Multi-Agent Collaboration for Uncertainty-Aware RAG: Dynamic Evidence Verification and Risk-Controlled Generation
Keywords:
retrieval-augmented generation, multi-agent systems, uncertainty quantification, evidence verification, risk-controlled generation, AI governance, trustworthy AI, socio-technical infrastructureAbstract
Retrieval-augmented generation has become a central architecture for grounding large language models in external knowledge, yet its operational reliability remains fragile when retrieval quality, source authority, and generation confidence diverge. This paper examines multi-agent collaboration as a system-level strategy for uncertainty-aware retrieval-augmented generation, with emphasis on dynamic evidence verification and risk-controlled generation. The argument is that trustworthy generation cannot be achieved through a single retrieval pass or a monolithic model call. Instead, it requires an orchestrated division of labor among specialized agents responsible for query interpretation, evidence discovery, source validation, contradiction analysis, uncertainty estimation, policy adjudication, and final response synthesis. Such an architecture introduces new structural trade-offs involving latency, cost, auditability, fairness, and deployment sustainability. The paper develops a conceptual framework for multi-agent uncertainty-aware retrieval-augmented generation, analyzes the governance implications of risk-controlled generation, and considers how dynamic evidence verification can be integrated into public-sector, clinical, legal, financial, and scientific workflows. It further assesses infrastructure requirements, failure modes, evaluation metrics, and policy constraints that shape real-world adoption. The central finding is that multi-agent collaboration improves trustworthiness only when uncertainty is treated as a first-class operational signal and when verification is embedded throughout the generation pipeline rather than appended as a post hoc filter. Risk control must therefore be understood not merely as model alignment but as socio-technical infrastructure involving institutional accountability, contestability, and continuous monitoring. The paper concludes that uncertainty-aware retrieval-augmented generation should be designed as a governed evidence system, not simply as a faster or larger language model application.
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