A Novel Hierarchical Multi-Agent System for Payments Using LLMs

2026-03-02 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, FinTech & Digital Financial Services · Depth: Advanced, quick

Summary

A novel Hierarchical Multi-Agent System for Payments (HMASP) has been proposed to enable end-to-end agentic payment workflows using large language models (LLMs). Existing LLM agents like OpenAI's Operator and Claude's Computer Use are unable to handle payment tasks, and current agentic solutions lack full end-to-end payment capabilities. HMASP addresses this by employing a modular architecture with four hierarchical agent levels: a Conversational Payment Agent (CPA) as the entry point, Supervisor agents, Routing agents, and a Process summary agent. The system utilizes either open-weight or proprietary LLMs and incorporates architectural patterns such as shared state variables, decoupled message states, and structured handoff protocols to facilitate coordination. Experimental results confirm HMASP's feasibility, positioning it as the first LLM-based multi-agent system to implement complete agentic payment workflows.

Key takeaway

For AI Scientists and Research Scientists developing agentic systems, HMASP demonstrates a viable architecture for extending LLM capabilities into complex, sensitive domains like payments. You should consider adopting a hierarchical, modular multi-agent design with explicit state management and handoff protocols to tackle intricate, multi-step workflows. This approach can overcome current limitations in end-to-end automation for critical business processes.

Key insights

HMASP is the first LLM-based multi-agent system to implement end-to-end agentic payment workflows.

Principles

• Modular architecture enhances task execution.
• Hierarchical agents enable complex workflow coordination.
• Shared state variables facilitate inter-agent communication.

Method

HMASP uses a four-level hierarchical agent system: Conversational Payment Agent (CPA) for external requests, Supervisor agents, Routing agents, and a Process summary agent, coordinating via shared states and structured handoffs.

In practice

• Automate payment tasks with LLM agents.
• Design hierarchical agent systems for complex workflows.
• Implement shared state for multi-agent coordination.

Topics

• Multi-Agent Systems
• Large Language Models
• Payment Workflows
• Hierarchical Architectures
• Agentic AI

Best for: AI Scientist, Research Scientist, AI Researcher, AI Engineer, Machine Learning Engineer

Related on AIssential

• Agentic AI for Robot Teams — Applying LLM-based AI agents enables autonomy, coordination, and adaptability in heterogeneous robot teams.
• MADP: A Multi-Agent Pipeline for Sustainable Document Processing with Human-in-the-Loop — A multi-agent AI pipeline with human oversight significantly boosts document processing accuracy and sustainability.
• Agentic AI in Action — Part 12 — Building a Deal Desk Intelligence Agent with LangChain and OpenAI — Combine LLMs for language interpretation with deterministic code for policy enforcement to build reliable enterprise AI agents.
• Scaling up multi-agent systems: an interview with Minghong Geng — Hierarchical and hybrid designs are key to scaling multi-agent systems for complex, long-horizon problems.
• How We Built a Finance AI Agent That Processes 2,000 Invoices a Day (Architecture Inside) — A multi-agent LLM architecture can automate high-volume financial document processing with high accuracy and speed.
• Don't need Humans! New Protocol for #aiagents to purchase and pay with #stripe - #mpp — The Machines Payments Protocol enables programmatic, agent-driven e-commerce and microtransactions through an open standard.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.