The manufacturing industry faces a shortage of skilled workers and an increasing demand for customized products, making traditional automation unsuitable for the emerging high-mix, low-volume production. There is a growing need for intelligent automation that enables in-house knowledge to perform frequent reconfigurations. A promising solution involves distributed intelligence, where products autonomously manage their goals, and modular resources possess the necessary skills to achieve them. Process planners aim to digitally configure manufacturing processes and sequencing without relying on detailed, device-specific programming. This article validates a configurable multi-agent-based control system that employs Artificial Intelligence-driven transport planning using Satisfiability Modulo Theories (SMT), ensuring cost-effective adaptation to new parts, materials, and resources. A Plug & Produce robot station, equipped with tools, tool holders, and related components for a kitting application, is utilized for validation. This study compares automatically planned tool changes with the complexity of traditional Programmable Logic Controller (PLC) and robot programming in on-demand manufacturing.