LLM-based framework for intent-driven network topology design

Designing robust and adaptable network topologies from requirements expressed in natural language represents one of the major challenges in modern infrastructure automation. With the emergence of large language models (LLMs), a promising path opens up to transform abstract descriptions into valid and resilient network configurations. A recent approach combines hierarchical modeling with systematic validation to guide these models in generating topologies that meet structural and connectivity constraints. This type of framework not only evaluates correctness through metrics such as F1 over nodes and links, but also measures resilience against failures using server-content connectivity indicators. Underlying this technology is a pipeline that integrates abstraction layers and automatic verification, reducing common errors such as interface mismatches or directional inconsistencies. For companies seeking to adopt these capabilities, having a specialized technology partner makes the difference. Q2BSTUDIO offers artificial intelligence for businesses that enables the implementation of automated infrastructure generation solutions, combining AI agents with language models adapted to production environments. Additionally, we develop custom applications for process automation that integrate structural validation and multi-layer deployment, facilitating the transition towards intent-driven networks. Our AWS and Azure cloud services provide the necessary scalability to run these systems efficiently, while cybersecurity capabilities and business intelligence services, including Power BI, allow monitoring and optimizing the performance of the generated topologies. The future of network design lies in the convergence of natural language, generative models, and rigorous validation, and at Q2BSTUDIO we accompany organizations at every step of this journey, offering custom software that turns intent into reliable infrastructure.