A LARGE LANGUAGE MODEL DRIVEN AGENT FRAMEWORK FOR AUTONOMOUS, SECURE, AND SCALABLE BIG DATA MANAGEMENT IN HETEROGENEOUS CLOUD ECOSYSTEMS

Abstract

The proliferation of heterogeneous multi-cloud and edge environments has made big-data management increasingly difficult to automate, secure, and scale using conventional rule-based orchestration. This paper proposes a layered conceptual and architectural framework in which large language model (LLM) based agents act as the reasoning and coordination layer for autonomous data placement, access-control decisioning, schema reconciliation, and anomaly response across heterogeneous cloud ecosystems. The framework couples an LLM planner-negotiator-critic agent pipeline with a zero-trust policy engine, a reinforcement-learning-based resource scheduler, and a federated-learning layer for privacy-preserving cross-silo model updates. We describe the system model, the architecture, the interaction protocol between agents and the enforcement layer, and the agent roles in detail, and we present illustrative, simulated scenarios and figures to reason about expected scalability, cost, compliance-risk, and policy-conformance trade-offs. We position the proposal against existing literature on LLM-based autonomous agents, federated learning, zero-trust security, and reinforcement-learning-driven cloud scheduling, and we outline the empirical evaluation, red-teaming, and benchmarking work required before the framework could be validated in production settings. We further discuss data-governance and regulatory-compliance implications, a taxonomy of agent-specific threats, and a research agenda for follow-up empirical studies. The contribution of this paper is architectural and conceptual: it is intended as a foundation for subsequent empirical validation rather than as a report of completed experimental results.