Future Cloud Architectures for Agentic Financial AI: From Managed MLOps to Autonomous Compliance and Resilient AI Infrastructure
Main article
Abstract
Cloud platforms have become the de-facto substrate on which financial institutions train, deploy, and govern their artificial intelligence systems. As the industry transitions from managed machine-learning operations (MLOps) toward agentic financial AI capable of executing multi-step trading, compliance, and customer-service workflows autonomously, the architectural expectations placed on the underlying cloud stack are changing in fundamental ways. This paper proposes a forward-looking architectural model for the next generation of cloud platforms supporting financial AI, organized around three pillars: an evolved MLOps layer that natively accommodates large language model fine-tuning and retrieval-augmented inference; an autonomous compliance layer that emits continuous, machine-verifiable evidence aligned with ISO/IEC 42001, the EU AI Act, and the Digital Operational Resilience Act (DORA); and a resilient infrastructure layer that explicitly engineers for cascading failure, post-quantum cryptographic transitions, and the extraordinary energy demands of inference at scale. We compare the dominant hyperscaler offerings across five operational dimensions (latency, throughput, energy intensity, vendor portability, and compliance velocity), draw lessons from the 2025 series of high-profile cloud outages, and propose a maturity model that financial institutions can use to position themselves on the path from manual evidence collection to autonomous compliance. The discussion is intentionally architectural rather than product-centric: the goal is a coherent picture of what the cloud must become in order to host financial AI systems that are simultaneously autonomous, accountable, and resilient.
