The convergence of Artificial Intelligence (AI) planning with Internet of Things (IoT) devices presents a valuable strategy for automating and optimizing operations in dynamic environments. Nonetheless, the challenge of achieving seamless integration of these technologies for effective plan execution and real-time monitoring persists, primarily due to factors such as the variability and unpredictability in IoT devices, network latency, and the necessity for real-time data processing and decision-making. This research investigates the creation of a microservice-based system aimed at addressing these issues, with an emphasis on developing adaptable and interoperable services for plan execution and monitoring. We have developed two microservices that can operate independently and are aligned with the PlanX framework: one dedicated to plan execution and the other to monitoring. Both microservices were constructed using NodeJS and employ Javascript Object Notation (JSON) for data transfer, thereby facilitating integration with various systems. The architecture underwent testing for scalability, performance, resource utilization, and fault tolerance, showcasing strong performance under standard conditions while also highlighting limitations in managing multiple concurrent plans and high-load situations. The findings suggest that this microservice-based methodology effectively enhances automation and optimization within IoT environments, representing a significant advancement by merging AI planning with real-time IoT monitoring and execution in a standalone framework. This research streamlines the development of integrated AI planning systems, providing guidance for future progress in the field and promoting the adoption of best practices. By deepening the understanding of engineering AI planning systems, this work lays the groundwork for more resilient, efficient, and adaptable solutions across a variety of applications.