Engineering Autonomous Enterprise Decision Systems Using Neuro-Symbolic AI and Cloud-Native Infrastructure
DOI:
https://doi.org/10.15662/IJEETR.2026.0803017Keywords:
Neuro-Symbolic AI, Cloud-Native Infrastructure, Autonomous Decision Systems, Knowledge Graphs, Deep Learning, Explainable AI, Event-Driven ArchitectureAbstract
Modern enterprises operate within highly complex, high-throughput environments where data-driven choices must be made with absolute accuracy, speed, and regulatory compliance. Traditional autonomous decision systems typically rely either on deep learning networks, which offer exceptional pattern recognition but function as uninterpretable black boxes, or on symbolic expert systems, which provide strict logical explainability but lack the flexibility to handle unstructured data. This paper presents a cohesive architectural framework for engineering autonomous enterprise decision systems by integrating Neuro-Symbolic AI with scalable, cloud-native infrastructure. By fusing the sub-symbolic perceptual power of neural networks with the symbolic, rule-based reasoning of knowledge graphs and logic engines, our framework achieves both cognitive flexibility and deterministic regulatory compliance. To deploy this hybrid paradigm at an enterprise scale, we introduce a cloud-native topology that encapsulates neural and symbolic workloads into decoupled microservices, coordinated via an asynchronous, event-driven service mesh. Empirical evaluations demonstrate that this neuro-symbolic framework reduces logic-validation failures to zero, improves decision accuracy in volatile market environments by 38% compared to pure deep-learning models, and maintains optimal horizontal scaling metrics. Ultimately, this approach establishes a scalable design blueprint for deployable, transparent, and enterprise-grade autonomous intelligence.
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