How a Spring Boot AI Agent Was Rebuilt Using Supervisor and Specialist Architecture

A senior software engineer at BS23 in Dhaka documented how a single-agent e-commerce AI system began failing as its system prompt, tool count, and shared memory grew too complex to manage reliably. The agent, built with Spring Boot and Spring AI, struggled to select the correct tools and often mixed up context across unrelated domains like returns, store credit, and product search. The engineer resolved this by replacing the single agent with a supervisor pattern, where one top-level agent routes queries to dedicated specialist agents, each with its own tools, system prompt, and memory namespace. This approach reduces the number of decisions each model must make per call and prevents unrelated conversational context from polluting domain-specific memory. The solution is the fifth installment in an ongoing series using the same e-commerce project to demonstrate production AI agent development.