PaymentOps: The Flow Restored

Every day, financial institutions process payment messages that may contain incomplete or inconsistent information. Even when a payment is structurally valid, missing address details or other data-quality issues can trigger manual investigations, delays, and operational complexity. **PaymentOps: The Flow Restored** demonstrates how AI-powered business agents can transform this process into an explainable, evidence-driven investigation. Built around ISO 20022 payment workflows, PaymentOps uses an Evolus-inspired agent workspace to bring together payment messages, supporting evidence, validation rules, and proposed corrections. The AI investigator examines available information and identifies possible improvements, while the rules and validation components assess whether those proposals are supported and technically acceptable. Our demonstration focuses on a realistic payment exception involving incomplete debtor and creditor address information. Instead of blindly changing the original payment, PaymentOps creates a separate sandbox copy where supported corrections can be tested. The original message remains immutable, and any information lacking sufficient evidence is explicitly left unresolved. The solution demonstrates how AI assistance, business rules, and human oversight can work together. It emphasizes traceability, transparent decisions, and controlled validation rather than automatic modifications without accountability. For the AMD hackathon, we integrate the Evolus business-agent approach with AMD-hosted AI inference to demonstrate a practical agentic workflow for financial operations. Our goal is to help payment operations teams investigate exceptions more efficiently while maintaining the accuracy, explainability, and control expected in financial services. The demonstration uses synthetic payment data and illustrates an investigation workflow rather than claiming live banking deployment or automatic production payment repair.