Retrieval-Augmented Multi-Agent Systems for Autonomous Business Intelligence and Strategic Planning
Keywords:
Retrieval-augmented generation, multi-agent systems, autonomous business intelligence, strategic planning, system architecture, governance, fairness, deployment, robustnessAbstract
The convergence of retrieval-augmented generation and multi-agent systems presents a transformative approach to autonomous business intelligence and strategic planning. This paper develops a comprehensive architectural framework that integrates distributed agents with external knowledge retrieval capabilities, enabling organizations to synthesize vast, heterogeneous data sources for real-time decision-making. The proposed system is examined across multiple dimensions: structural trade-offs between retrieval latency and factual accuracy, inter-agent coordination protocols, governance mechanisms for accountability, and operational robustness under uncertainty. Distinct from monolithic models or purely reactive analytics, the retrieval-augmented multi-agent paradigm allows for modular composition of specialized agents that each access curated knowledge bases, thus reducing hallucination and improving relevance in strategic outputs. However, the introduction of retrieval pipelines introduces latency and dependency on external repositories, necessitating careful design of caching policies, query routing, and fallback strategies. Furthermore, the multi-agent architecture raises challenges in fairness, as differential access to information can amplify biases, and in governance, as autonomous agents may produce decisions that require human oversight. This paper analyzes these trade-offs using cross-domain comparisons with centralized business intelligence systems and decentralized planning approaches, and discusses infrastructure requirements for deployment in enterprise environments. Policy implications are considered, particularly regarding liability, transparency, and regulatory compliance. The paper concludes by outlining future research directions for scalable, trustworthy, and sustainable retrieval-augmented multi-agent systems for strategic decision-making.
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