Developing effective AI planning systems is crucial for real-world applications. However, current practices can be complex and prone to errors due to the use of multiple tools. This research explores PlanX Toolbox, a framework designed to simplify AI planning system development. PlanX Toolbox offers a set of services that help researchers and developers quickly create, integrate, and use advanced planning systems. It addresses challenges related to tool compatibility and complexity, allowing users to focus on core planning functions without getting tangled in technical details. The study investigates barriers to adopting best practices in AI planning system engineering and how PlanX Toolbox overcomes them. Through a comprehensive approach including literature review, framework analysis, case studies, and user feedback, the research assesses PlanX Toolbox’s adherence to best practices, usability, and potential for improvement. Findings show that PlanX Toolbox effectively incorporates best practices from software and AI engineering, presenting a strong framework. It provides a user-friendly and adaptable platform for creating reliable AI planning systems. Users find it flexible and useful in various domains. The research also identifies ways to further improve PlanX Toolbox by incorporating emerging best practices. This research simplifies the process of building strong AI planning systems with the comprehensive PlanX Toolbox. The insights gained guide future AI planning technology development and promote the use of best practices. It contributes to better understanding of engineering AI planning systems, leading to more robust and efficient planning solutions.