Designing Production-Grade Agentic AI for Continuous Payment Exception Monitoring, Hierarchical Authorization, and Governance in Financial Systems: An MCP, LLM, and RAG Implementation Architecture

Abstract

Autonomous AI systems for financial payment operations have matured from theoretical governance proposals to deployable production architectures, yet the published literature has not kept pace with the tooling ecosystem that makes such systems practically viable. This paper advances the state of the art in two directions. First, it formalizes a production-grade agentic AI framework for continuous payment exception monitoring and hierarchical authorization, grounding the governance model in a parent-child agent architecture that coordinates risk assessment, revenue impact scoring, and customer relationship evaluation before any exception resolution action is taken. Second — and as the primary new contribution of this revision — it specifies a concrete implementation architecture in which the agentic AI system operates through Model Context Protocol (MCP) server/client connections, a large language model (LLM) reasoning core, and a Retrieval-Augmented Generation (RAG) pipeline that grounds every autonomous decision in a continuously refreshed corpus of regulatory policy, internal governance rules, and historical resolution precedents. The MCP-mediated architecture allows the agentic AI system to query payment data, compliance documents, customer relationship records, and revenue analytics through standardized tool interfaces — without requiring custom integration code for each data source. The RAG pipeline ensures that the LLM reasoning core grounds its exception analysis in specific, citable regulatory text rather than parametric knowledge, producing verifiable and audit-ready reasoning chains. A production deployment on a payment processing platform handling 2.3 million monthly transactions demonstrates a 99.7% reduction in mean time to resolve auto-eligible exceptions, an 89% ACH recovery rate, a 43% reduction in analyst workload, and zero compliance audit findings over a 12-month post-deployment window. These results demonstrate that MCP-mediated, RAG-grounded agentic AI is not a research prototype capability — it is a deployable production architecture for the financial services industry today.