This blog post discusses the use of Large Language Model (LLM)-powered multi-agent systems to automate Accounts Payable (AP) processes. The authors highlight the benefits of using such systems, including separation of concerns, modularity, diversity of perspectives, and reusability. They explain how these systems work by combining AI with specialized task allocation to deliver scalable, adaptive, and human-like solutions. The post also covers the architecture of multi-agent systems, which consists of critical components such as agents, connections, orchestration, human interaction, tools and resources, and LLMs. The authors choose CrewAI as a framework for implementing AP automation due to its ease of use and setup. They demonstrate how to build an AP system using CrewAI by creating three agents for the invoice processing workflow: image_text_extractor, invoice_data_analyst, and payment_processor. The agents are defined along with their respective tasks, which are then initialized in a crew.ai project. The post concludes that LLM-powered multi-agent systems provide a scalable and intelligent solution for automating tasks like AP, combining specialized roles and advanced comprehension to streamline workflows.