Interpreting gene clusters from RNA-seq remains challenging, especially in antimicrobial resistance studies where mechanistic context is essential for hypothesis generation. Conventional enrichment methods summarize co-expressed modules using predefined categories, but often return sparse results and lack cluster-specific, literature-linked explanations. We present BIOGEN, an evidence-grounded multi-agent framework for post hoc interpretation of RNA-seq transcriptional modules that integrates biomedical retrieval, structured reasoning, and multi-critic verification. BIOGEN organizes evidence from PubMed and UniProt into traceable cluster-level interpretations with explicit support and confidence tiering. On a primary Salmonella enterica dataset, BIOGEN achieved strong evidence-grounding performance while reducing hallucination from 0.67 in an unconstrained LLM setting to 0.00 under retrieval-grounded configurations. Compared with KEGG/ORA and GO/ORA, BIOGEN recovered broader biological coverage, identifying substantially more biological themes per cluster. Across four additional bacterial RNA-seq datasets, BIOGEN maintained zero hallucination and consistently outperformed KEGG/ORA in cluster-level thematic coverage. These results position BIOGEN as an interpretive support framework that complements transcriptomic workflows through improved traceability, evidential transparency, and biological coverage.

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