Decomposing the Problem: Multi-Agent System
Explore how to design a multi-agent system for automating shipment rerouting in global logistics. Understand how to monitor disruption signals from multiple sources, identify affected shipments, generate approved rerouting options, calculate cost trade-offs, and deliver clear recommendations for human approval. This lesson helps develop skills to handle complex workflows with audit compliance and parallel event processing in production AI systems.
This lesson works through a case study from a global freight company. The company manages shipment disruptions through a manual coordination process that spans three separate data sources and ends with a write action to a production system. The request is to automate that process. The real design question is which steps can safely run without human involvement and which require a coordinator to remain in the loop. Answering that question requires working through the case study carefully before any system is designed.
The case study
The complete case study is given below. Read it once before we begin decomposing it.
The case study
The organization: A global freight and logistics company with operations across North America, Europe, and Southeast Asia. The company manages around 45,000 active shipments at any given time, routed through a network of 200-plus carriers and 30 regional hubs.
The problem: When a weather event, port closure, or carrier capacity failure occurs, a disruption coordinator manually checks signals from three separate sources, identifies which shipments are affected, evaluates available alternative routes and carrier slots, and executes rerouting decisions by updating records in
TM. The process is labor-intensive and requires pulling and reconciling data across systems that were not designed to work together. During peak disruption periods, such as hurricane season or major port strikes, multiple simultaneous disruptions arrive faster than the team can process them one at a time. SAP Systems, Applications, and Products in Data Processing The systems: Shipment records live in SAP TM, where SAP stands for Systems, Applications, and Products in Data Processing and TM stands for Transportation Management. It serves as the company’s system of record for all active loads and exposes a REST API used by other internal tools for read and write operations. Disruption signals come from three sources the operations team monitors today.
The first is Tomorrow.io FleetIQ, a weather intelligence platform that provides route-level weather risk scores and severe-event alerts via REST API.
The second is the Portcast Port Congestion API, a port congestion intelligence service that provides real-time berth wait times, vessel queue data, and terminal-level congestion scores for the ports the company operates through.
The third is project44, a multi-modal supply ...