Multi-Agent Readiness Scoring Methodology in Bioinformatics Domain

Abstract

The emergence of Large Language Models (LLMs) has significantly advanced computational biology, yet their integration into autonomous, multi-agent systems (MASs) and clinical workflows remains challenging due to systemic architectural fragmentation. To quantify the operational readiness and regulatory compliance of bioinformatics LLMs, we developed the Multi-Agent Readiness Score (MARS), a standardized evaluation framework assessing models across four structural dimensions: Governance & Accessibility, Biological Competence, Technical Maturity, and Agentic Orchestration. The framework incorporates compliance criteria from the EU AI Act, HL7 FHIR, HL7 CDA, and MyHealth@EU standards. To empirically validate this domain-agnostic methodology, we applied it to a highly mature subset of the field: a diverse cohort of 43 prominent genomic LLMs. Our assessment revealed a severe, industry-wide readiness gap: the majority of models fell into “Not Suitable” or “Research Prototype” tiers, lacking essential technical interfaces, structured communication schemas, and provenance tracking. Furthermore, the data demonstrated a ’competence-readiness gap’, where models scale in biological predictive competence without corresponding improvements in engineering utility. The primary barrier to scalable bioinformatics AI is no longer biological competence, but operational and architectural incompatibility. By quantifying integration friction, MARS provides a crucial, reproducible metric to audit model maturity, guide system architecture, and ensure future models are structurally prepared for the rigorous regulatory demands of precision medicine workflows.