The rational design of microbial cell factories is essential for sustainable biomanufacturing, yet the traditional Design-Build-Test-Learn (DBTL) cycle is bottlenecked by the highly non-linear and interconnected nature of biological systems. Although large language models (LLMs) offer computational advantages for automated design, their application in systems metabolic engineering is hindered by factual inconsistencies and a limited capacity for multi-hop causal reasoning—challenges that static single-pass retrieval-augmented generation (RAG) fails to resolve. Here, we present SynBioGPT2, a dynamic reasoning framework that integrates paragraph-level hybrid retrieval, an iterative self-evaluation loop, and domain-specific expert prompt templates to enable autonomous, multi-source knowledge synthesis. Evaluated on a multidimensional synthetic biology benchmark, the architecture achieved 91.67% accuracy and completeness, significantly outperforming zero-shot LLMs and static RAG baselines. We demonstrated the framework's capability to resolve systems-level biochemical constraints, including redox balancing and complex allosteric feedback networks, during the rational computational design of Corynebacterium glutamicum. Furthermore, SynBioGPT2 ensured the high-fidelity extraction of quantitative parameters and successfully reconstructed 93.5% (86/92) of expert-curated metabolic engineering strategies across diverse target products. By mitigating structural reasoning deficits and integrating expert-guided deductive logic, SynBioGPT2 provides a mechanistically robust, scalable platform to accelerate automated biological discovery and rational cell factory engineering.