Multi-Agent Orchestration for Agentic Commerce: Coordinating Shopping, Payment, Rewards, and Fraud Agents in Real-Time Financial Transactions

Agentic commerce is shifting the process of digital commerce buying away from a set of stand-alone recommenders and towards orchestrated autonomous services that can identify items, approve payment, earn rewards and manage transaction risk all in one real-time interaction. This study suggests a multi-agent orchestration system where shopping, payment, rewards and fraud agents are linked together via a central coordinator and their functional boundaries are kept clear. The study created a synthetic data set of 127 transaction cases with varying customer use cases, merchant contexts, paymentterms, reward opportunities and fraud review signals for the evaluation. The dataset has been designed for experimentation and re-experimentation and does not include any personal information from other sources. Data preparation of transaction records, coordination of scenario execution and organization of experimental outputs and result graphs were done using Python, pandas were used for structured data handling and Matplotlib for the result graphs. The decision flow continuity, reliability of authorization, alignment of incentives, fraud control, consistency of response and readiness of audit are the key assessment points. Results show that by having a coordinated agent that takes care of managing the order of messages, the shared context, the policy checks and exception handling, it is more effective than letting independent services operate without being aware of each other across transactions. The architecture is suitable for financial commerce platforms that need quick decisions and secure automated responses, improved accountability, and customer experience throughout the entire purchasing process.