A novel low-code platform integrates a Bayesian adversarial multi-agent framework to enhance the reliability of Large Language Models (LLMs) in scientific code generation. This framework addresses the challenges of error propagation in multi-agent workflows and evaluation in domains with unclear success metrics. By leveraging LLMs, the platform aims to automate scientific code generation, improving the efficiency and accuracy of AI-for-Science tasks. The incorporation of a Bayesian approach enables the platform to model uncertainty and adapt to complex workflows. This development has significant implications for the security landscape, as LLMs can introduce new risks, particularly in domains like DeFi, where the line between capability and risk is increasingly blurred1. The emergence of such platforms underscores the need for practitioners to carefully evaluate the security implications of LLMs in their workflows, as the technology's potential benefits are accompanied by potential vulnerabilities.