Autonomous Optimization of Business Intelligence Platforms Through Multi-Agent Systems
Keywords:
Autonomous Optimization, Multi-Agent Systems, Business Intelligence, Reinforcement Learning, Governance-Aware IntelligenceAbstract
Despite being a core piece of enterprise decision-making, Business Intelligence (BI) optimization is still reactive and limited to isolated automation scripts that cannot adapt to the growing complexity of today's analytics landscape in a continuous fashion. This article introduces a novel Multi-Agent Autonomous Optimization Framework (MAAOF), a distributed and highly adaptable approach based on cooperating and competing autonomous agents to continuously optimize the performance, data quality, query execution, and governance of all layers of a BI architecture. The combination of reinforcement learning, the distributed coordination of agents, and metadata-driven intelligence supports increasing levels of autonomous adaptation while keeping human oversight available. Compared to the benchmark industry standard, experiments conducted on simulated enterprise settings of a regulated banking infrastructure show MAAOF's ability to improve query latency, data pipeline efficiency, anomaly detection, and regulatory compliance. The work presents an integrated method that builds upon agentic artificial intelligence theory, autonomous systems theory, and self-healing data architecture. It helps establish scalable, adaptive, and resilient analytics infrastructures in contemporary enterprises.
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