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The increasing demand for flexibility in automated production systems also affects the automated material flow systems (aMFSs) within these systems and, thus, demands reconfigurable systems. However, the centralized control concept usually applied in aMFSs hinders easy adaptation, as the entire control software has to be retested when subparts of the control are manually changed. As adaption and subsequent testing are time-consuming tasks, concepts are required for splitting the control from one centralized node to multiple, decentralized control nodes. Therefore, this article presents a holistic, agent-based control concept for aMFSs, whereby the system is divided into so-called automated material flow modules (aMFMs), each controlled by a dedicated module agent. The concept allows reconfiguring an aMFS consisting of heterogeneous, stationary aMFMs, during runtime. Furthermore, it includes aspects such as uniform agent knowledge bases through metamodel-based development, a communication ontology considering different information types and properties, strategic route optimization in decentralized control architectures, and a visualization concept to make decisions of the module agents comprehensible to operators and maintenance staff. We performed the concept evaluation using material flow simulations and a prototypical implementation on a lab-sized demonstrator. Note to Practitioners—Currently, the adaption of automated material flow systems (aMFSs) concerning their layout requires modifications to the control software, including extensive testing. This conflicts the demand for flexible and reconfigurable aMFSs due to changing requirements throughout a system’s life cycle. A promising approach is the modularization of aMFSs, including their control to ease layout changes and adaptations to changing material flows (known as Plug and Produce in the scope of Industrie 4.0). However, common concerns when implementing agent-based control are the real-time capability of the controlled systems and the trust of operators in the automation regarding the safety and security of these systems. More precisely, it is feared that operators might be unable to distinguish the benevolent and malevolent behaviors of an aMFS if agents make decisions autonomously. The concept we present here addresses these issues by establishing different communication types classified according to varying real-time requirements, and by supporting operators via a human–machine interface to make agent decisions comprehensible. [ABSTRACT FROM AUTHOR]