Architecting Scalable Intelligence for High-Throughput Autonomous Systems: Generative AI Integration via Systems Programming and Cloud-Native Microservices
In today’s rapidly evolving technological ecosystem, autonomous systems are transitioning from controlled experimental settings into mainstream, mission-critical applications. These systems demand intelligent computation that is not only scalable but also efficient and adaptable to changing conditions in real time. This paper presents an innovative architectural paradigm that leverages the transformative power of Generative Artificial Intelligence (GenAI) — specifically diffusion-based models — and combines it with robust systems programming principles and elastic cloud microservices. The result is an infrastructure capable of delivering high-throughput, intelligent behaviors across domains such as autonomous vehicles, adaptive monitoring systems, and real-time decisioning platforms. The proposed architecture is evaluated through experimental benchmarks and real-world simulations, demonstrating significant improvements in latency, scalability, and reliability. Our contribution lies in detailing the integration process, identifying architectural patterns for modular development, and offering a roadmap for future deployments of GenAI-powered, cloud-native autonomous systems.
Keywords: Generative Artificial Intelligence (GenAI), Autonomous Systems, Cloud-Native Microservices, Systems Programming, Diffusion Models, Scalable Architecture, Distributed AI, Real-Time Inference, High-Performance Computing (HPC), Adaptive Intelligence, Edge Computing
