for benchmarking microbial genome analysis pipelines, simulating rare taxa, evaluating metagenomic workflows, and supporting reproducible computational biology. Recent genomic foundation models demonstrate that biological sequences can be modelled at unprecedented scale, with emerging capacity for genome-level interpretation, generation, and design. However, the scientific value of synthetic microbial genomic data depends not only on whether sequences can be generated, but whether they are biologically plausible, computationally useful, reproducible, and responsibly governed. This Perspective argues that agentic AI can provide the missing orchestration layer for trustworthy synthetic microbial genomics. Rather than treating synthetic data generation as a single model output, agentic workflows can coordinate specialised roles for sequence generation, biological plausibility assessment, taxonomic validation, functional annotation, contamination detection, downstream benchmarking, provenance logging, and governance review. I propose a validation-first agentic framework in which synthetic microbial genomes, plasmids, phages, and metagenomic profiles are iteratively generated, evaluated, revised, and documented before release or downstream use. Such a framework can help transform synthetic microbial genomic data from computational artefacts into auditable scientific infrastructure with explicit validation gates, escalation criteria, and machine-readable provenance.